Contributions to the quantitative analysis of dynamic PET studies using clustering approaches

Mención Internacional en el título de doctorDynamic positron emission tomography (PET) is a widespread medical imaging technique that allows the quantification of different physiological parameters within the body and yields more information that the one provided by a single, static image. Quantification of these studies involves obtaining the input function, that is, the amount of tracer present in arterial blood at any given point in time, and the tissue time-activity curve (TAC) for the tissue or organ under study. The subjacent biological processes are modelled as the tracer exchange rates between the arterial activity source and a compartmental model; this mathematical approach allows to quantify different biological aspects (metabolic rates, blood flow, specific receptor binding) in a non-invasive way. Typically, arterial and tissue TACs are extracted from the image data by drawing a ROI over the areas of interest, either over the PET image or over some anatomical imaging modality, such as CT, and in some cases acquire some blood samples to correct the input function for metabolites, partial volume effects or other different sources of distortion that may bias the final result. While this ROI delineation is done normally by an experienced operator, this process is very slow and, more importantly, subjective and non-replicable. Furthermore, ROI delineation over registered anatomical images may group together regions that look identical in the CT image but have different underlying kinetics. These reasons have motivated the development of automatic segmentation or TAC extraction algorithms, of which there are several examples in the medical imaging literature. Most of the proposed methods involve the use of unsupervised machine learning algorithms or the direct application of dimensionality reduction techniques, such as PCA or SVD. This thesis studies the feasibility of supervised algorithms to extract the activity curves of dynamic studies based solely on the knowledge acquired about the kinetics of similar ones. Our experiments on three swine studies showed that the segmentation was successful and the obtained TACs allowed the computation of the kinetic analysis and obtained smaller errors in the kinetic parameters obtained from the mathematical model than the manual segmentations. Said supervised algorithms are not common in the literature but we have shown that they can be a viable option for very specific subset of cases. One of the problems of the published automatic segmentation algorithms is the general lack of published source codes or even binary distributions. As has been studied in the literature, this presents a problem by itself, as it forces other researchers to re-implement said algorithms. This work presents the development of an open framework for dynamic imaging clustering that includes the most commonly used algorithms and that can be easily extended by third parties through the use of its public API. The code for said framework has been published with a free software license to allow it to be modified by external researchers and adapt it to their needs. It has been developed as an ImageJ plugin to take advantage to all the imaging analysis functionalities already presented in said platform. Using this framework, we also present an improvement of the classical leader-follower algorithm. This unsupervised algorithm groups image voxels with similar TACs according to a threshold set by the user and creates as many clusters as necessary to form homogeneous regions. Due to the nature of the partial volume distortions that need to be removed from the final TACs as much as possible, the proposed method implements a two-step leader-follower modification. In this case, the image voxels are clustered according to both a similarity metric and a distance metric; particularly, the cosine similarity and the Euclidean distance were chosen for our tests. This algorithm successfully segmented all of the evaluated 24 mice imaging studies, yielding quantitative parameters after the kinetic modelling that were not significantly different from those obtained via manual delineation and maintained the differences between the three tracers used in this experiment. --------------------------------------------------------La tomografía por emisión de positrones (PET) es una técnica de imagen médica ampliamente utilizada que permite la cuantificación de diferentes parámetros fisiológicos dentro del cuerpo y arroja más información que la que puede obtenerse mediante una única imagen estática. La cuantificación de estos estudios necesita la obtención de la función de entrada, esto es, la cantidad de trazador presente en sangre arterial a lo largo del tiempo, y la curva de actividad (TAC) del tejido u órgano bajo estudio. Los procesos biológicos subyacentes se modelan como las velocidades de intercambio de trazador entre la fuente de actividad arterial y un modelo compartimental; esta aproximación matemática permite cuantificar diferentes aspectos biológicos (metabolismo, flujo sanguíneo, fijación a receptores específicos) de una forma no invasiva. Típicamente, la función de entrada y la TAC de los tejidos se extraen directamente de la imagen mediante el trazado de una región de interés (ROI), bien sobre la imagen PET directamente o sobre alguna modalidad de imagen que presente información anatómica, como el CT, y en algunos casos requiere la obtención de muestras de sangre para corregir en la función de entrada el efecto de metabolitos, efectos de volumen parcial u otras fuentes de distorsión que pueden sesgar el resultado final. Aunque este proceso de delineación lo realiza habitualmente un operador experimentado, este proceso es lento, subjetivo y no replicable. Además, la delineación de ROIs sobre imágenes anatómicas registradas puede agrupar regiones que aparecen idénticas en la imagen de CT pero tienen diferentes comportamientos cinéticos. Estas razones han motivado el desarrollo de algoritmos de segmentación automática o extracción de TAC, de los cuales hay múltiples ejemplos en la literatura de imagen médica. La mayoría de los métodos propuestos son implementaciones de algoritmos de unsupervised machine learning, o aprendizaje máquina no supervisado, o la aplicación directa de técnicas de reducción de dimensionalidad, como análisis de componentes principales (PCA) o descomposición en valores singulares (SVD). Esta tesis doctoral estudia la posibilidad de emplear algoritmos supervisados para extraer las curvas de actividad de estudios dinámicos basándose únicamente en el conocimiento adquirido en la cinética de estudios similares. La experimentación con tres estudios porcinos mostró que la obtención de las TACs fue exitosa, y estos datos permitieron el cálculo de los parámetros cinéticos, obteniendo errores en el ajuste matemático menores que los obtenidos mediante una segmentación manual. Este tipo de algoritmos supervisados no son comunes en la literature pero hemos demostrado que pueden ser una opción viable para un subconjunto de casos específico. Uno de los problemas de los algoritmos de segmentación automática publicados en la literatura es la carencia general de código fuente o incluso distribuciones binarias. Como ya se ha estudiado en la literature, esto presenta un problema, al forzar a investigadores de otras instituciones a reimplementar dichos algoritmos. Este trabajo presenta un marco de desarrollo para algoritmos de clustering aplicados a imagen médica dinámica que incluye los algoritmos más comúnmente utilizados y que puede ser extendido fácilmente mediante terceros a través del uso de su interfaz de programación (API) pública. El código para dicho marco de desarrollo ha sido publicado con una licencia libre para permitir su modificación por investigadores externos y su adaptación a sus necesidades. Se ha programado como un plugin de la plataforma de análisis de imagen ImageJ para aprovechar todas las ventajas y funcionalidades de análisis ya presentes en dicha plataforma. Empleando este marco de desarrollo, finalmente presentamos una mejora sobre un algoritmo clásico leader-follower. Este algoritmo no supervisado agrupa vóxeles de la imagen con TACs similares de acuerdo a un umbral establecido por el usuario, y crea tantos clusters, o grupos, necesaarios para formar regiones homogéneas. Debido a los efectos de volumen parcial, que deben ser eliminados de las TACs finales lo máximo posible, el método propuesto implementa una modificación del leader-follower en dos pasos. En este caso, los vóxeles de la imagen se agrupan de acuerdo a una métrica de similitud (coseno) y una métrica de distancia (Euclídea). El algoritmo segmentó con éxito 24 imágenes dinámicas de ratón, ofreciendo parámetros cuantitativos tras el modelado cinético que no fueron diferentes de forma significativa de los obtenidos a través de la delineación manual y manteniendo las diferencias observadas entre los tres trazadores empleados en este experimento.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: María Jesús Ledesma Carbayo; Secretario: Jorge Ripoll Lorenzo; Vocal: Stephen L. Bacharac

