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Inicio Revista Española de Medicina Nuclear e Imagen Molecular Deep Inspiration Breath Hold [18F]FDG PET-CT on 4-rings scanners in evaluating l...
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Original article
Deep Inspiration Breath Hold [18F]FDG PET-CT on 4-rings scanners in evaluating lung lesions: Evidences from a phantom and a clinical study
[18F]FDG PET/TC en ausencia de respiración utilizando escáneres de 4 anillos en la valoración de lesiones pulmonares: evidencias de un estudio clínico y con maniquí
Federico Caobellia,
Corresponding author
fedefournier@libero.it

Corresponding author at: Department of Nuclear Medicine, Fondazione Poliambulanza, Via Bissolati 57, 25100 Brescia, Italy. Tel.: +39 0303518104; fax: +39 0303518104.
, Erinda Putaa, Stefano Ren Kaiserb, Valentina Massettia, Michela Andreolia, Angelica Mostardaa, Alberto Soffientinia, Claudio Pizzocaroa, Ugo Paolo Guerraa
a Nuclear Medicine Department, Fondazione Poliambulanza Brescia, Italy
b Medical Physics Department, Fondazione Poliambulanza Brescia, Italy
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        "descripcion" => array:1 [
          "en" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">PET&#47;CT images of the thorax acquired in FB &#40;A&#44; transaxial&#59; B&#44; sagittal&#44; C&#44; coronal&#41; and PET&#47;CT images of the thorax in the same patients&#44; acquired in DIBH &#40;D&#44; transaxial&#59; E&#44; sagittal&#59; F&#44; coronal&#41;&#46; The misalignment between PET and CT is dramatically reduced in DIBH images&#46; In FB images&#44; SUV<span class="elsevierStyleInf">max</span> was 5&#46;17&#44; SUV<span class="elsevierStyleInf">mean</span> was 3&#46;31&#44; volume was 3&#46;04<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>&#46; In DIBH&#44; SUV<span class="elsevierStyleInf">max</span> was 18&#46;82 &#40;364&#37; compared to FB&#41;&#44; SUV<span class="elsevierStyleInf">mean</span> was 6&#46;84 &#40;206&#37; compared to FB&#41;&#44; lesion volume was 4&#46;43<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span> &#40;146&#37; compared to FB&#41;&#46;</p>"
        ]
      ]
    ]
    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">&#91;<span class="elsevierStyleSup">18</span>F&#93;FDG PET&#47;CT is an important nuclear medicine imaging tool for diagnosis&#44; staging&#44; restaging&#44; therapy monitoring and radiotherapy planning of malignant lesions&#46; Hybrid PET&#47;CT scanners provide both anatomical and functional information with high sensitivity&#44; specificity and accuracy in the detection of malignant lesions&#44; in particular those located in lungs&#46;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">However&#44; the CT scan is acquired during a single-state of the breathing cycle&#44; whereas a PET scan acquisition occurs over many breathing cycles for the same length of CT scan&#46; This discrepancy in acquisition time may introduce misalignment between PET and CT images&#46; When a misalignment is present&#44; the quantification of Standardized Uptake Value &#40;SUV&#41;&#44; an important parameter in lesion evaluation&#44; may not be feasible due to the incorrect attenuation correction map obtained from the CT scan&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">Furthermore&#44; respiratory motion blurs the PET images with an overestimation of tumour volume&#44; degrades the contrast&#44; and interferes with SUV evaluation resulting in reduced PET&#47;CT accuracy&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">In order to have a solution to misalignment and blurring&#44; three different techniques have been proposed&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">Nehmeh et al&#46; proposed the use of the four-dimensional 4D-PET&#47;CT&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> This is a feasible technique but has some limits such as long acquisition and post processing time&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">Another possibility&#44; proposed by other authors&#44;<a class="elsevierStyleCrossRefs" href="#bib0020"><span class="elsevierStyleSup">4&#8211;8</span></a> is the use of Deep Inspiration Breath Hold &#40;DIBH&#41; PET&#47;CT technique&#46; Their studies have demonstrated better alignment between PET and CT and higher SUV<span class="elsevierStyleInf">max</span> using DIBH versus free breathing &#40;FB&#41;&#59; anyway&#44; the short acquisition time of a PET scan in DIBH affected the image quality and provided poorly reproducible results&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">A third possibility&#44; as proposed in a recent paper by Mitsumoto et al&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> is to perform a summation of a few acquisitions in DIBH&#44; thus reducing the effect of noise&#44; which could affect image quality as well&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">Since previous studies were characterized by a lower sensitivity due to the use of 3-rings scanner types&#44; it seemed even more effective to perform a study to investigate feasibility and advantages of DIBH technique for clinical purposes using a 4-rings PET&#47;CT scanner&#44; which allows an increased sensitivity &#40;9&#46;5<span class="elsevierStyleHsp" style=""></span>cps&#47;kBq&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a></p><p id="par0045" class="elsevierStylePara elsevierViewall">We therefore performed a phantom study to determine the minimum breath hold time versus the minimum detectable lesion size in different lesion-to-background ratio and different acquisition times&#46; Subsequently&#44; a clinical study was performed in order to evaluate the improvement of image contrast for clinical purposes&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Material and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">PET&#47;CT scanner</span><p id="par0050" class="elsevierStylePara elsevierViewall">Image data were acquired with a Biograph mCT TOF PET&#47;CT scanner &#40;Siemens Medical Solution AG&#41;&#46; PET component consists of 4 rings with a total number of 32&#46;448 detector elements of lutetium oxyorthosilicate &#40;LSO&#41;&#46; This 3D scanner has a transaxial FOV of 700<span class="elsevierStyleHsp" style=""></span>mm and an axial FOV of 216<span class="elsevierStyleHsp" style=""></span>mm&#46; Transverse and axial resolution are 4&#46;4<span class="elsevierStyleHsp" style=""></span>mm and 4&#46;5<span class="elsevierStyleHsp" style=""></span>mm&#44; respectively&#44; both in <span class="elsevierStyleItalic">full width at half maximum</span> &#40;FWHM&#41; at 1<span class="elsevierStyleHsp" style=""></span>cm from the centre according to the NEMA standards&#46; Using a reconstruction protocol that uses a 3-D iterative OSEM reconstruction technique accounting for the Point Spread Function of the PET system and using TOF information &#40;commercial name&#58; ULTRA HD<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a>&#41;&#44; the transverse and axial resolution &#40;FWHM&#41; at 20<span class="elsevierStyleHsp" style=""></span>cm from the centre is 2&#46;1<span class="elsevierStyleHsp" style=""></span>mm&#46; The CT component consists of 40 slices with a slice increment of 0&#46;1&#8211;10<span class="elsevierStyleHsp" style=""></span>mm&#44; FOV<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>780<span class="elsevierStyleHsp" style=""></span>mm&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Phantom study</span><p id="par0055" class="elsevierStylePara elsevierViewall">The PET Emission phantom &#40;model type L981606&#44; PTW&#44; Freiburg&#44; Germany&#41; was used in pre-clinical study to investigate the detectability of small size objects with PET&#47;CT scanner in our institution&#46; This phantom complies the NEMA standard NU2 &#8211; 2007&#59; therefore its shape simulates the human body in the range of thorax and six hollow solid spheres&#44; with nominal volume of 24&#46;65&#44; 12&#46;08&#44; 5&#46;75&#44; 2&#46;51&#44; 1&#46;10 and 0&#46;53<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>&#44; are embedded in the phantom&#46;</p><p id="par0060" class="elsevierStylePara elsevierViewall">The torso cavity of the phantom and the six hollow spheres were filled with different amounts of a &#91;<span class="elsevierStyleSup">18</span>F&#93;FDG solution in order to get two different radioactivity densities for spheres and for torso cavity&#44; which simulates the background&#46; Four values of spheres-to-background &#40;S&#47;B&#41; ratio of radioactivity density were investigated&#58; 3&#58;1&#44; 7&#46;4&#58;1 12&#46;1&#58;1 and 17&#46;2&#58;1&#46; Precisely&#44; concentration values are respectively 3&#46;61&#44; 9&#46;33&#44; 13&#46;70 and 18&#46;52<span class="elsevierStyleHsp" style=""></span>kBq&#47;ml for all hollow spheres and 1&#46;20&#44; 1&#46;26&#44; 1&#46;13 and 1&#46;075<span class="elsevierStyleHsp" style=""></span>kBq&#47;ml for the phantom torso cavity&#46; These concentration values of torso cavity were chosen since they are in the range of those of a typical clinical exam&#46;</p><p id="par0065" class="elsevierStylePara elsevierViewall">For every S&#47;B activity ratio studied a PET&#47;CT scan of the phantom was performed at a single bed position by using a 2550<span class="elsevierStyleHsp" style=""></span>s acquisition in list-mode collection method&#46; This static acquisition was subdivided in six time frames of different duration&#44; consecutively in time&#58; 5&#44; 10&#44; 15&#44; 20&#44; 30 and 90<span class="elsevierStyleHsp" style=""></span>s&#46; Each time frame was collected 15 times&#44; for a total of 90 frames&#46; Tomographic data were reconstructed with a 3-D iterative Ordered-Subset-Expectation-Maximization &#40;OSEM&#41; algorithm accounting for the Point Spread Function of the PET system and using time of flight &#40;TOF&#41; information with CT based attenuation correction &#40;precisely&#58; 2 iterations&#44; 21 subsets&#44; Gaussian filter