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Axial CT (B) showed enlarged mediastinal lymph nodes while PET (C) and fusion images (D) showed high uptake of <span class="elsevierStyleSup">18</span>F-FDG in mediastinal lymph nodes (SUVmax 7.9). Intense FDG uptake in mid-esophagus with SUVmax of 10.7 on PET (E) and fusion (G) images were corresponding to the esophagus with thickened wall and narrow lumen on axial CT image (F) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).</p>" ] ] ] "autores" => array:1 [ 0 => array:2 [ "autoresLista" => "Q. Xu, M. Lv, Y. Xia, Y. Zhao, W. Zhang, Y. Chen" "autores" => array:6 [ 0 => array:2 [ "nombre" => "Q." "apellidos" => "Xu" ] 1 => array:2 [ "nombre" => "M." "apellidos" => "Lv" ] 2 => array:2 [ "nombre" => "Y." "apellidos" => "Xia" ] 3 => array:2 [ "nombre" => "Y." "apellidos" => "Zhao" ] 4 => array:2 [ "nombre" => "W." "apellidos" => "Zhang" ] 5 => array:2 [ "nombre" => "Y." "apellidos" => "Chen" ] ] ] ] ] "idiomaDefecto" => "en" "Traduccion" => array:1 [ "es" => array:9 [ "pii" => "S2253654X19301611" "doi" => "10.1016/j.remn.2019.07.002" "estado" => "S300" "subdocumento" => "" "abierto" => array:3 [ "ES" => false "ES2" => false "LATM" => false ] "gratuito" => false "lecturas" => array:1 [ "total" => 0 ] "idiomaDefecto" => "es" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253654X19301611?idApp=UINPBA00004N" ] ] "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2253808919300953?idApp=UINPBA00004N" "url" => "/22538089/0000003900000003/v3_202101050619/S2253808919300953/v3_202101050619/en/main.assets" ] "en" => array:20 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Continuing Education</span>" "titulo" => "Relevance of quantification in brain PET studies with <span class="elsevierStyleSup">18</span>F-FDG" "tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "184" "paginaFinal" => "192" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "A. Niñerola-Baizán, P. Aguiar, M.N. Cabrera-Martín, C. Vigil, A. Gómez-Grande, C. Lorenzo, S. Rubí, P. Sopena, V. Camacho" "autores" => array:10 [ 0 => array:3 [ "nombre" => "A." "apellidos" => "Niñerola-Baizán" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] 1 => array:3 [ "nombre" => "P." "apellidos" => "Aguiar" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">d</span>" "identificador" => "aff0020" ] ] ] 2 => array:3 [ "nombre" => "M.N." "apellidos" => "Cabrera-Martín" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">e</span>" "identificador" => "aff0025" ] ] ] 3 => array:4 [ "nombre" => "C." "apellidos" => "Vigil" "email" => array:1 [ 0 => "carmen.vigil@sespa.es" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">f</span>" "identificador" => "aff0030" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 4 => array:3 [ "nombre" => "A." "apellidos" => "Gómez-Grande" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">g</span>" "identificador" => "aff0035" ] ] ] 5 => array:3 [ "nombre" => "C." "apellidos" => "Lorenzo" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">h</span>" "identificador" => "aff0040" ] ] ] 6 => array:3 [ "nombre" => "S." "apellidos" => "Rubí" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">i</span>" "identificador" => "aff0045" ] ] ] 7 => array:3 [ "nombre" => "P." "apellidos" => "Sopena" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">j</span>" "identificador" => "aff0050" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">k</span>" "identificador" => "aff0055" ] ] ] 8 => array:3 [ "nombre" => "V." "apellidos" => "Camacho" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">l</span>" "identificador" => "aff0060" ] ] ] 9 => array:1 [ "colaborador" => "por el Grupo de Neuroimagen SEMNIM" ] ] "afiliaciones" => array:12 [ 0 => array:3 [ "entidad" => "Servicio de Medicina Nuclear, Hospital Clínic, Barcelona, Spain" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Grupo de Imagen Biomédica, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Grupo de Imaxe Molecular e Física Médica, Departamento de Radioloxía, Facultade de Medicina, Universidade de Santiago de Compostela, Santiago de Compostela, Spain" "etiqueta" => "c" "identificador" => "aff0015" ] 3 => array:3 [ "entidad" => "Servicio de Medicina Nuclear, Hospital Clínico de Santiago de Compostela, Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain" "etiqueta" => "d" "identificador" => "aff0020" ] 4 => array:3 [ "entidad" => "Servicio de Medicina Nuclear, Hospital Clínico San Carlos, Madrid, Spain" "etiqueta" => "e" "identificador" => "aff0025" ] 5 => array:3 [ "entidad" => "Servicio Medicina Nuclear, Hospital Universitario Central de Asturias, Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain" "etiqueta" => "f" "identificador" => "aff0030" ] 6 => array:3 [ "entidad" => "Servicio de Medicina Nuclear, Hospital Universitario 12 de Octubre, Madrid, Spain" "etiqueta" => "g" "identificador" => "aff0035" ] 7 => array:3 [ "entidad" => "Servicio de Medicina Nuclear, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain" "etiqueta" => "h" "identificador" => "aff0040" ] 8 => array:3 [ "entidad" => "Servicio de Medicina Nuclear, Hospital Universitari Son Espases, Institut d’Investigació Sanitària Illes Balears (IdISBa), Palma, Spain" "etiqueta" => "i" "identificador" => "aff0045" ] 9 => array:3 [ "entidad" => "Servicio de Medicina Nuclear, Hospital Vithas-Nisa 9 de Octubre, Valencia, Spain" "etiqueta" => "j" "identificador" => "aff0050" ] 10 => array:3 [ "entidad" => "Servicio de Medicina Nuclear, Hospital Universitario y Politécnico La Fe, Valencia, Spain" "etiqueta" => "k" "identificador" => "aff0055" ] 11 => array:3 [ "entidad" => "Servicio de Medicina Nuclear, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain" "etiqueta" => "l" "identificador" => "aff0060" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Relevancia de la cuantificación en los estudios PET cerebrales con <span class="elsevierStyleSup">18</span>F-FDG" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1318 "Ancho" => 1505 "Tamanyo" => 256684 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Fusion <span class="elsevierStyleSup">18</span>F-FDG PET with MR in a patient with temporal lobe epilepsy showing a map of significant differences compared to a database of normal images obtained by voxel-based quantification.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">In the last years there has been a gradual increase in the use of biomarkers in the clinical diagnostic criteria of neurodegenerative diseases, with a notable rise in imaging modalities. In this context, functional neuroimaging techniques such as positron emission tomography (PET) have opened a new pathway for improving our knowledge of the physiopathology of disease <span class="elsevierStyleItalic">in vivo</span>, with greater specficity than the clinical manifestations.</p><p id="par0010" class="elsevierStylePara elsevierViewall">The fixation of the radiopharmaceutical in fluorodeoxyglucose F 18 (<span class="elsevierStyleSup">18</span>F-FDG) PET studies is very elevated, since glucose is the almost exclusive source of energy to the brain and also because of the low proportion of glucose-6-phosphatase in brain tissue. Thus, the incorporation of glucose is directly proportional to brain metabolism and synaptic activity. Anomalies in <span class="elsevierStyleSup">18</span>F-FDG uptake are the result of the combination of multiple processes involved in the physiopathology of the disease such as the expression of specific genes, mitochondrial dysfunction, oxidative stress, glial activation, inflammation, loss of synapsis and cell death.</p><p id="par0015" class="elsevierStylePara elsevierViewall">This radiotracer constitutes a valid indirect biomarker of synaptic dysfunction even before atrophy and cell death are produced, thereby allowing diagnosis in very early stages of the disease.<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">1</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">The Spanish Society of Neurology (SEN) and the Spanish Society of Nuclear Medicine and Molecular Imaging (SEMNIM) met in 2013 to discuss the use of PET biomarkers in the diagnosis of neurodegenerative diseases. They recommended the use of <span class="elsevierStyleSup">18</span>F-FDG PET in patients with cognitive impairment and a non established diagnosis after clinical, neuropsychological and structural evaluation by computerized tomography (CT) and/or magnetic resonance (MR) and in those in whom diagnostic certainty can be increased and have an impact on the management of the patient.