Identification of Key Molecules Involved in the Protection of Vultures Against Pathogens and Toxins

This is an open access article distributed under the terms of the Creative Commons Attribution License.This work was supported by the Junta de Comunidades de Castilla-La Mancha (JCCM), project PII1I09-0243-4350.Peer Reviewe

Segmentación automática de estudios PET cardíacos con ¹³NH_3 basada en correlación iterativa

Actas de: XXVIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica (CASEIB 2010). Madrid, 24-26 de noviembre de 2010.La obtención de la función de entrada en estudios dinámicos de corazón a partir de la imagen PET se realiza habitualmente mediante la selección previa de una región de interés (ROI) o utilizando procedimientos de análisis factorial para encontrar aquellas curvas actividad/tiempo que mejor se adaptan a la función de entrada. En este trabajo se presenta un método novedoso de segmentación automática y obtención de la función de entrada que utiliza mapas de correlación calculados sobre estudios dinámicos que emplean ¹³NH_3 como trazador. Partiendo de un modelo analítico inicial, se buscan las curvas temporales más parecidas en el estudio real empleando la correlación. Tomando como datos estas curvas se calculan nuevos modelos con los que realizar sucesivas iteraciones. El resultado final es tanto una segmentación automática como la curva de actividad/tiempo de cada región segmentada.Ministerio de Ciencia e Innovación, TEC2007-64731, TEC 2008-06715-C02-1, la RETIC-RECAVA del Ministerio de Sanidad y Consumo, y el programa ARTEMIS S2009/DPI-1802 de la Comunidad de Madrid.Publicad