with 4<span class="elsevierStyleHsp" style=""></span>mm Full Width Half Maximum &#40;FWHM&#41;&#44; matrix size 200<span class="elsevierStyleHsp" style=""></span>pixel<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>200<span class="elsevierStyleHsp" style=""></span>pixel&#44; zoom 1&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">PET and CT phantom images were then co-registered and merged with a dedicated software &#40;Syngo MI&#46; PET&#47;CT 2009C Sevice Pack 1&#44; Siemens AG&#41; supplied by the PET&#47;CT scanner manufacturer&#46; By using this software&#44; image analysis was performed as follows&#46; Displaying the PET tomographic image of this dynamic PET and CT merged image&#44; volumes of interest &#40;VOIs&#41; were drawn over each sphere with VOIs&#8217; sizes equal to nominal spheres dimensions&#46; A further VOI&#44; with size equal to that of the biggest sphere&#44; was placed among the six spheres images in order to take background into account&#46; Average activity density inside each single VOI was computed&#46; All these seven VOIs were drawn for each acquisition that belongs to a single time frame&#59; therefore&#44; a total of 7<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>15<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>6 VOIs were drawn&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">For a given sphere and for a given time frame&#44; mean and standard deviation &#40;SD&#41; of activity densities of the 15 drawn VOIs were calculated&#46; Coefficient of variation &#40;CV&#41; was also computed with the following formula&#58;<elsevierMultimedia ident="eq0005"></elsevierMultimedia>where the mean and SD of the formula refer to the 15 acquisitions of a single time frame and for a given sphere&#46;</p><p id="par0080" class="elsevierStylePara elsevierViewall">Moreover&#44; contrast of the sphere was calculated as&#58;<elsevierMultimedia ident="eq0010"></elsevierMultimedia></p><p id="par0085" class="elsevierStylePara elsevierViewall">Also sphere contrast was computed for each acquisition of a given time frame&#59; therefore&#44; mean&#44; SD and CV of the 15 contrast data were then computed&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Clinical study</span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Patients data and preparation</span><p id="par0090" class="elsevierStylePara elsevierViewall">Twenty-five consecutive patients &#40;20 males&#59; 5 females&#41; referring to our Department in order to perform a <span class="elsevierStyleSup">18</span>F-FDG PET&#47;CT for characterization of lung lesions were selected in this study between May 2011 and January 2012&#46; Mean Body mass index &#40;BMI&#41; was 26&#46;08<span class="elsevierStyleHsp" style=""></span>&#177;<span class="elsevierStyleHsp" style=""></span>4&#46;75&#44; mean age was 63&#46;2 &#91;45&#8211;79&#93; years&#46; 15&#47;25 patients had a previously diagnosed tumour&#44; 9&#47;25 patients had lung nodules of unknown nature and one patient had fever of unknown origin &#40;FUO&#41;&#46; The diameter of the lesions ranged from 6&#46;5<span class="elsevierStyleHsp" style=""></span>mm and 49<span class="elsevierStyleHsp" style=""></span>mm&#46; All patients provided written informed consent for participation in the PET study&#46; All patients fasted for at least 6<span class="elsevierStyleHsp" style=""></span>h before PET studies&#46; At the injection time with &#91;<span class="elsevierStyleSup">18</span>F&#93;FDG&#44; serum glucose levels were &#60;130<span class="elsevierStyleHsp" style=""></span>mg&#47;dl&#46; The mean injected &#91;<span class="elsevierStyleSup">18</span>F&#93;FDG activity was 3&#46;8<span class="elsevierStyleHsp" style=""></span>MBq&#47;kg &#40;2&#46;89&#8211;4&#46;40<span class="elsevierStyleHsp" style=""></span>MBq&#47;kg&#41;&#46; Standard PET&#47;CT images were acquired after an uptake phase of 90<span class="elsevierStyleHsp" style=""></span>min approximately&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">PET&#47;CT acquisition&#44; reconstruction and analysis</span><p id="par0095" class="elsevierStylePara elsevierViewall">The clinical PET&#47;CT session was performed according to the standard protocol used in our institution &#40;whole body PET&#47;CT in free breathing from skull base to mid thigh&#44; 90<span class="elsevierStyleHsp" style=""></span>s per bed position&#44; patient in supine position&#41;&#46; Images were attenuation corrected by CT scan acquired in helical mode&#44; 120<span class="elsevierStyleHsp" style=""></span>kV&#44; 120<span class="elsevierStyleHsp" style=""></span>mAs&#44; slice 5<span class="elsevierStyleHsp" style=""></span>mm&#44; pitch 1&#46;2&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">Subsequently&#44; thorax CT and PET scan were acquired in DIBH&#59; PET acquisition lasted for 20<span class="elsevierStyleHsp" style=""></span>s&#44; within 30<span class="elsevierStyleHsp" style=""></span>min from the standard clinical PET&#47;CT&#46; If patients could not collaborate or could not hold their breath&#44; as it happened in 2 out of 25 patients&#44; no further images in DIBH modality were acquired&#46; The overall time for the session in DIBH was about 5<span class="elsevierStyleHsp" style=""></span>min&#46; CT parameters were the same of FB acquisition&#46; PET and CT data were always aligned after DIBH acquisition&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">The percentage difference between SUV<span class="elsevierStyleInf">max</span> in FB and SUV<span class="elsevierStyleInf">max</span> in DIBH was defined as follows&#58;<elsevierMultimedia ident="eq0015"></elsevierMultimedia></p><p id="par0110" class="elsevierStylePara elsevierViewall">All PET images were reconstructed using 3D iterative ordered-subset-expectation-maximization &#40;OSEM&#41; algorithm accounting for the point spread function of the PET system and using TOF information &#40;2 iterations and 21 subsets&#41; with CT based attenuation correction &#40;4<span class="elsevierStyleHsp" style=""></span>mm full width half maximum Gaussian filtering&#44; matrix 200<span class="elsevierStyleHsp" style=""></span>&#215;<span class="elsevierStyleHsp" style=""></span>200&#41;&#46;</p><p id="par0115" class="elsevierStylePara elsevierViewall">The following parameters were measured both in FB and in DIBH acquisitions by manually placing a 3D-isocontour VOI on PET images around the lesion&#58; SUV<span class="elsevierStyleInf">max</span>&#44; SUV<span class="elsevierStyleInf">mean</span>&#44; and displacement between PET and CT target lesion&#46; Lesion volume was also calculated from the same VOI around nodules on PET images&#46;</p></span></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Statistical analysis</span><p id="par0120" class="elsevierStylePara elsevierViewall">Paired <span class="elsevierStyleItalic">t</span>-test and Bland&#8211;Altman analysis were used to evaluate differences in SUV<span class="elsevierStyleInf">max</span>&#44; SUV<span class="elsevierStyleInf">mean</span> and volumes&#44; determined with the two investigated techniques&#46; Linear regression analysis between &#37;BH index and displacement was also performed&#46; All statistical tests were performed by using a dedicated statistical analysis software &#40;Statgraphics Centurion v15&#41;&#46; A <span class="elsevierStyleItalic">p</span> value &#60;0&#46;05 was considered as significant&#46;</p></span></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Results</span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Phantom study</span><p id="par0125" class="elsevierStylePara elsevierViewall">Data analysis has been performed by studying activity density and contrast of spheres&#44; which are two parameters describing an image and influencing its quality&#46;</p><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Activity density</span><p id="par0130" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRefs" href="#tbl0005">Tables 1&#8211;4</a> list activity density &#40;Bq&#47;ml&#41; data for each sphere&#44; acquisition time and S&#47;B activity ratio investigated&#46; Background average values are also reported&#46; In <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>A&#8211;D average activity density of spheres is plotted versus acquisition time and S&#47;B ratio value&#46; For the sake of clarity&#44; activity density is shown only for four sphere sizes&#44; namely the &#8220;biggest&#8221;&#44; the &#8220;medium&#8221;&#44; the &#8220;next to smallest sphere&#8221; and the &#8220;smallest&#8221; sphere corresponding to volume of 24&#46;65&#44; 5&#46;75&#44; 1&#46;10 and 0&#46;53<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span> respectively&#46; As shown in <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#44; the uncertainty &#40;1 SD&#41; of activity density decreases as acquisition time increases&#44; because of increased count statistics&#44; and it tends to decrease as S&#47;B ratio value becomes higher&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="tbl0020"></elsevierMultimedia><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0135" class="elsevierStylePara elsevierViewall">For a given S&#47;B ratio value&#44; all spheres show the same activity density within their uncertainty for all acquisition times&#46;</p><p id="par0140" class="elsevierStylePara elsevierViewall">In particular&#44; it is important to pay attention to the &#8220;smallest&#8221; and the &#8220;next to smallest&#8221; spheres since they result more challenging for the system detectability because of their small volume&#46;</p><p id="par0145" class="elsevierStylePara elsevierViewall">Concerning <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>D&#44; activity density of the &#8220;smallest&#8221; sphere is constant within its uncertainty for every acquisition time and for every S&#47;B ratio value investigated&#44; with the exception of the 90<span class="elsevierStyleHsp" style=""></span>s acquisition time and 3&#46;0 S&#47;B ratio value&#46;</p><p