<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">1</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">Later, in 2018, the European Association of Nuclear Medicine (EANM) and the European Academy of Radiology (EAN) carried out a joint project (Delphi consensus) to orient the use of brain <span class="elsevierStyleSup">18</span>F-FDG PET in different clinical indications. This technique was considered to be useful in the differentiation of different types of dementia (Alzheimer disease (AD), Lewy body dementia, frontotemporal degeneration or vascular dementia) the diagnosis of mild congnitive impairment (MCI), atypical AD, primary progressive aphasia and in the differentation of Parkinsonisms.<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">2</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">On the other hand, in addition to dementias and movement disorders, another indication of cerebral <span class="elsevierStyleSup">18</span>F-FDG PET is the detection of an epileptogenic focus in drug-resistant epilepsies when considering surgical treatment.<a class="elsevierStyleCrossRef" href="#bib0215"><span class="elsevierStyleSup">3</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">Adquate use of this technique and correct interpretation can add crucial information for patient management. Visual assessment of the PET image is generally what is indicated in the evaluation of the studies,<a class="elsevierStyleCrossRef" href="#bib0220"><span class="elsevierStyleSup">4</span></a> but it should be taken into account that the diagnosis largely depends on the experience of the medical specialist, especially in cases showing early or subtle alterations.</p><p id="par0040" class="elsevierStylePara elsevierViewall">The search for objective criteria supporting visual analysis of PET images in neurodegenerative diseases has led to the implementation of processing techniques which are able to obtain quantitative values of these images.</p><p id="par0045" class="elsevierStylePara elsevierViewall">There is currently wide experience in the use of quantitative methodologies. The simplest is the standardized uptake value (SUV) which quantifies the absolute number of counts. However, multiple factors influence this measure, including the weight of the patient, the dose injected, acquisition time or the plasma glucose levels at the time of radiotracer injection. This generates a great variability, even in the same patient. In this setting, the determination of the SUV ratio (SUVR) is of great interest, since it can correct this variability and normalize the count values to the mean value of the global uptake in the brain or a specific region such as the cerebellum or the protuberance. In this way, the values obtained are relative to the region of reference and allow reliable comparisons in the same patient or among different patients. However, it should be taken into account that these ratios do not provide information of the absolute value of FDG uptake in all the cerebral parenchyma.</p><p id="par0050" class="elsevierStylePara elsevierViewall">There are currently more complex and automated imaging analyses which can largely standardize the statistical analysis of the brain PET and allow better comparison among studies. These methods of quantification perform an analysis based on voxels which does not carry out a quantification that is comparable to obtaining an SUV value. They extract quantitative information from the image in the post-processing, the aim of which is to generate statistical maps to obtain a p value. The historical beginning of these analyses began with statistical parametric mapping or SPM, a free software that allows the analysis of functional neuroimaging images using statistical techniques.</p><p id="par0055" class="elsevierStylePara elsevierViewall">In routine clinical practice, quantification methods are generally not necessary, but it is true that in an important number of cases these methods add greater credibility to the results obtained. Galileo Galilei said “Measure what can be measured, and make measurable what cannot be measured.” This premise continues to be valid today, and these new methods of image analysis have undoubtedly been extensively used in investigation, providing greater reliabiilty for the making of conclusions and having demonstrated their capacity to generate a greater grade of diagnostic reliability for specialists.<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">5</span></a> Along this line, different studies have shown that quantitative analysis increases the reproducibility and the diagnostic specificity of neurological diseases,<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">6</span></a> while a diagnosis based only on visual analysis depends on the experience of the specialist, especially in those cases in which the anomalies in the PET image are not evident. In view of this, it has been considered that there is sufficient evidence to recommend the use of imaging analysis tools to aid and support visual diagnosis in routine clinical practice. Since there is a large number of quantification tools, there is no consensus as to which is optimal in each case. On the other hand, these tools are complex and make use of basic image processing techniques which are important to know. Below we provide a detailed description of the imaging analysis techniques which include PET image quantification programs that are currently available for clinical use, as well as their advantages and limitations.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Semiquantitative/quantitative analysis</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Realignment, co-registry and spatial normalization</span><p id="par0060" class="elsevierStylePara elsevierViewall">Quantification of PET images requires combining the functional image with the MR image which provides structural information and facilitates the localization of the regions of the brain to be analyzed. This process consists in the application of mathematical transformations that are able to rewrite all the images in the same reference space so that there is point by point (pixel to pixel) correspondence between them. It is clear that two images obtained in different scanners, albeit of the same patient, will not be on the same reference space since the patient is not in exactly the same position. The mathematical transformations are applied so that the same pixels of an image correspond to the same anatomical region in all of the images as if the images had been acquired with the patient in the same position.</p><p id="par0065" class="elsevierStylePara elsevierViewall">There are several types of techniques according to the characteristics of the images to be processed:</p><p id="par0070" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Realignment techniques</span>: these refer to mathematical transformations that are able to rewrite, in a common reference space, several images of the same patient and of the same modality. Realignment techniques should be applied in cases in which you want to compare two PET images of a patient acquired before and after treatment. In this case, realignment of the two PET images will, for example, allow pixel to pixel subtraction between the two images in order to look for differences. The mathematical transformation making up a realignment process are transformations related to rotations and tridimensional translations (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). The realignment process consists in an iterative algorithm able to find the solution rapidly, guided by the values of the spatial correlation that are obtained.</p><p id="par0075" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Co-registry techniques</span>: these techniques are mathematical transformations used for rewriting, in the same reference space, images of the same patient from a different modality. Co-registry techniques should be used when you wish to fuse a PET image with an MR image of the same patient. This process is similar to the previous one, and therefore, the transformations involved are of new rotations and tridimensional translations. It should be taken into account that this process works provided that there is little time between the acquisition of the PET study and the MR study so that there are no great anatomical differences between the two studies. The difference with the realignment techniques is that the pixel size of an MR image is smaller that that of a PET image, and it is therefore necessary to apply resampling, a technique based on linear interpolations (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>).