jClustering, an open framework for the development of 4D clustering algorithms

We present jClustering, an open framework for the design of clustering algorithms in dynamic medical imaging. We developed this tool because of the difficulty involved in manually segmenting dynamic PET images and the lack of availability of source code for published segmentation algorithms. Providing an easily extensible open tool encourages publication of source code to facilitate the process of comparing algorithms and provide interested third parties with the opportunity to review code. The internal structure of the framework allows an external developer to implement new algorithms easily and quickly, focusing only on the particulars of the method being implemented and not on image data handling and preprocessing. This tool has been coded in Java and is presented as an ImageJ plugin in order to take advantage of all the functionalities offered by this imaging analysis platform. Both binary packages and source code have been published, the latter under a free software license (GNU General Public License) to allow modification if necessary.Funded by TEC2011-28972-C02-01, Ministerio de Ciencia e Innovación, CEN-20101014; Programa CENIT-CDTI, Ministerio de Ciencia e Innovación; S2009/DPI-1802 (ARTEMIS), Comunidad de Madrid. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Publicad

Iterative Automatic Segmentation in cardiac PET based on TAC correlation: preliminary results

Proceeding of: 2010 IEEE Nuclear Science Symposium, Medical Imaging Conference and 17th Room Temperature Semiconductor Detector Workshop (IEEE), Knoxville, Tennessee, USA, October 30 - November 6, 2010Conventional kinetic parameter estimation based on compartmental models requires an accurate estimation of arterial blood input function. To avoid invasive blood sampling, an image-derived input function can be obtained by manually defining a Region of Interest. Here we propose a new and simple, iterative method for automatic segmentation and input function calculation of PET cardiac studies using correlation as a distance metric between a priori information regarding the approximate shape of the final time-activity curve (TAC) and the actual TAC extracted from the image temporal series.This work was supported in part by the CENIT-AMIT Ingenio 2010, Ministerio de Ciencia e Innovación, TEC2007-64731, TEC 2008-06715-C02-1, RETIC-RECAVA, Ministerio de Sanidad y Consumo, and the ARTEMIS de la Comunidad de Madrid (S2009/DPI-1802) programsPublicad

Automatic TAC extraction from dynamic cardiac PET imaging using iterative correlation from a population template

This work describes a new iterative method for extracting time-activity curves (TAC) from dynamic imaging studies using a priori information from generic models obtained from TAC templates. Analytical expressions of the TAC templates were derived from TACs obtained by manual segmentation of three 13NH3 pig studies (gold standard). An iterative method for extracting both ventricular and myocardial TACs using models of the curves obtained as an initial template was then implemented and tested. These TACs were extracted from masked and unmasked images; masking was applied to remove the lungs and surrounding non-relevant structures. The resulting TACs were then compared with TACs obtained manually; the results of kinetic analysis were also compared. Extraction of TACs for each region was sensitive to the presence of other organs (e.g., lungs) in the image. Masking the volume of interest noticeably reduces error. The proposed method yields good results in terms of TAC definition and kinetic parameter estimation, even when the initial TAC templates do not accurately match specific tracer kinetics.This work is supported by the following grants: RD07/0014/2009, Subprograma RETICS, Ministerio de Ciencia e Innovación. S2009/DPI-1802 (ARTEMIS), Comunidad de Madrid. CEN-20101014, Programa CENIT, CDTI, Ministerio de Ciencia e Innovación. European Commission, EFPIA, INNOVATIVE MEDICINE INITIATIVE (PredDICT-TB project, 115337-1)Publicad

Changes in depression and suicidal ideation under severe lockdown restrictions during the first wave of the COVID-19 pandemic in Spain: a longitudinal study in the general population - RETRACTION

El texto del artículo original se encuentra disponible en http://hdl.handle.net/10486/70204

Development and validation of an open source quantification tool for DSC-MRI studies

Motivation. This work presents the development of an open source tool for the quantification of dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies. The development of this tool is motivated by the lack of open source tools implemented on open platforms to allow external developers to implement their own quantification methods easily and without the need of paying for a development license.Materials and methods. This quantification tool was developed as a plugin for the ImageJ image analysis platform using the Java programming language. A modular approach was used in the implementation of the components, in such a way that the addition of new methods can be done without breaking any of the existing functionalities. For the validation process, images from seven patients with brain tumors were acquired and quantified with the presented tool and with a widely used clinical software package. The resulting perfusion parameters were then compared.Results. Perfusion parameters and the corresponding parametric images were obtained. When no gamma-fitting is used, an excellent agreement with the tool used as a gold-standard was obtained (R²>0.8 and values are within 95% CI limits in Bland–Altman plots).Conclusion. An open source tool that performs quantification of perfusion studies using magnetic resonance imaging has been developed and validated using a clinical software package. It works as an ImageJ plugin and the source code has been published with an open source license.This work was partially supported by the Human Frontier Science Program (Research Grant 2013).Publicad