120 research outputs found

    Small animal PET imaging using GATE Monte Carlo simulations : Implementation of physiological and metabolic information

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    Tese de doutoramento, (Engenharia Biomédica e Biofísica), Universidade de Lisboa, Faculdade de Ciências, 2010O rato/ratinho de laboratório é o modelo animal de escolha para o estudo dos processos fundamentais associados a determinadas patologias, como o cancro. Esta escolha deve-se a uma gama de factores que incluem uma grande homologia genética com o Homem. Assim sendo o rato/ratinho é amplamente utilizado em laboratórios por todo o Mundo para estudo dos processos celulares básicos associados á doença e à terapia. A comunidade laboratorial tem, nos últimos anos, desenvolvido um grande interesse pela imagiologia não-invasiva destes animais. De entre as diversas tecnologias de imagem aplicadas aos estudosin vivo de pequenos animais, a Tomografia por Emissão de Positrões (PET) permite obter informação sobre a distribuição espacial e temporal de moléculas marcadas com átomo emissor de positrões, de forma não invasiva. Os traçadores utilizados para obter esta “imagem molecular” são administrados em baixas quantidades, de tal forma que os processos biológicos que envolvem concentrações da ordem do nano molar, ou mesmo inferiores, podem ser determinadas sem perturbar o processo em estudo. Muitas combinações de diferentes moléculas com diferentes radionúclidos permitem traçar uma gama de caminhos moleculares específicos (e.g. processos biológicos de receptores e síntese de transmissores em caminhos de comunicação em células, processos metabólicos e expressão genética). A imagem pode ser executada repetidamente antes e depois de intervenções permitindo o uso de cada animal como o seu próprio controlo biológico. A investigação já realizada em curso que aplicam a PET ao estudos de pequenos animais, tem permitido compreender, entre outras coisas, a evolução de determinadas doenças e suas potenciais terapias. Contudo, existem algumas dificuldades de implementação desta técnica já que a informação obtida está condicionada pelos fenómenos físicos associados à interacção da radiação com a matéria, pelos instrumentos envolvidos na obtenção da informação e pela própria fisiologia do animal (por exemplo o seu movimento fisiológico). De facto, a fiabilidade da quantificação das imagens obtidas experimentalmente, em sistemas PET dedicados aos pequenos animais, é afectada ao mesmo tempo pelos limites de desempenho dos detectores (resolução espacial e em energia, sensibilidade, etc.), os efeitos físicos como a atenuação e a dispersão, que perturbam a reconstrução da imagem, e os efeitos fisiológicos (movimentos do animal). Na prática estes efeitos são corrigidos com métodos de correcção específicos com a finalidade de extrair parâmetros quantitativos fiáveis. Por outro lado, as características fisiológicas dos animais a estudar e a necessidade da existência de animais disponíveis, são factores adicionais de complexidade. Recentemente, tem sido dedicada alguma atenção aos efeitos resultantes dos movimentos fisiológicos, nomeadamente do movimento respiratório, na qualidade das imagens obtidas no decurso de um exame PET. Em particular, no caso do estudo dos tumores do pulmão (algo infelizmente muito frequente em humanos), o movimento fisiológico dos pulmões é uma fonte de degradação das imagens PET, podendo comprometer a sua resolução e o contraste entre regiões sãs e doentes deste orgão. A precisão quantitativa na determinação da concentração de actividade e dos volumes funcionais fica assim debilitada, sendo por vezes impedida a localização, detecção e quantificação do radiotraçador captado nas lesões pulmonares. De modo a conseguir diminuir estes efeitos, existe a necessidade de melhor compreender a influência deste movimento nos resultados PET. Neste contexto, as simulações Monte Carlo são um instrumento útil e eficaz de ajuda à optimização dos componentes dos detectores existentes, à concepção de novos detectores, ao desenvolviBaseados em modelos matemáticos dos processos físicos, químicos e, sempre que possível, biológicos, os métodos de simulação Monte Carlo são, desde há muito, uma ferramenta privilegiada para a obtenção de informação fiável da previsão do comportamento de sistemas complexos e por maioria de razão, para uma sua melhor compreensão. No contexto da Imagiologia Molecular, a plataforma de simulação Geant4 Application for Tomographic Emission (GATE), validada para as técnicas de imagem de Medicina Nuclear, permite a simulação por Monte Carlo dos processos de obtenção de imagem. Esta simulação pode mesmo ser feita quando se pretende estudar a distribuição de emissores de positrões cuja localização varia ao longo do tempo. Adicionalmente, estas plataformas permitem a utilização de modelos computacionais para modelar a anatomia e a fisiologia dos organismos em estudo mediante a utilização de uma sua representação digital realista denominada de fantôma. A grande vantagem na utilização destes fantômas relaciona-se com o facto de conhecermos as suas características geométricas (“anatómicas”) e de podermos controlar as suas características funcionais (“fisiológicas”). Podemos assim obter padrões a partir dos quais podemos avaliar e aumentar a qualidade dos equipamentos e técnicas de imagem. O objectivo do presente trabalho consiste na modelação e validação de uma plataforma de simulação do sistema microPET® FOCUS 220, usado em estudos de PET para pequenos animais, utilizando a plataforma de simulação GATE. A metodologia adoptada procurou reproduzir de uma forma realista, o ambiente de radiação e factores instrumentais relacionados com o sistema de imagem, assim como o formato digital dos dados produzidos pelo equipamento. Foram usados modelos computacionais, obtidos por segmentação de imagem de exames reais, para a avaliação da quantificação das imagens obtidas. Os resultados obtidos indicam que a plataforma produz resultados reprodutíveis, adequados