</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0080" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Spatial normalization techniques</span>: these are mathematical transformation used to rewrite, in the same reference space, images of different patients but of the same modality. These techniques should be applied when you wish to compare the PET image of a patient with a reference PET image; for example, corresponding to the average of a database of normal subjects. As in the previous cases, rotations and tridemsional translations must be applied as well as known deformations such as shear and warping (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). In most cases shear deformations are sufficient. These are linear and do not generate discontinuities. However, in special cases in which normalization cannot be performed due to the presence of anomalies which distort the shape of the brain such as the presence of a tumor or previous surgery, it is neccesary to apply non related deformations as in warping. These transformations generate discontinuity in the image, and in many cases, are not able to find an optimal solution, and therefore, require supervision.</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0085" class="elsevierStylePara elsevierViewall">Spatial normalization of PET images can be directly done by seeking the mathematical transformation that rewrites the PET image of the patient in the space of the reference PET image. However, it is advisable to estimate this transformation from the MR image of the patient since the MR image provides very useful anatomical information in the search for the solution, and the process is much more accurate. Spatial normalization through the MR image needs previous segmentation of the image in gray matter, white matter, cerebrospinal fluid, bone and skin. Spatial normalization is calculated separately for each of these, and afterwards, optimal transformation is sought as the average of all segments. By previously co-registering the PET image with the MR image of the patient, normalization of the PET can simply be done by applying the transformation obtained on the PET image. This process has shown to be much more accurate than direct normalization of the PET image.<elsevierMultimedia ident="tbl0005"></elsevierMultimedia></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Methods for the quantification of <span class="elsevierStyleSup">18</span>F-FDG PET images</span><p id="par0090" class="elsevierStylePara elsevierViewall">Image analysis methods can be classsified as those based on delimitation of the brain into volumes of interest (VOIs) or methods focused on the generation of parametric images by voxel to voxel analysis (extrapolation of the pixels to a tridimensional image).</p><p id="par0095" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">VOI-based quantification</span>: this is focused on the estimation of the average concentration of the radiopharmaceutical in different VOIs of the brain. The indexes obtained from this type of quantification are usually relative; for example, a quotient between the average of the radiopharmceutical in the VOI we wish to study and a reference VOI that is considered stable, such as the cerebellum. In other cases, the contralateral region is used as the VOI of reference, and thus, the index measures the grade of left-right asymmetry of a region of the brain. The essential question with these methods is that automatic delimitation of the VOIs can be made for each patient since manual segmentation would be too tedious a process and operator dependent. The current methods make use of a standard anatomical reference atlas that contains previous delimitation of the VOIs of interest for diagnosis. To adjust this standard atlas to the anatomy of each patient, it is necessary to apply the transformations of co-registry and spatial normalization explained in the previous section and shown in <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>A. The first step is to co-register the PET image with the MR of the patient. Then, transformation (N) that normalizes the MR of the patient to the reference space of the Montreal Neurological Institute (MNI) is estimated. Since the atlas of VOIs also refers to the MNI space, if the inverse transformation N<span class="elsevierStyleSup">−1</span> is applied over this atlas, a map of VOIs is obtained that adjusts to the MR of the patient, and thus, the PET image that had previously been co-registered (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>A).</p><p id="par0100" class="elsevierStylePara elsevierViewall">The atlases best known and used for the quantification of PET images are described below. First, the Brodmann Atlas<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">7</span></a> was one of the first anatomical segmentations of the brain and consists in segmenting the human brain based on histology and organization of the neurons. The segmentation results in 52 areas which, in turn, can be regrouped according to cortical functions such as the primary visual cortex or the auditory cortex. On the other hand, the Automated Anatomical Labeling (AAL) Atlas<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">8</span></a> is a segmentation manual of MR images in 45 functional regions of the brain per each hemisphere. The Desikan-Killiany Atlas<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">9</span></a> is a probabilistic segmentation of the brain into 34 cortical areas per each hemisphere obtained as the average of the manual segmentation of 40 MR images. Lastly, Hammers Atlas<a class="elsevierStyleCrossRefs" href="#bib0250"><span class="elsevierStyleSup">10,11</span></a> is a probabilistic segmentation of the brain into 45 regions per each hemisphere from the manual segmentation of 30 MR images.</p><p id="par0105" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Voxel-based quantification</span>: these quantification methods are based on a statistical comparison between the PET image of the patient and a group of PET images obtained in control subjects.<a class="elsevierStyleCrossRef" href="#bib0260"><span class="elsevierStyleSup">12</span></a> Since the comparison is made point by point (voxel to voxel), it is necessary to apply spatial normalization to convert not only the PET of the patient but also the PETs of the control subjects to the standard MNI space as shown in <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>B. The result is a parametric image showing statistically significant values (p values) obtained from the voxel to voxel comparison between the PET image of the patient and the group of PET images of the controls. The interpretation of this parametric image of p values is not direct, and two types of thresholds must be applied. First, the threshold of statistical significance must be defined (usually set between 0.05 and 0.001) considering the presence of multiple comparisons. With this threshold, most of the points of the parametric image will have values above the threshold and will be set at zero, since they will not be considered as significant. Only points with statistically significant p values will be visible. Second, a spatial threshold must be applied. This is because the presence of statistically significant values presented as isolated points have no real value and may be considered as noise. Only points that make up part of the clusters of points above a certain threshold are considered significant; for example, between 100–500 points, which should also be defined according to the application. An alternative to the previous method, albeit simpler in terms of computational calculation, is Three-dimensional Stereotactic Surface Projection (3D-SSP).<a class="elsevierStyleCrossRef" href="#bib0265"><span class="elsevierStyleSup">13</span></a> This method consists in a simplification by which the statistical comparison between the patient and the control group is made only from the PET images projected in a determined direction instead of comparison of all the volume. Therefore, a pixel to pixel statistical comparison is made instead of voxel to voxel. The final result is a parameteric image showing the differences projected on the brain surface corresponding with the stereotactic projections of the brain. In general, the advantage of these voxel- or pixel-based methods compared to VOI-based methods is that they do not require previous anatomical delimitation, and thus, an atlas is not necessary, making the sensitivity of this quantification method much greater. However, incorrect definition of the threshold processes described previously could lead to a low specificity. In addition, the quantification methods based on these techniques use heterogeneous protocols, and greater standardization is needed for their adequate incorporation in routine clinical practice.