id="par0150" class="elsevierStylePara elsevierViewall">Regarding the &#8220;next to smallest&#8221; sphere&#44; similar conclusion may be achieved&#44; since activity density is constant within its uncertainty for the 12&#46;1 and 7&#46;4 S&#47;B values in the 5&#8211;30<span class="elsevierStyleHsp" style=""></span>s range&#46;</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Contrast</span><p id="par0155" class="elsevierStylePara elsevierViewall">Sphere contrast has been studied in function of sphere size&#44; acquisition time and ratio of activity density of spheres to background&#46; All these data are listed in <a class="elsevierStyleCrossRefs" href="#tbl0005">Tables 1&#8211;4</a>&#46;</p><p id="par0160" class="elsevierStylePara elsevierViewall">For a fixed value of S&#47;B ratio and acquisition time&#44; sphere contrast increases with sphere volume&#44; as it is expected &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>A&#8211;C&#41;&#46; This result holds for every value of S&#47;B ratio and acquisition time investigated&#46; Conversely&#44; CV of the sphere contrast increases as sphere size decreases since counts become poorer as sphere volume becomes smaller&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0165" class="elsevierStylePara elsevierViewall">For a given S&#47;B ratio value and a given sphere volume&#44; sphere contrast keeps constant within its statistical uncertainty as acquisition time increases&#44; as it is shown in <a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a>A&#8211;D&#46; On the contrary&#44; CV of contrast decreases with acquisition time because of higher count statistic&#46; These results hold for every S&#47;B ratio value and for each sphere volume studied&#46; Contrast values are shown for all the S&#47;B ratio values but only for the four spheres called the &#8220;biggest&#8221;&#44; the &#8220;medium&#8221;&#44; the &#8220;next to smallest&#8221; and the &#8220;smallest&#8221; sphere for the sake of clarity&#46; <a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a>A&#8211;D shows sphere contrast in function of acquisition time and of S&#47;B ratio value for the four above mentioned spheres&#46;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia><p id="par0170" class="elsevierStylePara elsevierViewall">For a given acquisition time and sphere volume&#44; sphere contrast increases with S&#47;B ratio value&#44; while CV of contrast tends to decrease as S&#47;B ratio value increases&#44; as it may be expected&#46; These results hold for each sphere volume and acquisition time&#46;</p><p id="par0175" class="elsevierStylePara elsevierViewall">In <a class="elsevierStyleCrossRef" href="#fig0025">Fig&#46; 5</a>A&#8211;C&#44; contrast is plotted in function of S&#47;B ratio value for acquisition time of 10&#44; 20 and 30<span class="elsevierStyleHsp" style=""></span>s&#44; respectively&#59; these values are illustrated only for the same four spheres for simplicity&#46; Concerning the two smallest spheres&#44; with 0&#46;53 and 1&#46;10<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span> volume&#44; which are supposed to be more critical to detect&#44; it is interesting to focus our attention to the 7&#46;4 and 12&#46;1 S&#47;B ratio values&#46; In fact the other two S&#47;B ratio values represent the limit situations&#46; In the lowest one &#40;S&#47;B<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>3&#46;0&#41; contrast is independent from acquisition time within its statistical uncertainty&#44; as it is stated above&#44; and the count statistic is so poor that no one of these two sphere is visible on the image for every acquisition time&#46; For the highest one &#40;S&#47;B<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>17&#46;2&#41;&#44; conversely&#44; the count statistic is enough high that contrast of the two spheres is significantly different&#46;</p><elsevierMultimedia ident="fig0025"></elsevierMultimedia><p id="par0180" class="elsevierStylePara elsevierViewall">Concerning the &#8220;smallest&#8221; sphere&#44; there are no significant differences &#40;5&#37; confidence level&#41; in contrast between 7&#46;4 and 12&#46;1 S&#47;B ratio values in the range of 5&#8211;30<span class="elsevierStyleHsp" style=""></span>s acquisition time&#46;</p><p id="par0185" class="elsevierStylePara elsevierViewall">Regarding the &#8220;next to smallest&#8221; sphere&#44; contrast holds the same value in the 5&#8211;30<span class="elsevierStyleHsp" style=""></span>s acquisition time range for a given S&#47;B ratio&#46; Anyway&#44; for the 5&#8211;15<span class="elsevierStyleHsp" style=""></span>s range&#44; the contrast CV value is so high that contrast does not significantly differ between the 7&#46;4 and 12&#46;1 S&#47;B ratio&#46; On the contrary&#44; for the 20<span class="elsevierStyleHsp" style=""></span>s and the 30<span class="elsevierStyleHsp" style=""></span>s acquisition&#44; CV values are smaller&#44; so there is a significant difference of contrast values at the level of 5&#37; of confidence between the 7&#46;4 and 12&#46;1 S&#47;B ratio&#46;</p><p id="par0190" class="elsevierStylePara elsevierViewall">For the other spheres&#44; namely those bigger than 0&#46;10<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>&#44; their contrast and contrast CV are such that these spheres are visible also with a 5<span class="elsevierStyleHsp" style=""></span>s acquisition time for a S&#47;B ratio down to 7&#46;4&#46;</p></span></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Clinical study</span><p id="par0195" class="elsevierStylePara elsevierViewall">Twenty-three out of the 25 patients enrolled in the study successfully carried out PET&#47;CT in FB and DIBH&#46; Overall&#44; 27 lesions in 23 patients were studied&#46; Only 2&#47;25 patients could not complete the DIBH acquisition &#40;a 78 years old female treated for rectal carcinoma and a 40 years old male who had undergone radiation and chemotherapy for epipharynx carcinoma&#41;&#46;</p><p id="par0200" class="elsevierStylePara elsevierViewall">Results are shown in <a class="elsevierStyleCrossRef" href="#tbl0025">Table 5</a>&#46; There was a significant statistical difference in SUV<span class="elsevierStyleInf">max</span> values between DIBH and FB &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;0009&#41;&#46; On the basis of the displacement between PET and CT lesions during FB acquisition&#44; patients were divided into two groups&#58; group A &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>19 lesions&#41; with a displacement ranging from 3&#46;4<span class="elsevierStyleHsp" style=""></span>mm to 25<span class="elsevierStyleHsp" style=""></span>mm &#40;<a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a>&#41; and group B &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>8 lesions&#41; with lesion displacement inferior to 1<span class="elsevierStyleHsp" style=""></span>mm&#46;</p><elsevierMultimedia ident="tbl0025"></elsevierMultimedia><elsevierMultimedia ident="fig0030"></elsevierMultimedia><p id="par0205" class="elsevierStylePara elsevierViewall">Significant difference was observed between the SUV<span class="elsevierStyleInf">max</span> in FB and SUV<span class="elsevierStyleInf">max</span> in DIBH acquisition in the group A &#40;<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>19 lesions&#41;&#44; while no statistically significant difference was found in group B&#46;</p><p id="par0210" class="elsevierStylePara elsevierViewall">In group A significant differences were observed between FB and DIBH acquisition also regarding SUV<span class="elsevierStyleInf">mean</span> values &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0002&#41; and lesion volumes &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41;&#59; a significant relationship was also observed between SUV<span class="elsevierStyleInf">max</span> in FB and SUV<span class="elsevierStyleInf">max</span> in DIBH acquisition &#40;<span class="elsevierStyleItalic">R</span><span class="elsevierStyleSup">2</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;6985&#46; <span class="elsevierStyleItalic">p<span class="elsevierStyleHsp" style=""></span>&#61;</span><span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41;&#46; A significant correlation was observed between &#37;BH-index and displacement &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;01&#44; <a class="elsevierStyleCrossRef" href="#fig0035">Fig&#46; 7</a>&#41;&#44; even in presence of a positive large range &#40;13&#8211;279&#46;25&#37;&#41; of &#37;BH-index&#46; Finally&#44; there was a significant difference regarding &#37;BH index also between upper and lower lung lesions of &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;013&#41;&#44; as well as for displacement &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;034&#41; &#40;<a class="elsevierStyleCrossRef" href="#tbl0030">Table 6</a>&#41;&#46;</p><elsevierMultimedia ident="fig0035"></elsevierMultimedia><elsevierMultimedia ident="tbl0030"></elsevierMultimedia><p id="par0215" class="elsevierStylePara elsevierViewall">In group B&#44; no significant statistical difference could be observed among the measurements of SUV<span class="elsevierStyleInf">max</span>&#44; SUV<span class="elsevierStyleInf">mean</span> and lesions volume obtained in FB and DIBH acquisition&#46;</p></span></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Discussion</span><p id="par0220" class="elsevierStylePara elsevierViewall">Deep inspiration breath hold PET&#47;CT technique is under investigation and different studies report advantages of this technique in the study of lung lesions&#46;</p><p id="par0225" class="elsevierStylePara elsevierViewall">Respiratory motion causes the diffusion of the activity within a given lesion over a larger area and that causes an underestimation of the true activity density with consequent lower SUV<span class="elsevierStyleInf">max</span> value&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> So&#44; in order to obtain an