para a sua utilização de estudos de pequenos animais em PET. Este objectivo foi concretizado estudando os efeitos combinados do tamanho das lesões, do rácio de concentração de actividade lesão-para-fundo e do movimento respiratório na recuperação de sinal de lesões esféricas localizadas no pulmão em imagens PET de pequenos animais. Para este efeito, foi implementada no código GATE uma representação digital em 4D de um ratinho de corpo inteiro (o fantôma MOBY). O MOBY permitiu reproduzir uma condição fisiológica que representa a respiração em condição de "stress", durante um exame típico de PET pequeno animal, e a inclusão de uma lesão esférica no pulmão tendo em conta o movimento da mesma. Foram realizadas um conjunto de simulações estáticas e dinâmicas usando 2-Deoxy-[18F]fluoro-D-glucose (FDG) tendo em consideração diferentes tamanhos das lesões e diferentes captações deste radiofármaco. O ruído da imagem e a resolução temporal foram determinadas usando imagens 3D e 4D. O rácio sínal-para-ruído (SNR), o rácio contraste-para-ruído (CNR), a relação lesão-fundo (target-to-background activity concentration ratio- TBR), a recuperação de contraste (CR) e a recuperação de volume (VR) foram também avaliados em função do tamanho da lesão e da actividade captada. Globalmente, os resultados obtidos demonstram que a perda de sinal depende tanto do tamanho da lesão como da captação de actividade na lesão. Nas simulações estáticas, onde não foi simulado movimento, os coeficientes de recuperação foram influenciados pelo efeito de volume parcial para os tamanhos mais reduzidos de lesão. Além disso, o aumento do contraste na lesão produz um aumento significativo no desvio padrão da média de sinal recuperado resultando numa diminuição no CNR e no SNR. Também concluímos que o movimento respiratório diminui significativamente a recuperação do sinal e que esta perda depende principalmente do tamanho da lesão. A melhor resolução temporal e resolução espacial foram obtidas nas simulações estáticas, onde não existia movimento envolvido. Os resultados simulados mostram que o efeito de volume parcial é dominante nas lesões mais pequenas devido à resolução espacial do sistema FOCUS, tanto nas imagens estáticas como nas dinâmicas. Além disso, para concentrações baixas de radiofármaco existe uma dificuldade inerente em quantificar a recuperação de sinal nas lesões comprometendo a análise quantitativa dos dados obtidos.Organ motion has become of great concern in medical imaging only recently. Respiratory motion is one source of degradation of PET images. Respiratory motion may lead to image blurring, which may result in reduced contrast and quantitative accuracy in terms of recovered activity concentration and functional volumes. Consequently, the motion of lungs hinders the localization, detection, and the quantification of tracer uptake in lung lesions. There is, therefore, a need to better understand the effects of this motion on PET data outcome. Medical imaging methods and devices are commonly evaluated through computer simulation. Computer generated phantoms are used to model patient anatomy and physiology, as well as the imaging process itself. A major advantage of using computer generated phantoms in simulation studies is that the anatomy and physiological functions of the phantom are known, thus providing a gold standard from which to evaluate and improve medical imaging devices and techniques. In this thesis, are presented the results of a research studied the combined effects of lesion size, lesion-to-background activity concentration ratio and respiratory motion on signal recovery of spherical lesions in small animal PET images using Monte Carlo simulation. Moreover, background activity is unavoidable and it causes significant noise and contrast loss in PET images. For these purposes, has been used the Geant4 Application for Tomographic Emission (GATE) Monte Carlo platform to model the microPET®FOCUS 220 system. Additionaly, was implemented the digital 4D Mouse Whole-Body (MOBY) phantom into GATE. A physiological “stress breathing” condition was created for MOBY in order to reproduce the respiratory mouse motion during a typical PET examination. A spherical lung lesion was implemented within this phantom and its motion also modelled. Over a complete respiratory cycle of 0.37 s was retrieved a set of 10 temporal frames (including the lesion movement) generated in addition to a non-gated data set. Sets of static (non-gated data) and dynamic (gated data) 2-Deoxy-[18F]fluoro-D-glucose (FDG) simulations were performed considering different lesion sizes and different activity uptakes. Image noise and temporal resolution were determined on 3D and 4D images. Signal-to-Noise Ratio (SNR), Contrast-to-Noise Ratio (CNR), Target-to-Background activity concentration Ratio (TBR), Contrast Recovery (CR) and Volume Recovery (VR) were also evaluated as a function of lesion size and activity uptake. Globally, the results obtained show that signal loss depends both on lesion size and lesion activity uptake. In the non-gated data, where was no motion included (perfect motion correction), the recovery coefficients were influenced by the partial volume effect for the smallest lesion size. Moreover, the increased lesion contrast produces a significant increase on the standard deviation of the mean signal recover. This led to a decrease in CNR and SNR. In addition, respiratory motion significantly deteriorates signal recovery and this loss depends mainly of the lesion size. Best temporal resolution (volume recovery) and spatial resolution was given by the non-gated data, where no motion is involved. The simulated results show that the partial volume effect is dominant for small objects due to limited FOCUS system resolution in both 3D and 4D PET images. In addition, lower activity concentrations significantly deteriorates the lesion signal recovery compromising quantitative analysis.Fundação para a Ciência e a Tecnologia (FCT) under grant nº SFRH/BD/22723/200