<elsevierMultimedia ident="tbl0010"></elsevierMultimedia></p></span></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Programs available</span><p id="par0110" class="elsevierStylePara elsevierViewall">Although quantification methods may be complex to carry out, today there are several programs that are easy to perform. Some of the programs currently available for the quantification of <span class="elsevierStyleSup">18</span>F-FDG PET images are discussed below, with several already being integrated in routine clinical practice. It is also specified whether each of these programs has the CE marking of conformity, which is a mandatory requisite for the commercialization of health care products in Europe. CE marking is a declaration of the manufacturer of health care products that a product fulfills the essential requisites of all the European directives related to health care products and is a legal requisite for marketing a health care product in the market of the European Union.</p><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">CortexID Suite (General Electric Healthcare)</span><p id="par0115" class="elsevierStylePara elsevierViewall">CortexID Suite allows automated analysis. When the PET image is uploaded a control of quality and visual analysis is made, and the image is automatically normalized to the standard space. Then, it is determined that the normalization is correct; the image can be reoriented and the process repeated. The PET image normalizes in intensity according to the protuberance, the cerebellum or all the brain. 3D-SSP is the quantification method used, and the results are presented as a <span class="elsevierStyleItalic">z</span>-score (number of deviation with respect to the mean), voxel to voxel or in stereotactic projections, with the user being able to choose the threshold of significance. The <span class="elsevierStyleItalic">z</span>-score calculated in predefined VOIs and/or created by the user is also presented in a table. The database available allows comparisons in four age groups. In addition, longitudinal comparison can be made between two studies and co-register the PET image with a MR image and fuse the results with the MR image or the CT itself. CortexID Suite is an aplication for General Electric work stations and has CE marking.</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Database Comparison (Siemens Healthineers)</span><p id="par0120" class="elsevierStylePara elsevierViewall">Database Comparison is also known as Scenium y syngo.PET DB Comparison. This program makes a statistical comparison of the PET image with a normal reference image by automated analysis designed by work flow. First, the database that will be used is selected, and the PET image is normalized in the standard space, with manual alignment being possible. Then, the <span class="elsevierStyleItalic">z</span>-score is calculated for each voxel and in VOIs according to the AAL Atlas.<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">9</span></a> The results can be visualized in orthogonal, cortical views or in Stereotactic Surface Projection (SSP) images. Normalization of intensity is performed according to the cerebellum or all of the brain. There are seven <span class="elsevierStyleSup">18</span>F-FDG PET databases of normal patients which allow the analysis of a variety of scanners, reconstruction protocols and ages. The results obtained can be co-registered on a CT or MR image. In addition, it allows the creation and management of personalized databases. Database Comparison is an application for Siemens work stations and has CE marking.</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">MIMneuro (MIM Software)</span><p id="par0125" class="elsevierStylePara elsevierViewall">MIMneuro allows automated analysis following work flow. The user imports the study of the patient through a connection to the picture archiving and communication system (PACS), and the flow begins with automatic normalization to the standard space, with manual adjustments being possible. Secondly, the study of intensity normalizes according to a specific structure or all of the brain. Finally, it is quantified according to VOIs and voxels. VOI-based quantification calculates the <span class="elsevierStyleItalic">z</span>-score and asymmetry indexes in the VOIs defined in the atlases created by MIM Software and/or the users themselves. These values are calculated from the comparison of the subject with the database of <span class="elsevierStyleSup">18</span>F-FDG PET studies of normal subjects. Voxel-based quantification calculates the <span class="elsevierStyleItalic">z</span>-score voxel to voxel showing the results according to a threshold of statistical and spatial significance which the user can modify. In addition, the results of these two analyses can be visualized in cortical projections. The results obtained can be co-registered on a CT or MR image. In addition, it allows the creation and management of personalized databases. MIMneuro requires installation upon payment of the license and has CE marking.</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Neurocloud-PET (Qubiotech Health Intelligence)</span><p id="par0130" class="elsevierStylePara elsevierViewall">Neurocloud-PET is a cloud platform that is compatible with any navegator and is accessible from computers, tablets and smartphones. After the user has imported the images (DICOM or Nifti format), the analysis is totally automatic and is compatible with most scanners. Voxel-based quantification is made by parametric statistical analysis using age as the covariable. The image of the subject is compared with a large database of healthy subjects. The results are shown according to a threshold of statistical and spatial significance. It also makes VOI-based quantification in which the <span class="elsevierStyleItalic">z</span>-score and asymmetry indexes are calculated according to either the Hammers<a class="elsevierStyleCrossRef" href="#bib0250"><span class="elsevierStyleSup">10</span></a> or the AAL Atlas.<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">8</span></a> The results obtained can be co-registered on a CT or MR image. If an MR is available, the spatial normalization is calculated from this image and correction for atrophy of the different VOIs is made, obtaining improved and fused results on the MR. Normalization of intensity is performed by an iterative process based on the histogram of the image which automatically localizes the regions not affected by the disease. Neurocloud-PET does not require installation and payment is made per analysis. This program has CE marking.</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Neurology (Hermes Medical Solutions)</span><p id="par0135" class="elsevierStylePara elsevierViewall">Neurology provides automated analysis performing the normalization to the standard space followed by VOI-, voxel- and 3D-SSP-based quantification. The results are shown in a map with the values of the <span class="elsevierStyleItalic">z</span>-score and can be fused on the MR image. In addition, personalized databases can be made. Neurology requires installation upon payment of the license and has CE marking.</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">NeuroQ (Syntermed)</span><p id="par0140" class="elsevierStylePara elsevierViewall">NeuroQ performs automatic quantification based on VOIs. The user imports the study of the patient through a connection to the PACS and starts the quality control in which the user should select the cut-off corresponding to the brain, select a threshold to eliminate the cranium and correct the slope of the study. Then, the program automatically normalizes the PET image to a standard space and determines the uptake in the VOIs, normalizing the intensity in all the brain, the sensorimotor cortex region, cerebellum, bridge or thalamus. The mean uptake calculated in the VOIs is compared with the database of normal subjects showing the <span class="elsevierStyleItalic">z</span>-score. A spatial threshold is applied to the results. The results obtained can be visualized on the CT image of the patient but do not allow co-registry of the PET image on another CT or MR. In addition, this progam makes longitudinal comparisons between two studies and personalized databases can be created and managed. The VOIs available are defined by comparision with multiple sources including MR, PET and the Talairach-Tournoux Atlas. NeuroQ requires installation upon payment of the license and is an application for Philips work stations. It has CE marking.