accurate evaluation of lung lesions&#44; and since movement dramatically affects also attenuation correction&#44; a good alignment should be desirable&#46; Moreover&#44; since tumour location is an important factor for determining the therapeutic method&#44; such as combined radio-chemotherapy&#44; a correct anatomical assignment is critical&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a></p><p id="par0230" class="elsevierStylePara elsevierViewall">Using DIBH technique&#44; Nehmeh et al&#46; demonstrated an increase of 32&#46;5&#37; in tumour SUV<span class="elsevierStyleInf">max</span> &#40;range&#44; 4&#8211;83&#37;&#41; and an improvement in spatial matching between PET and CT images&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a></p><p id="par0235" class="elsevierStylePara elsevierViewall">Many papers tried to determine the optimal time acquisition in DIBH acquisition&#58; Torizuka et al&#46; found in a phantom study that at least a 20-s acquisition was required<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a>&#59; anyway&#44; in their paper statistical error was higher than in our study probably thanks to the increased sensitivity of the 4-rings scanner&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a></p><p id="par0240" class="elsevierStylePara elsevierViewall">Meirelles et al&#46;<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> and Nehmeh et al&#46;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> obtained similar results in their studies&#44; though they did not evaluate lesions on a phantom model&#46;</p><p id="par0245" class="elsevierStylePara elsevierViewall">Mitsumoto et al&#46; found that a single 20-s acquisition might not be of good enough quality to evaluate the precise SUV<span class="elsevierStyleInf">max</span> value because of the effect of increased noise on the SUV<span class="elsevierStyleInf">max</span> measurement&#44; thus suggesting image summation&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a></p><p id="par0250" class="elsevierStylePara elsevierViewall">Another phantom study showed that PET images in simulated-DIBH with acquisition times of 45&#44; 60&#44; and 120<span class="elsevierStyleHsp" style=""></span>s had significantly higher diagnostic accuracy than PET images during 120<span class="elsevierStyleHsp" style=""></span>s of simulated FB&#44; suggesting that at least a 45-s breath-hold time is required&#46;<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a></p><p id="par0255" class="elsevierStylePara elsevierViewall">Concerning phantom study&#44; a general result has been achieved&#58; as expected&#44; sphere contrast increases as sphere volume and S&#47;B ratio increase&#44; while its uncertainty tends to decrease as acquisition time increases&#46;</p><p id="par0260" class="elsevierStylePara elsevierViewall">As a consequence&#44; it is worthwhile looking for whether it is possible to determine the minimum breath hold time versus the minimum detectable lesion size on the basis of our results&#46;</p><p id="par0265" class="elsevierStylePara elsevierViewall">Results of our phantom study show that for the &#8220;smallest&#8221; sphere&#44; when S&#47;B ratio ranges from 7&#46;4 to 12&#46;1&#44; the minimum acquisition time might also be 5<span class="elsevierStyleHsp" style=""></span>s because no significant differences in sphere contrast occur between these two acquisitions in the 5&#8211;30<span class="elsevierStyleHsp" style=""></span>s temporal range&#46; However&#44; it is also important to consider the contrast CV&#44; since it influences the detectability&#46; In fact&#44; the &#8220;smallest&#8221; sphere is not detectable at the 5<span class="elsevierStyleHsp" style=""></span>s acquisition time neither with a 7&#46;4 S&#47;B value nor with a 12&#46;1 S&#47;B value&#46; Detectability of this sphere occurs in the 10&#8211;15<span class="elsevierStyleHsp" style=""></span>s temporal range for both S&#47;B ratio values&#46; Regarding the 3&#46;0<span class="elsevierStyleHsp" style=""></span>S&#47;B ratio&#44; the &#8220;smallest&#8221; sphere is not detectable at all&#44; neither with a 90<span class="elsevierStyleHsp" style=""></span>s acquisition&#46; On the other hand&#44; for the highest 17&#46;2 S&#47;B ratio&#44; it is detectable even with a 5&#8211;10<span class="elsevierStyleHsp" style=""></span>s acquisition time&#46;</p><p id="par0270" class="elsevierStylePara elsevierViewall">Regarding the 1&#46;10<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span> sphere&#44; i&#46;e&#46; the &#8220;next to smallest&#8221; sphere&#44; it seems that an optimal acquisition time might be equal to 5<span class="elsevierStyleHsp" style=""></span>s&#46; In fact&#44; our results show that for a given value of S&#47;B ratio&#44; sphere contrast is constant within its uncertainty in the 5&#8211;90<span class="elsevierStyleHsp" style=""></span>s temporal range and this sphere is detectable with 5<span class="elsevierStyleHsp" style=""></span>s acquisition for every S&#47;B ratio value studied&#44; with the exception of the lowest value &#40;when S&#47;B ratio equals 3&#46;0 this sphere is not visible neither with a 90<span class="elsevierStyleHsp" style=""></span>s acquisition time&#41;&#46;</p><p id="par0275" class="elsevierStylePara elsevierViewall">However&#44; also in this case it is important to consider the magnitude of contrast CV of the sphere&#46; In fact&#44; when we consider the 7&#46;4 and 12&#46;1 S&#47;B ratio acquisitions&#44; our results show that there is a significant difference of sphere contrast at the 5&#37; level of confidence between these two acquisitions in the 20&#8211;30<span class="elsevierStyleHsp" style=""></span>s range&#46; Conversely&#44; there are no significant differences of contrast between these two acquisitions in the 5&#8211;15<span class="elsevierStyleHsp" style=""></span>s temporal range&#46; This is due to the contrast CV that is generally higher in this temporal range than that in the 20&#8211;30<span class="elsevierStyleHsp" style=""></span>s range&#46; This means that an acquisition of 5&#8211;15<span class="elsevierStyleHsp" style=""></span>s exhibits the same contrast independently from S&#47;B ratio value&#44; while a 20&#8211;30<span class="elsevierStyleHsp" style=""></span>s acquisition is &#8220;sensible&#8221; to the S&#47;B ratio value because it exhibits a contrast that significantly increases as activity dose increases&#46; It turns out that performing a 5&#8211;15<span class="elsevierStyleHsp" style=""></span>s acquisition does not improve the detectability of the sphere also when a higher S&#47;B ratio value is used&#59; conversely&#44; performing a 20&#8211;30<span class="elsevierStyleHsp" style=""></span>s acquisition means to improve the detectability of a 1&#46;10<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span> sphere shaped lesion also with a lower S&#47;B ratio value&#46;</p><p id="par0280" class="elsevierStylePara elsevierViewall">Given this consideration&#44; our results show that it is preferable to perform an acquisition of 20&#8211;30<span class="elsevierStyleHsp" style=""></span>s because this acquisition may detect a 1&#46;10<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span> sphere shaped lesion by keeping low the administered dose to the patient&#44; and may also contribute to detect a sphere shaped lesion of smaller volume &#40;i&#46;e&#46; 0&#46;53<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>&#41;&#46; On the contrary&#44; a 5&#8211;15<span class="elsevierStyleHsp" style=""></span>s acquisition does not give any significant contribution to the detectability of this lesion or to the smaller one&#46;</p><p id="par0285" class="elsevierStylePara elsevierViewall">Therefore&#44; since we are interested in the minimum breath hold time&#44; a 20<span class="elsevierStyleHsp" style=""></span>s acquisition seems to be adequate so that for specific conditions &#40;acquisition time&#44; size of the lesion&#44; S&#47;B activity ratio&#41; a simulated lesion will be qualitatively detected and the corresponding SUV will not be biased&#46;</p><p id="par0290" class="elsevierStylePara elsevierViewall">This relatively short time makes breath hold technique highly feasible in clinical practice&#44; since the majority of the patients can hold breath for 20<span class="elsevierStyleHsp" style=""></span>s&#44; but hardly for longer time&#46; A DIBH acquisition should therefore be performed whenever there is evidence of a marked misalignment between CT and PET images&#44; since the overall time to perform it is extremely small&#46;</p><p id="par0295" class="elsevierStylePara elsevierViewall">However&#44; since this is the first study investigating the role of DIBH in the evaluation of lung lesions applied on a 4-rings PET&#47;CT tomograph&#44; our results could have been obtained at least partly because of the higher sensitivity of this scanner over tomographs used in previous studies&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a></p><p id="par0300" class="elsevierStylePara elsevierViewall">In fact&#44; 4-rings PET&#47;CT scanners are demonstrated to improve sensitivity and reduce statistical uncertainty&#44;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> thus dramatically minimizing uncertainties about lesion determination&#46;</p><p id="par0305" class="elsevierStylePara elsevierViewall">Performing a DIBH acquisition improves contrast and produces an increase in SUV<span class="elsevierStyleInf">max</span> value by deleting a possible artefact caused by the spread of radiotracer activity over a larger area&#44; thus underestimating real activity density&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0310" class="elsevierStylePara elsevierViewall">Moreover&#44; limitations from other factors such as non-uniform distribution of <span class="elsevierStyleSup">18</span>F-FDG activity within the tumour and shape and size of the lesion&#44; which may reduce SUV<span class="elsevierStyleInf">max</span> and SUV<span class="elsevierStyleInf">mean</span> of