    Modélisation pharmacocinétique en tomographie d'émission par positrons en utilisant la technique des ondelettes

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    Dans le cadre de ce travail de recherche, les objectifs étaient de mettre en oeuvre et de valider la technique des ondelettes dans la modélisation pharmacocinétique chez le rat par tomographie d'émission par positrons (TEP). En TEP, le métabolisme du glucose dans l'organe étudié est mesuré en injectant un analogue du glucose, le fluorodéoxyglucose ([indice supérieur 18]FDG). La quantité de radioactivité injectée est mesurée dans le plasma sanguin en fonction du temps et constitue la courbe d'entrée, tandis que la radioactivité mesurée dans les tissus à l'aide de la TEP constitue la réponse des tissus. Avec la courbe d'entrée et l'intensité de la radioactivité dans les tissus telle que mesurée par le tomographe, le métabolisme du glucose est calculé à l'aide d'un modèle mathématique compartimental. Ce calcul se fait habituellement sur des images reconstruites filtrées ou itérées. Cependant, ces images filtrées ont perdu la résolution spatiale ou contiennent encore du bruit dû à la faible dose de radioactivité injectée ou le temps restreint de la mesure. Dans ce travail, nous proposons la technique des ondelettes basée sur des algorithmes de compression et de filtrage qui s'avèrent performants et faciles à utiliser. De plus, à partir des images filtrées et compressées par les ondelettes, nous calculons le métabolisme du glucose pixel par pixel, afin de générer une image appelée l'image paramétrique qui permet une visualisation du métabolisme du glucose dans les différentes structures d'un organe. Nous avons appliqué la technique des ondelettes autant sur les images que sur les projections, c'est-à-dire directement sur les matrices de projections avant de reconstruire les images pour éviter le filtrage des mesures et les opérations de reconstruction. Les ondelettes ont l'avantage de réduire les matrices et de grouper les intensités des pixels, procurant une meilleure statistique, donc plus de précision, et par conséquent une meilleure qualité des images paramétriques. La technique des ondelettes a été introduite également pour la correction du volume partiel en imagerie TEP. L'effet du volume partiel survient lorsque la radioactivité des structures, dont la taille est inférieure à la résolution spatiale du tomographe, est sous-estimée. La méthode des ondelettes continues représente une alternative aux méthodes habituellement utilisées, basées sur les informations anatomiques qui proviennent de l'imagerie par résonance magnétique (IRM) ou de tomodensitométrie (TDM). L'approche des ondelettes continues consiste à caractériser les différentes structures par le couple échelle et position. En utilisant ces informations fournies par les ondelettes, toutes les intensités sous-estimées des petites structures sont rehaussées, ce qui permet d'améliorer la détection des lésions et des tumeurs en imagerie TEP. En conclusion, le travail de cette thèse démontre l'avantage de l'utilisation des ondelettes dans le calcul des paramètres physiologiques à partir des images et des sinogrammes TEP mesurés avec le [indice supérieur 18]FDG chez le rat. Enfin, les résultats obtenus sur les images avec les ondelettes ont montré moins de variation, moins de bruit tout en préservant la résolution spatiale. L'application de la transformée en ondelettes continues dans la correction de l'effet du volume partiel pour les images TEP en utilisant l'ondelette appropriée a montré le potentiel des ondelettes pour localiser les différentes structures permettant une bonne correction et une meilleure qualité d'image