</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">NeuroSTAT (University of Utah)</span><p id="par0145" class="elsevierStylePara elsevierViewall">NeuroSTAT is a software library for the analysis of images with the 3D-SPP method after normalization of the PET study to the standard space to compare with the database of normal subjects and obtain a map of <span class="elsevierStyleItalic">z</span>-score values. The results can be fused on an MR image. NeuroSTAT requires installation and is free of charge. It does not have CE marking and can only be used for investigation.</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">PETQuant (Cortechs Labs)</span><p id="par0150" class="elsevierStylePara elsevierViewall">PETQuant performs quantification by the VOIs identified by the segmentation of the MR of the patient made in NeuroQuant, a program devoted to quantitative analysis of MR images. The results of the statistical analysis (<span class="elsevierStyleItalic">z</span>-score) of each uptake value in the VOI can be compared with the data of a database of normal subjects. PETQuant can be installed or a cloud platform can be used upon payment of the license. It does not have CE marking and can only be used for investigation.</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">PNEURO/PALZ (PMOD Technologies)</span><p id="par0155" class="elsevierStylePara elsevierViewall">PNEURO provides guided processing for automatic quantification of PET images based on VOIs and voxels. If MR is available, PNEURO segments the cortical regions and the basal lymph nodes in which the quantitative analysis is to be made, with the VOIs used being those of the patient. When an MR image is available, the VOIs can be obtained from Hammers Atlas10 after normalization of the study of the subject to the standard space. In voxel-based quantification, the information of the subject is compared with a database of normal subjects and results in a parametric image with the <span class="elsevierStyleItalic">z</span>-score. PNEURO does not have a database of normal subjects, and thus, in order to perform voxel-based quantification the users must create their own database. PALZ provides automatic processing performing voxel-based quantification by parametric statistical analysis showing the results according to a threshold of statistical and spatial significance. This program is only applicable to <span class="elsevierStyleSup">18</span>F-FDG PET images of subjects with symptoms of AD. PNEURO and PALZ require installation following payment of the license. They do not have CE marking and can only be used for investigation.<elsevierMultimedia ident="tbl0015"></elsevierMultimedia></p><p id="par0160" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0035">Table 1</a> summarizes the different characteristics of the programs described.</p><elsevierMultimedia ident="tbl0035"></elsevierMultimedia><p id="par0165" class="elsevierStylePara elsevierViewall">The large spectrum of possibilities for performing the quantification of PET images in a simple and user friendly manner is evident. Some programs are integrated in the work stations and others are installed in the computer while still others provide the possibility of analyzing the images in an online platform. There is also a variety of quantification methods available. However, in order to choose the program that has a quantification method that best adapts to our needs, it must not be forgotten that any program that we use must first be previously validated and extensive evaluation of the characteristics of the programs is also necessary in regard to the type of analysis and databases available if comparisons are to be made with normal data.<elsevierMultimedia ident="tbl0020"></elsevierMultimedia></p></span></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095"><span class="elsevierStyleSup">18</span>F-FDG PET quantification in dementias</span><p id="par0170" class="elsevierStylePara elsevierViewall">The use of the previously described methods for quantifying <span class="elsevierStyleSup">18</span>F-FDG PET images in dementias has risen notably in the last years.<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">6</span></a> Different quantitative indexes obtained with the combination of the averages of different VOIs or from parametric images have been proposed and validated. Most of the studies published to date include heterogeneous samples of patients with different types of dementia which, on one hand, are aimed at differential diagnosis, and on the other hand, at the prediction of cognitive impairment. From VOI-based quantitative analysis, for example, the average FDG on the interior temporal gyrus has been proposed as a useful index for predicting impairment,<a class="elsevierStyleCrossRef" href="#bib0270"><span class="elsevierStyleSup">14</span></a> with a greater capacity than cognitive evaluation. In the same line, another study<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">15</span></a> performed automatic selection among 90 VOIs of the AAL Atlas of VOIs that had a greater capacity to predict the conversion of DCL patients to AD-type dementia. The selection is made using an algorithm based on Support Vector Machine (SVM) and shows that different subsets of VOIs can predict conversion with area under the curve (AUC) values up to 0.95. On the other hand, with the use of voxel-based quantitative analysis the Hypometabolic Convergence Index (HCI) has been proposed. This index is calculated from the product of the parametric image obtained from the voxel-based analysis of the patient and that obtained from a group of confirmed AD patients.<a class="elsevierStyleCrossRef" href="#bib0280"><span class="elsevierStyleSup">16</span></a> The HCI measures the similarity between the pattern of hypometabolism of the patient and the typical pattern of AD and shows a high predictive value of conversion to AD (AUC<span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>0.78). A comparative study between visual evaluation of <span class="elsevierStyleSup">18</span>F-FDG PET images and a voxel-based quantitative analysis<a class="elsevierStyleCrossRef" href="#bib0285"><span class="elsevierStyleSup">17</span></a> in a more heterogeneous group of patients showed a very significant increase in the AUC obtained by voxel-based quantitative analysis (0.67) compared to visual analysis (0.50). Other comparative studies along the same line showed increases in the AUC from 0.94 to 0.99<a class="elsevierStyleCrossRef" href="#bib0290"><span class="elsevierStyleSup">18</span></a> and from 0.72 to 0.88,<a class="elsevierStyleCrossRef" href="#bib0295"><span class="elsevierStyleSup">19</span></a> in more homogeneous samples of patients including controls, DCL patients and patients with AD. Other studies have shown that visual diagnosis made by expert specialists could achieve the same diagnostic value as quantitative analysis in the differential diagnosis between AD and other dementias,<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">20</span></a> showing an AUC of 0.77 for the quantitative analysis and an AUC of 0.65 (non experts) and 0.76 (experts) for visual analysis. These results were confirmed in other studies for estimating the conversion of DCL patients to AD.<a class="elsevierStyleCrossRefs" href="#bib0305"><span class="elsevierStyleSup">21,22,23</span></a> In a group of patients with primary progressive aphasia including three clinical variants (logopenic, semantic and nonfluent), the mean sensitivity and specificity for the visual analysis were 87.8% and 89.9%, respectively, being 96.9 and 90% for the quantitative analysis, with greater interest in the cases of nonfluent aphasia whch showed more subtle alterations. However, the two evaluators with the greatest number of years of experience in neuroimaging obtained a very high correlation with the clinical diagnosis by visual evaluation, in some cases being even higher than the statistical analysis (kappa 0.439–0.899; for the statistical analysis kappa 0.610–0.806), with visual analysis showing greater dependence on experience.