FB PET&#47;CT&#44; particularly in large tumors<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> are overcome with DIBH acquisition&#46; In particular&#44; the latest two are the most probably affected by motion artefacts&#46;</p><p id="par0315" class="elsevierStylePara elsevierViewall">This aspect is consistent with what observed in our clinical study&#58; we reported a significant increasing in SUV<span class="elsevierStyleInf">max</span> values between FB and DIBH with an extremely variable range&#44; depending on lesion dimension&#44; respiratory movement and intensity of radiopharmaceutical uptake &#40;<a class="elsevierStyleCrossRef" href="#fig0040">Fig&#46; 8</a>&#41;&#46; Although we cannot rule out that SUV<span class="elsevierStyleInf">max</span> values increase also due to the time interval between FB and DIBH acquisitions&#44; it is unlikely that a so large difference could be explained only with metabolic behaviour of lung lesions&#46; Moreover&#44; the effect of DIBH is demonstrated by the fact that patients in group B did not show any increase in SUV<span class="elsevierStyleInf">max</span> values&#59; if the time interval had affected results&#44; lesions found in these patients would have presented a significant SUV<span class="elsevierStyleInf">max</span> increase&#46;</p><elsevierMultimedia ident="fig0040"></elsevierMultimedia><p id="par0320" class="elsevierStylePara elsevierViewall">Motion has a great impact on SUV<span class="elsevierStyleInf">max</span> value determination<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a>&#58; so&#44; when comparing different studies&#44; even in the same patient&#44; one should carefully consider the possible limitation caused by patient&#39;s movements&#44; which could lead&#44; as an example&#44; to inaccurate evaluation of therapy response&#46;</p><p id="par0325" class="elsevierStylePara elsevierViewall">As observed by Torizuka et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> we found a more significant difference among lesion located in different parts of the lungs&#58; in particular&#44; lesions located in the lower lobes are more likely to be affected by movements artefacts &#40;<a class="elsevierStyleCrossRef" href="#tbl0030">Table 6</a>&#41;&#46;</p><p id="par0330" class="elsevierStylePara elsevierViewall">The authors reported a significant higher percentage difference in SUV<span class="elsevierStyleInf">max</span> in lesions located in lower lobes than in the upper lung fields &#40;52&#46;9&#37; vs 24&#46;4&#37;&#44; respectively&#41;&#46;</p><p id="par0335" class="elsevierStylePara elsevierViewall">This is probably related to diaphragmatic motion leading to misalignments between PET and CT images and marked differences in SUV<span class="elsevierStyleInf">max</span> value during different respiratory phases&#59; it is therefore clear that lesions near the lung base should be carefully checked&#46;</p><p id="par0340" class="elsevierStylePara elsevierViewall">Other studies introduced a respiratory gating system in order to overcome the limitations due to movement artefacts<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8&#44;15</span></a>&#58; anyway&#44; unlike this method&#44; DIBH is simpler&#44; highly feasible&#44; with absolutely no costs&#44; it requires only a minor increase in overall time of acquisition and the patients do not need a particular training&#46;</p><p id="par0345" class="elsevierStylePara elsevierViewall">Moreover&#44; the effective radiation dose to patients from the additional breath-hold CT is only about 1<span class="elsevierStyleHsp" style=""></span>mSv&#44; which is however needed also when an acquisition with respiratory gating is performed&#44; thus not representing a significant increasing risk for radio-induced pathologies&#46;</p><p id="par0350" class="elsevierStylePara elsevierViewall">Our study has some limitations&#58; first&#44; the patients sample is relatively small&#46; However&#44; since the differences in considered parameters were studied within the same patient&#44; a significant statistical analysis could be obtained&#46;</p><p id="par0355" class="elsevierStylePara elsevierViewall">Another important limitation is the lack of a comparison between PET&#47;CT data and clinical outlook as it would have been needed&#59; so&#44; at least theoretically some false positive could have been present&#46;</p><p id="par0360" class="elsevierStylePara elsevierViewall">Finally&#44; we did not compare our data with images obtained with a gating respiratory device to monitor any respiratory motion and breath hold phase anomalies during DIBH&#46; We cannot rule out that some data variability can be partly due to breath hold that is not always at the same phase of the respiratory cycle&#44; time of real breath hold and time between the two PET&#47;CT studies&#46;</p></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0135">Conclusion</span><p id="par0365" class="elsevierStylePara elsevierViewall">Our phantom study indicates that a 20<span class="elsevierStyleHsp" style=""></span>s acquisition time should be enough to provide an accurate evaluation of lung lesions&#44; except in presence of a low S&#47;B ratio and small lesions as inferior or equal to 1&#46;10<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>&#46; Conversely&#44; a shorter acquisition time has been shown to exhibit a contrast of these small sphere shaped details that is not enhanced as administered activity is increased&#46; A longer acquisition time&#44; indeed&#44; surely improves the detectability of these small lesions&#46; Anyway&#44; since we were interested in looking for the minimum acquisition time of our tomographic system&#44; an acquisition of 20<span class="elsevierStyleHsp" style=""></span>s has been shown to be sufficient to detect such spheres&#46;</p><p id="par0370" class="elsevierStylePara elsevierViewall">A 20-s DIBH PET&#47;CT scan provides a better alignment between PET and CT lung lesion images when compared to a FB scan&#44; resulting in a more accurate evaluation of lesions location&#44; SUV<span class="elsevierStyleInf">max</span> and SUV <span class="elsevierStyleInf">mean</span>&#44; especially for small lesions in the lower lung lobes&#46;</p><p id="par0375" class="elsevierStylePara elsevierViewall">Given the great advantages and the minor limitation of this technique&#44; if a 4-rings PET&#47;CT scanner is available&#44; we suggest to apply it every time there is an evidence of misalignment between PET and CT images&#46;</p></span><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0140">Conflict of interest</span><p id="par0380" class="elsevierStylePara elsevierViewall">The authors declare no conflict of interest&#46;</p></span></span>"
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        "titulo" => "Abstract"
        "resumen" => "<span class="elsevierStyleSectionTitle" id="sect0010">Objective</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">To investigate the clinical feasibility of a Deep Inspiration Breath Hold &#40;DIBH&#41; <span class="elsevierStyleSup">18</span>F-FDG PET-CT acquisition in apnea and compare the results obtained between these acts of acquisition in apnea and in Free Breathing in the evaluation of lung lesions&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0015">Material and methods</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">A pre-clinical phantom study was performed to evaluate the shortest simulated DIBH time according to the minimum detectable lesion that can be detected by our ultrasound scanner&#46; This study was conducted by changing acquisition time and sphere-to-background activity ratio values and by using radioactivity densities similar to those generally found in clinical examinations&#46; In the clinical study&#44; 25 patients with pulmonary lesions underwent a standard whole body <span class="elsevierStyleSup">18</span>F-FDG PET-CT scan in free breathing followed by a 20<span class="elsevierStyleHsp" style=""></span>s single thorax acquisition PET&#47;CT in DIBH acquisition&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0020">Results</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The phantom study indicated that a 20-s acquisition time provides an accurate evaluation of smallest sphere shaped lesions&#46; In the clinical study&#44; PET-CT scans obtained in DIBH studies showed a significant reduction of misalignment between the PET and CT scan images and an increase of SUV<span class="elsevierStyleInf">max</span> compared to free breathing acquisitions&#46; A correlation between the &#37;BH-index and lesion displacement between PET and CT images in FB acquisition was demonstrated&#44; significantly higher for lesions with a displacement<span class="elsevierStyleHsp" style=""></span>&#62;<span class="elsevierStyleHsp" style=""></span>8<span class="elsevierStyleHsp" style=""></span>mm&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0025">Conclusion</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">The single 20<span class="elsevierStyleHsp" style=""></span>s acquisition of DIBH PET-CT is a feasible technique for lung lesion detection in the clinical setting&#46; It only requires a minor increase in examination time without special patient training&#46; 20<span class="elsevierStyleHsp" style=""></span>s DIBH scan provided a more precise measurement of SUV<span class="elsevierStyleInf">max</span>&#44; especially for lesions in the lower lung lobes which usually show greater displacement between PET and CT scan images in FB acquisition&#46;</p>"
      ]
      "es" => array:2 [
        "titulo" => "Resumen"