<a class="elsevierStyleCrossRef" href="#bib0320"><span class="elsevierStyleSup">24</span></a></p><p id="par0175" class="elsevierStylePara elsevierViewall">In summary, it can be considered that quantitative analysis increases the diagnostic capacity of <span class="elsevierStyleSup">18</span>F-FDG PET in terms of AUC, which is mainly due to a significant increase in the specificity with respect to visual analysis, maintaining similar or even slightly higher sensitivity values. The usual clinical practice is to visually interpret the parametric images obtained by quantification and not only based on dichotomic indexes. Many studies have described that visual evaluation of <span class="elsevierStyleSup">18</span>F-FDG PET images is highly dependent on evaluator experience, and there is important improvement if the visual interpretation is made on the parametric images generated by the methods of quantification, showing an increase in the level of evaluator confidence.<a class="elsevierStyleCrossRef" href="#bib0230"><span class="elsevierStyleSup">6</span></a><elsevierMultimedia ident="tbl0025"></elsevierMultimedia></p></span><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100"><span class="elsevierStyleSup">18</span>F-FDG PET quantification in epilepsy</span><p id="par0180" class="elsevierStylePara elsevierViewall">The use of quantification methods for the localization of the epileptogenic zone in <span class="elsevierStyleSup">18</span>F-FDG PET images has been proposed and validated in multiple studies by both VOI-based and voxel-based analyses. VOI-based quantitative analysis was first proposed for detecting the epileptogenic focus in <span class="elsevierStyleSup">18</span>F-FDG PET images more than two decades ago.<a class="elsevierStyleCrossRef" href="#bib0325"><span class="elsevierStyleSup">25</span></a> The results showed that this type of analysis provides useful information with respect to visual analysis, mainly in terms of an increase in sensitivity.</p><p id="par0185" class="elsevierStylePara elsevierViewall">Later on, voxel-based quantitiative analysis was also proposed for localizing the focus in <span class="elsevierStyleSup">18</span>F-FDG PET images in a small sample of patients with temporal lobe epilepsy,<a class="elsevierStyleCrossRef" href="#bib0330"><span class="elsevierStyleSup">26</span></a> showing a sensitivity of 71% and a specificity of 100%. <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a> shows an example of a case quantified by this technique. The first comparative studies showed that voxel-based quantitative analysis is consistent with visual analysis carried out by expert evaluators in the case of a sample of patients with temporal lobe epilepsy.<a class="elsevierStyleCrossRef" href="#bib0335"><span class="elsevierStyleSup">27</span></a> Another more recent study showed that both voxel-based quantification and the simplification based on the 3D-SSP technique are able to detect the focus in a greater number of patients than visual analysis of <span class="elsevierStyleSup">18</span>F-FDG PET Images.<a class="elsevierStyleCrossRef" href="#bib0340"><span class="elsevierStyleSup">28</span></a> Another study reported that voxel-based quantitative analysis can localize the focus in 40% of patients who had been informed of negative <span class="elsevierStyleSup">18</span>F-FDG PET results by visual analysis.<a class="elsevierStyleCrossRef" href="#bib0345"><span class="elsevierStyleSup">29</span></a> Still another study showed that in patients with temporal lobe epilepsy, visual analysis and quantitative analysis present a high correlation and again concluded that visual analysis performed by experienced evaluators could be sufficient.<a class="elsevierStyleCrossRef" href="#bib0350"><span class="elsevierStyleSup">30</span></a> On the other hand, in the case of patients with cortical dysplasia, voxel-based quantitative analysis is able to detect the epileptic focus in double the number of patients compared to visual analysis. These results were confirmed in another recent study,<a class="elsevierStyleCrossRef" href="#bib0355"><span class="elsevierStyleSup">31</span></a> that concluded that visual analysis should routinely be combined with quantitative analysis.</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0190" class="elsevierStylePara elsevierViewall">On the other hand, the prognostic value of these quantification methods has also been explored in patients undergoing surgery with the objective of knowing which patients will be seizure free. One study concluded that the quantification of asymmetries in a VOI over the thalamus can be used as a prognostic value of surgery,<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">32</span></a> with these results being later confirmed in other cohorts.<a class="elsevierStyleCrossRef" href="#bib0365"><span class="elsevierStyleSup">33</span></a> Other studies showed that in temporal lobe epilepsy, the quantification of asymmetry can predict the patients who will be seizure free after surgery.<a class="elsevierStyleCrossRefs" href="#bib0370"><span class="elsevierStyleSup">34–36</span></a></p><p id="par0195" class="elsevierStylePara elsevierViewall">In short, the quantification of <span class="elsevierStyleSup">18</span>F-FDG PET images in epilepsy is a useful tool to support visual diagnosis in patients with temporal lobe epilepsy and significantly improves the localization of the focus in cases of cortical dysplasia. In addition, quantification has shown good capacity to generate quantitative indexes with prognostic value that are able to predict which patients will be seizure free following surgery.<elsevierMultimedia ident="tbl0030"></elsevierMultimedia></p></span><span id="sec0085" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Why and when to quantify?</span><p id="par0200" class="elsevierStylePara elsevierViewall">Quantitative measures can detect small variations in the grade of uptake that may remain unseen by the human eye in visual analysis. This, together with the use of statistical analysis that allows comparison of the findings of our test with a database of control subjects, provides a tool with great potential for early and differential diagnosis. Obtaining different quantitative measures as well as indexes of asymmetry allows comparative evaluation of longitudinal studies and to determine whether a parameter is within the normal range or provides as estimation of the severity of an anomaly. Although quantification of PET studies is not essential, its application allows numerical parameters to be obtained with the objective of achieving a more reliable diagnosis that is less dependent on the experience of the physician.</p><p id="par0205" class="elsevierStylePara elsevierViewall">In clinical practice it is necessary to quantify when a visual analysis is doubtful or negative. Today, quantification is routine practice for the detection of the epileptogenic focus in drug-resistant epilepsies<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">37</span></a> and the study of neurodegenerative diseases.<a class="elsevierStyleCrossRefs" href="#bib0230"><span class="elsevierStyleSup">6,38</span></a> Quantification of the PET study of other brain diseases is, at present, restricted to investigation.</p><p id="par0210" class="elsevierStylePara elsevierViewall">Since the beginning of the PET technique, methods of image quantification have shown important developments within the setting of neurology. There are currently a large number of quantification methods, but the methodology of quantification of PET in neurology studies is not standardized.<a class="elsevierStyleCrossRef" href="#bib0395"><span class="elsevierStyleSup">39</span></a> Before starting a clinical procedure or investigative study it is necessary to evaluate the potential benefits of quantification. The possible specific application of each PET study will determine which quantification method is the most adequate; first considering whether the measure to be extracted contains the information of interest, and second, knowing which type of measures or previous studies in the literature you wish to compare with. In addition, when sophisticated quantification methods are used, it is very important to have in depth knowledge of the conditions of applicability as well as all the technical details.