        "resumen" => "<span class="elsevierStyleSectionTitle" id="sect0035">Objetivo</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Estudiar la viabilidad cl&#237;nica de la adquisici&#243;n <span class="elsevierStyleSup">18</span>F-FDG PET-TC en apnea y comparar los resultados obtenidos entre las adquisiciones en apnea y en respiraci&#243;n libre en la valoraci&#243;n de lesiones pulmonares&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0040">Material y m&#233;todos</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Se ha realizado un estudio precl&#237;nico en maniqu&#237; con objeto de establecer el tiempo m&#237;nimo de adquisici&#243;n en apnea en funci&#243;n de la m&#225;s peque&#241;a lesi&#243;n evidenciable con nuestro tom&#243;grafo&#46; Este estudio se obtuvo modificando los tiempos de adquisici&#243;n y la relaci&#243;n de actividad entre esfera y fondo utilizando actividades parecidas a las encontradas en la pr&#225;ctica cl&#237;nica&#46; En el estudio cl&#237;nico&#44; 25 pacientes con lesiones pulmonares fueron evaluados mediante PET-TC con <span class="elsevierStyleSup">18</span>F-FDG en respiraci&#243;n libre y posteriormente en apnea de 20 segundos&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0045">Resultados</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">El estudio en maniqu&#237; indic&#243; que una adquisici&#243;n de 20 segundos es adecuada para la valoraci&#243;n de las esferas m&#225;s peque&#241;as&#46; En el estudio cl&#237;nico&#44; las adquisiciones PET-TC obtenidas en apnea mostraron una significativa reducci&#243;n de la desalineaci&#243;n entre la imagen PET y la imagen TC y un incremento en el valor del SUV<span class="elsevierStyleInf">max</span> respecto a las adquisiciones en respiraci&#243;n libre&#46; Hemos demostrado la existencia de correlaci&#243;n entre el &#37;BH-index y la desalineaci&#243;n en respiraci&#243;n libre&#44; significativamente mayor en las lesiones con desalineaci&#243;n superior a 8<span class="elsevierStyleHsp" style=""></span>mm&#46;</p> <span class="elsevierStyleSectionTitle" id="sect0050">Conclusiones</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">La adquisici&#243;n PET-TC de 20 segundos en apnea es una t&#233;cnica viable para la detecci&#243;n de la lesi&#243;n pulmonar en el entorno cl&#237;nico y requiere solo un peque&#241;o aumento en el tiempo de examen sin requerir especial entrenamiento del paciente&#46; La adquisici&#243;n en apnea aporta una medida m&#225;s precisa del SUV<span class="elsevierStyleInf">max</span>&#44; especialmente en las lesiones de los l&#243;bulos inferiores que normalmente presentan una marcada desalineaci&#243;n entre las im&#225;genes PET y TC&#46;</p>"
      ]
    ]
    "multimedia" => array:17 [
      0 => array:7 [
        "identificador" => "fig0005"
        "etiqueta" => "Figure 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
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          "en" => "<p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Sphere activity density values &#40;Bq&#47;ml&#41; are shown in function of acquisition time and S&#47;B ratio values&#46; In the figure are illustrated four spheres&#58; the &#8220;biggest&#8221; &#40;A&#41;&#44; the &#8220;medium&#8221; &#40;B&#41;&#44; the &#8220;next to smallest&#8221; &#40;C&#41;&#44; and the &#8220;smallest&#8221; &#40;D&#41;&#46; Data are represented with 1 &#963; of uncertainty&#46;</p>"
        ]
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      1 => array:7 [
        "identificador" => "fig0010"
        "etiqueta" => "Figure 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
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            "Ancho" => 2276
            "Tamanyo" => 121460
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        "descripcion" => array:1 [
          "en" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Sphere contrast is plotted in function of sphere volume and shown for three illustrative acquisition times&#58; 90<span class="elsevierStyleHsp" style=""></span>s &#40;A&#41;&#44; 20<span class="elsevierStyleHsp" style=""></span>s &#40;B&#41;&#44; and 10<span class="elsevierStyleHsp" style=""></span>s &#40;C&#41;&#46; Contrast is represented with its uncertainty &#40;1 &#963;&#41;&#46; Note that the CV of contrast decreases as sphere volume increases&#46;</p>"
        ]
      ]
      2 => array:7 [
        "identificador" => "fig0015"
        "etiqueta" => "Figure 3"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr3.jpeg"
            "Alto" => 1876
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            "Tamanyo" => 331997
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Contrast value versus acquisition time for the four investigated S&#47;B ratio values&#58; 17&#46;2&#58;1 &#40;A&#41;&#44; 12&#46;1&#58;1 &#40;B&#41;&#44; 7&#46;4&#58;1 &#40;C&#41;&#44; and 3&#46;0&#58;1 &#40;D&#41;&#46; Contrast values are represented for the &#8220;biggest&#8221;&#44; the &#8220;medium&#8221;&#44; the &#8220;next to smallest&#8221; and the &#8220;smallest&#8221; sphere&#46; Contrast values are shown with its uncertainty &#40;1&#963;&#41;&#46;</p>"
        ]
      ]
      3 => array:7 [
        "identificador" => "fig0020"
        "etiqueta" => "Figure 4"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr4.jpeg"
            "Alto" => 1932
            "Ancho" => 3576
            "Tamanyo" => 376691
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        "descripcion" => array:1 [
          "en" => "<p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Sphere contrast in function of acquisition time and S&#47;B ratio value&#46; Contrast is shown for the &#8220;biggest&#8221; &#40;A&#41;&#44; the &#8220;medium&#8221; &#40;B&#41;&#44; the &#8220;next to smallest&#8221; &#40;C&#41;&#44; and the &#8220;smallest&#8221; &#40;D&#41; sphere&#46; Data are illustrated with 1 &#963; uncertainty&#46;</p>"
        ]
      ]
      4 => array:7 [
        "identificador" => "fig0025"
        "etiqueta" => "Figure 5"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr5.jpeg"
            "Alto" => 3673
            "Ancho" => 1726
            "Tamanyo" => 345172
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Change of contrast in function of S&#47;B ratio value&#46; Contrast is plotted for the &#8220;biggest&#8221;&#44; the &#8220;medium&#8221;&#44; the &#8220;next to smallest&#8221; and the &#8220;smallest&#8221; sphere and for three illustrative acquisition times&#58; 30<span class="elsevierStyleHsp" style=""></span>s &#40;A&#41;&#44; 20<span class="elsevierStyleHsp" style=""></span>s &#40;B&#41;&#44; and 10<span class="elsevierStyleHsp" style=""></span>s &#40;C&#41;&#46; Contrast is shown with its uncertainty &#40;1&#963;&#41;&#46;</p>"
        ]
      ]
      5 => array:7 [
        "identificador" => "fig0030"
        "etiqueta" => "Figure 6"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr6.jpeg"
            "Alto" => 2542
            "Ancho" => 3000
            "Tamanyo" => 516194
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">PET&#47;CT images of the thorax acquired in FB &#40;A&#44; transaxial&#59; B&#44; sagittal&#44; C&#44; coronal&#41; and PET&#47;CT images of the thorax in the same patients&#44; acquired in DIBH &#40;D&#44; transaxial&#59; E&#44; sagittal&#59; F&#44; coronal&#41;&#46; There is an evidence of a large misalignment between PET and CT images concerning the lesion located in the left upper&#46;</p>"
        ]
      ]
      6 => array:7 [
        "identificador" => "fig0035"
        "etiqueta" => "Figure 7"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr7.jpeg"
            "Alto" => 972
            "Ancho" => 1718
            "Tamanyo" => 77124
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Correlation between movement and &#37; SUV<span class="elsevierStyleInf">max</span> BH index in group A &#40;19 lesions&#41;&#46; <span class="elsevierStyleItalic">R</span><span class="elsevierStyleSup">2</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;324&#44; <span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;01 lobe&#46;</p>"
        ]
      ]
      7 => array:7 [
        "identificador" => "fig0040"
        "etiqueta" => "Figure 8"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr8.jpeg"
            "Alto" => 2530
            "Ancho" => 3000
            "Tamanyo" => 479218
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">PET&#47;CT images of the thorax acquired in FB &#40;A&#44; transaxial&#59; B&#44; sagittal&#44; C&#44; coronal&#41; and PET&#47;CT images of the thorax in the same patients&#44; acquired in DIBH &#40;D&#44; transaxial&#59; E&#44; sagittal&#59; F&#44; coronal&#41;&#46; The misalignment between PET and CT is dramatically reduced in DIBH images&#46; In FB images&#44; SUV<span class="elsevierStyleInf">max</span> was 5&#46;17&#44; SUV<span class="elsevierStyleInf">mean</span> was 3&#46;31&#44; volume was 3&#46;04<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span>&#46; In DIBH&#44; SUV<span class="elsevierStyleInf">max</span> was 18&#46;82 &#40;364&#37; compared to FB&#41;&#44; SUV<span class="elsevierStyleInf">mean</span> was 6&#46;84 &#40;206&#37; compared to FB&#41;&#44; lesion volume was 4&#46;43<span class="elsevierStyleHsp" style=""></span>cm<span class="elsevierStyleSup">3</span> &#40;146&#37; compared to FB&#41;&#46;</p>"
        ]
      ]
      8 => array:7 [
        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "tabla" => array:1 [