<a class="elsevierStyleCrossRef" href="#bib0400"><span class="elsevierStyleSup">40</span></a> Finally, it is always essential to ensure that the quantification method has been validated in order to avoid the use of suboptimal or inadequate quantification methods. Whenever possible, incorporate co-registry of the PET image with the MR image or an atlas for anatomical localization of the findings.</p><p id="par0215" class="elsevierStylePara elsevierViewall">While the quantification of PET studies is a very useful tool, it is necessary to remember that it is only a complementary tool.</p></span><span id="sec0090" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Conflict of interest</span><p id="par0220" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:12 [ 0 => array:3 [ "identificador" => "xres1444329" "titulo" => "Abstract" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0005" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1317836" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres1444330" "titulo" => "Resumen" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0010" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1317837" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Semiquantitative/quantitative analysis" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Realignment, co-registry and spatial normalization" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Methods for the quantification of F-FDG PET images" ] ] ] 6 => array:3 [ "identificador" => "sec0025" "titulo" => "Programs available" "secciones" => array:9 [ 0 => array:2 [ "identificador" => "sec0030" "titulo" => "CortexID Suite (General Electric Healthcare)" ] 1 => array:2 [ "identificador" => "sec0035" "titulo" => "Database Comparison (Siemens Healthineers)" ] 2 => array:2 [ "identificador" => "sec0040" "titulo" => "MIMneuro (MIM Software)" ] 3 => array:2 [ "identificador" => "sec0045" "titulo" => "Neurocloud-PET (Qubiotech Health Intelligence)" ] 4 => array:2 [ "identificador" => "sec0050" "titulo" => "Neurology (Hermes Medical Solutions)" ] 5 => array:2 [ "identificador" => "sec0055" "titulo" => "NeuroQ (Syntermed)" ] 6 => array:2 [ "identificador" => "sec0060" "titulo" => "NeuroSTAT (University of Utah)" ] 7 => array:2 [ "identificador" => "sec0065" "titulo" => "PETQuant (Cortechs Labs)" ] 8 => array:2 [ "identificador" => "sec0070" "titulo" => "PNEURO/PALZ (PMOD Technologies)" ] ] ] 7 => array:2 [ "identificador" => "sec0075" "titulo" => "F-FDG PET quantification in dementias" ] 8 => array:2 [ "identificador" => "sec0080" "titulo" => "F-FDG PET quantification in epilepsy" ] 9 => array:2 [ "identificador" => "sec0085" "titulo" => "Why and when to quantify?" ] 10 => array:2 [ "identificador" => "sec0090" "titulo" => "Conflict of interest" ] 11 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2019-11-24" "fechaAceptado" => "2020-03-03" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1317836" "palabras" => array:6 [ 0 => "Quantification" 1 => "PET" 2 => "<span class="elsevierStyleSup">18</span>F-FDG" 3 => "Dementia" 4 => "Epilepsy" 5 => "Brain" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1317837" "palabras" => array:6 [ 0 => "Cuantificación" 1 => "PET" 2 => "<span class="elsevierStyleSup">18</span>F-FDG" 3 => "Demencia" 4 => "Epilepsia" 5 => "Cerebro" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">The inclusion of <span class="elsevierStyleSup">18</span>F-FDG PET as a biomarker in the diagnostic criteria of neurodegenerative diseases and its indication in the presurgical assessment for drug-resistant epilepsies allow to improve specificity of these diagnosis. The traditional interpretation of neurological PET studies has been performed qualitatively, although in the last decade, several quantitative evaluation methods have emerged. This technical development has become relevant in clinical practice, improving specificity, reproducibility and reducing the interrater reliability derived from visual analysis. In this article we update/review the main imaging processing techniques currently used. This may allow the Nuclear Medicine physician to know their advantages and disadvantages when including these procedures in daily clinical practice.</p></span>" ] "es" => array:2 [ "titulo" => "Resumen" "resumen" => "<span id="abst0010" class="elsevierStyleSection elsevierViewall"><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">La inclusión de la PET <span class="elsevierStyleSup">18</span>F-FDG como biomarcador en los criterios de diagnóstico clínico de enfermedades neurodegenerativas y su indicación en el estudio precirugía en la epilepsia resistente a los fármacos permiten mejorar la especificidad del diagnóstico. La interpretación clásica de los estudios PET neurológicos se ha abordado de forma cualitativa, aunque en la última década hemos sido testigos del auge en los sistemas de evaluación cuantitativa. Este desarrollo técnico es de vital importancia en la práctica clínica, ya que mejora la especificidad y la reproducibilidad y reduce el efecto dependiente del observador derivado del análisis visual. Consideramos que es conveniente exponer la complejidad de las técnicas de procesamiento de imagen empleadas, lo que permitirá al especialista en Medicina Nuclear conocer sus ventajas e inconvenientes a la hora de incluirlas en la práctica clínica diaria.</p></span>" ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Please cite this article as: Niñerola-Baizán A, Aguiar P, Cabrera-Martín MN, Vigil C, Gómez-Grande A, Lorenzo C, et al. Relevancia de la cuantificación en los estudios PET cerebrales con <span class="elsevierStyleSup">18</span>F-FDG. Rev Esp Med Nucl Imagen Mol. 2020;39:181–189.</p>" ] ] "multimedia" => array:10 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1207 "Ancho" => 2508 "Tamanyo" => 430317 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Mathematical transformations involved in the methods of PET quantification. Matrix of pixels of the original image (blue) and matrix of pixels of the image transformed by rotation, translation, resampling, shear and warping (red).</p>" ] ] 1 => array:7 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1444 "Ancho" => 2508 "Tamanyo" => 296300 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">(A) VOI-based method: co-registry of the PET image with the MR of the patient (C), normalization of the MR of the patient on the MNI space (N) and inverse normalization of the atlas (N<span class="elsevierStyleSup">−1</span>) that is adjusted on the cPET image. (B) Voxel-based method: apply the N normalization on the cPET image to obtain nPET and independently estimate the N<span class="elsevierStyleInf">i</span> normalization of each PET<span class="elsevierStyleInf">i</span> (nc subjects).</p>" ] ] 2 => array:7 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1318 "Ancho" => 1505 "Tamanyo" => 256684 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Fusion <span class="elsevierStyleSup">18</span>F-FDG PET with MR in a patient with temporal lobe epilepsy showing a map of significant differences compared to a database of normal images obtained by voxel-based quantification.</p>" ] ] 3 => array:8 [ "identificador" => "tbl0035" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "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"><th 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" scope="col" style="border-bottom: 2px solid black">Program \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Type of analysis \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Databases \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">Installation \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">License \t\t\t\t\t\t\n \t\t\t\t\t\t</th><th 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" scope="col" style="border-bottom: 2px solid black">CE marking \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">CortexID Suite \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Voxel (3D-SSP) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Separated into 4 age groups \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Application for General Electric equipment \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Pay for license \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Database comparison \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">VOIs/voxel \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">7 databases \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Application for Siemens equipment \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Pay for license \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">MIMneuro \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">VOIs/voxel \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Separated into 5 age groups \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">PC \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Pay for license \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Neurocloud-PET \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">VOIs/voxel \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Online platform \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Pay for analysis \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Neurology \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">VOIs/voxel \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">PC \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Pay for license \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NeuroQ \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">VOIs \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">PC and application for Philips equipment \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Pay for license \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">NeuroSTAT \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Voxel (3D-SSP) \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">PC \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Free \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">No \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">PETQuant \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">VOIs \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Yes \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">PC/Online platform \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Pay for license \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">No \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">PNEURO/PALZ \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">VOIs/voxel \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">No/Yes \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">PC \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Pay for license \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="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">No \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2484175.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Characteristics of the programs.</p>" ] ] 4 => array:5 [ "identificador" => "tbl0005" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => false "mostrarDisplay" => true "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=""><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleBold">Key point 1</span>Quantification of PET images requires combining the functional image with an MR image that provides structural information and facilitates the localization of the brain regions to be analyzed. To do this several types of techniques are available according to the characteristics of the images to be processed: realignment, co-registry and spatial normalization. \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2484173.png" ] ] ] ] ] 5 => array:5 [ "identificador" => "tbl0010" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => false "mostrarDisplay" => true "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=""><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleBold">Key point 2</span>Image analysis methods can be classifed as:a. VOI-based quantification methods which are focused on the estimation of the average of previously defined concentrations of the radiopharmaceutical in different regions of the brain.b. Voxel-based quantification methods which are based on statistical comparison the PET image of the patient and a group of PET images of control subjects. \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2484174.png" ] ] ] ] ] 6 => array:5 [ "identificador" => "tbl0015" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => false "mostrarDisplay" => true "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=""><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleBold">Key point 3</span>There is a wide spectrum of programs for the quantification of PET images. To choose a program it is necessary to establish that it has previously been validated and in depth evaluation of its characteristics should be made. \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2484176.png" ] ] ] ] ] 7 => array:5 [ "identificador" => "tbl0020" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => false "mostrarDisplay" => true "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=""><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleBold">Key point 4</span>Clinical interpretation of the quantitative results is essential since quantification methods are not automatic but rather are a complement to visual analysis. \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2484179.png" ] ] ] ] ] 8 => array:5 [ "identificador" => "tbl0025" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => false "mostrarDisplay" => true "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=""><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleBold">Key point 5</span>The quantification of images in dementias increases the diagnostic yield of <span class="elsevierStyleSup">18</span>F-FDG PET mainly due to the greater specificity compared to visual analysis as well as an increase in the level of confidence of nuclear physicians in their diagnosis. \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2484178.png" ] ] ] ] ] 9 => array:5 [ "identificador" => "tbl0030" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => false "mostrarDisplay" => true "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=""><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><span class="elsevierStyleBold">Key point 6</span>Quantification of <span class="elsevierStyleSup">18</span>F-FDG PET images in epilepsy significantly increases the localization of the focus in patients with cortical dysplasia. On the other hand, it is a useful tool to support visual diagnosis in patients with temporal lobe epilepsy. \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2484177.png" ] ] ] ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:40 [ 0 => array:3 [ "identificador" => "bib0205" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Recommendations for the use of PET imaging biomarkers in the diagnosis of neurodegenerative conditions associated with dementia: SEMNIM and SEN consensus" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "J. Arbizu" 1 => "G. García-Ribas" 2 => "I. Carrió" 3 => "P. Garrastachu" 4 => "P. Martínez-Lage" 5 => "J.L. 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Continuing Education
Relevance of quantification in brain PET studies with 18F-FDG
Relevancia de la cuantificación en los estudios PET cerebrales con 18F-FDG
A. Niñerola-Baizána,b, P. Aguiarc,d, M.N. Cabrera-Martíne, C. Vigilf,
, A. Gómez-Grandeg, C. Lorenzoh, S. Rubíi, P. Sopenaj,k, V. Camachol, por el Grupo de Neuroimagen SEMNIM
Corresponding author
a Servicio de Medicina Nuclear, Hospital Clínic, Barcelona, Spain
b Grupo de Imagen Biomédica, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain
c Grupo de Imaxe Molecular e Física Médica, Departamento de Radioloxía, Facultade de Medicina, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
d Servicio de Medicina Nuclear, Hospital Clínico de Santiago de Compostela, Instituto de Investigación Sanitaria de Santiago, Santiago de Compostela, Spain
e Servicio de Medicina Nuclear, Hospital Clínico San Carlos, Madrid, Spain
f Servicio Medicina Nuclear, Hospital Universitario Central de Asturias, Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain
g Servicio de Medicina Nuclear, Hospital Universitario 12 de Octubre, Madrid, Spain
h Servicio de Medicina Nuclear, Hospital Universitari Vall d’Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
i Servicio de Medicina Nuclear, Hospital Universitari Son Espases, Institut d’Investigació Sanitària Illes Balears (IdISBa), Palma, Spain
j Servicio de Medicina Nuclear, Hospital Vithas-Nisa 9 de Octubre, Valencia, Spain
k Servicio de Medicina Nuclear, Hospital Universitario y Politécnico La Fe, Valencia, Spain
l Servicio de Medicina Nuclear, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
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