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Sphere&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Sphere volume &#40;cm<span class="elsevierStyleSup">3</span>&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Parameter&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " colspan="6" align="center" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Acquisition time &#40;s&#41;</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">15&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">20&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">30&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">90&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="9" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Contrast with S&#47;B ratio<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>17&#46;2</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">24&#46;65</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">2</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">12&#46;08</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">3</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">6&#46;4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">4</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">2&#46;51</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">5</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">14&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">6</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">0&#46;53</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">2&#46;0&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;2&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">37&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">18&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " colspan="9" align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " colspan="9" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Activity density &#40;Bq&#47;ml&#41; with S&#47;B ratio<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>17&#46;2</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">1</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">24&#46;65</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">10&#44;243&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">10&#44;269&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">10&#44;312&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">10&#44;195&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">10&#44;268&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">10&#44;278&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">319&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">396&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">268&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">202&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">195&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">141&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">2</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">12&#46;08</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">8728&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">8888&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8925&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">401&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">245&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">24&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2978&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3047&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2855&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3117&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2906&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2917&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">900&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">606&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">644&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">334&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">301&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">158&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">30&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">20&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">23&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">11&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Average background value</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1150&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1082&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1103&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1051&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1082&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1089&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab492040.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Mean&#44; standard deviation &#40;SD&#41; and coefficient of variation &#40;CV&#41; of sphere contrast and sphere activity density &#40;Bq&#47;ml&#41; are listed for each reconstruction time and for all spheres sizes&#46; The last row of the table lists the average background values &#40;Bq&#47;ml&#41;&#46; Data refer to the 17&#46;2&#58;1 sphere-to-background &#40;S&#47;B&#41; ratio acquisition&#46;</p>"
        ]
      ]
      9 => array:7 [
        "identificador" => "tbl0010"
        "etiqueta" => "Table 2"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "tabla" => array:1 [
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Sphere&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Sphere volume &#40;cm<span class="elsevierStyleSup">3</span>&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Parameter&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " colspan="6" align="center" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Acquisition time &#40;s&#41;</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">15&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">20&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">30&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">90&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="9" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Contrast with S&#47;B ratio<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>12&#46;1</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">1</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">24&#46;65</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">6&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;0&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">1&#46;4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;3&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">23&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">11&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">2</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">12&#46;08</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">8&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">10&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">3</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">5&#46;75</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">4&#46;4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">4&#46;9&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">4&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">4&#46;7&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">4&#46;7&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">4&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;9&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;6&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;2&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">21&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">11&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">13&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">9&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">4&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">4</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">2&#46;51</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">3&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">3&#46;7&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">3&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">3&#46;7&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">3&#46;6&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;2&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">20&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">10&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">11&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">5</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">1&#46;10</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">2&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">0&#46;4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">14&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">10&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">6</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">0&#46;53</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;2&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">69&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " colspan="9" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Activity density &#40;Bq&#47;ml&#41; with S&#47;B ratio<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>12&#46;1</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">8335&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">8529&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">8576&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">8619&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">129&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">1&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">2</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">12&#46;08</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">7299&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">7586&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">7579&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">7605&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">7652&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">7692&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">16&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">34&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">18&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">12&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="3" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Average background value</td><td class="td" title="\n
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                  \t\t\t\t\ttop\n
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                  \t\t\t\t">1131&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">1227&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">1165&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1182&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1205&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab492038.png"
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        "descripcion" => array:1 [
          "en" => "<p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">The same parameters of <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> are reported but contrast and activity density values refer to the 12&#46;1&#58;1 sphere-to-background ratio acquisition&#46;</p>"
        ]
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      10 => array:7 [
        "identificador" => "tbl0015"
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            0 => array:2 [
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                0 => """
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">Sphere&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Sphere volume &#40;cm<span class="elsevierStyleSup">3</span>&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Parameter&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " colspan="6" align="center" valign="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">Acquisition time &#40;s&#41;</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="" valign="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">10&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">15&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">20&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">30&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">90&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " colspan="9" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Contrast with S&#47;B ratio<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>7&#46;4</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">4&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">2</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">12&#46;08</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">11&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">10&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">3</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">5&#46;75</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">2&#46;9&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2&#46;8&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2&#46;7&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">2&#46;7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">2&#46;6&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;8&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;4&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;2&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">27&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">13&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">12&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">7&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">4</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">2&#46;51</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">2&#46;3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2&#46;3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2&#46;0&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2&#46;1&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2&#46;1&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">0&#46;3&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">31&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">8&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">7&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">5</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">1&#46;10</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">1&#46;7&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">1&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">1&#46;5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">0&#46;53</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t  " colspan="9" align="left" valign="\n
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                  \t\t\t\t">Activity density &#40;Bq&#47;ml&#41; with S&#47;B ratio<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>7&#46;4</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">6575&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">1&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">2</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">12&#46;08</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">5312&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">5627&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">5504&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">5644&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">5581&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">5661&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">308&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">367&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t  " colspan="3" align="left" valign="\n
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                  \t\t\t\t">Average background value</td><td class="td" title="\n
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                  \t\t\t\t</td></tr></tbody></table>
                  """
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                  \t\t\t\t">Sphere volume &#40;cm<span class="elsevierStyleSup">3</span>&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Parameter&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">Acquisition time &#40;s&#41;</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">5&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">15&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">20&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">30&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " colspan="9" align="left" valign="\n
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                  \t\t\t\t">Contrast with S&#47;B ratio<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>3</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Activity density &#40;Bq&#47;ml&#41; with S&#47;B ratio<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>3</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t">2</td><td class="td" title="\n
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                  \t\t\t\t  " rowspan="3" align="left" valign="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">Mean&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">1834&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">1904&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">1969&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">2025&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">1927&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">2023&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">257&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">167&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">63&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">1393&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">SD&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">631&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">CV &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t  " colspan="3" align="left" valign="\n
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                  \t\t\t\t">Average background value</td><td class="td" title="\n
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                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
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      12 => array:7 [
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                0 => """
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                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Lesion number&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">Sex&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Administered dose&#44; MBq&#47;kg&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Age&#44; years&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">Diameter&#44; mm&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">SUV<span class="elsevierStyleInf">max</span> FB&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">SUV<span class="elsevierStyleInf">max</span> BH&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">SUV<span class="elsevierStyleInf">mean</span> FB&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">SUV<span class="elsevierStyleInf">mean</span> BH&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">VOL<span class="elsevierStyleInf">cc</span> FB&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">VOL<span class="elsevierStyleInf">cc</span> BH&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" style="border-bottom: 2px solid black">Disalignment&#44; mm&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">21&#46;2&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2&#46;43&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab492039.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0105" class="elsevierStyleSimplePara elsevierViewall">Characteristics of the lung lesions detected in the clinical study&#46;</p>"
        ]
      ]
      13 => array:7 [
        "identificador" => "tbl0030"
        "etiqueta" => "Table 6"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "tabla" => array:1 [
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><td class="td" title="\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Upper lung area <span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>8&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t" style="border-bottom: 2px solid black">Lower lung area <span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>11&nbsp;\t\t\t\t\t\t\n
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Article information
ISSN: 2253654X
Original language: English
DOI:10.1016/j.remn.2013.08.006
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