    NiftyPET: A high-throughput software platform for high quantitative accuracy and precision PET imaging and analysis

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    We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coeffi- cient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data

    Improving Quantification in Lung PET/CT for the Evaluation of Disease Progression and Treatment Effectiveness

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    Positron Emission Tomography (PET) allows imaging of functional processes in vivo by measuring the distribution of an administered radiotracer. Whilst one of its main uses is directed towards lung cancer, there is an increased interest in diffuse lung diseases, for which the incidences rise every year, mainly due to environmental reasons and population ageing. However, PET acquisitions in the lung are particularly challenging due to several effects, including the inevitable cardiac and respiratory motion and the loss of spatial resolution due to low density, causing increased positron range. This thesis will focus on Idiopathic Pulmonary Fibrosis (IPF), a disease whose aetiology is poorly understood while patient survival is limited to a few years only. Contrary to lung tumours, this diffuse lung disease modifies the lung architecture more globally. The changes result in small structures with varying densities. Previous work has developed data analysis techniques addressing some of the challenges of imaging patients with IPF. However, robust reconstruction techniques are still necessary to obtain quantitative measures for such data, where it should be beneficial to exploit recent advances in PET scanner hardware such as Time of Flight (TOF) and respiratory motion monitoring. Firstly, positron range in the lung will be discussed, evaluating its effect in density-varying media, such as fibrotic lung. Secondly, the general effect of using incorrect attenuation data in lung PET reconstructions will be assessed. The study will compare TOF and non-TOF reconstructions and quantify the local and global artefacts created by data inconsistencies and respiratory motion. Then, motion compensation will be addressed by proposing a method which takes into account the changes of density and activity in the lungs during the respiration, via the estimation of the volume changes using the deformation fields. The method is evaluated on late time frame PET acquisitions using ¹⁸F-FDG where the radiotracer distribution has stabilised. It is then used as the basis for a method for motion compensation of the early time frames (starting with the administration of the radiotracer), leading to a technique that could be used for motion compensation of kinetic measures. Preliminary results are provided for kinetic parameters extracted from short dynamic data using ¹⁸F-FDG

    Core Imaging Library - Part II:multichannel reconstruction for dynamic and spectral tomography

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    The newly developed core imaging library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularized reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g. dynamic and spectral. We formalize different optimization problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL’s capabilities for designing state-of-the-art reconstruction methods through case studies and code snapshots

    Monte-Carlo simulations and image reconstruction for novel imaging scenarios in emission tomography

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    AbstractEmission imaging incorporates both the development of dedicated devices for data acquisition as well as algorithms for recovering images from that data. Emission tomography is an indirect approach to imaging. The effect of device modification on the final image can be understood through both the way in which data are gathered, using simulation, and the way in which the image is formed from that data, or image reconstruction. When developing novel devices, systems and imaging tasks, accurate simulation and image reconstruction allow performance to be estimated, and in some cases optimized, using computational methods before or during the process of physical construction. However, there are a vast range of approaches, algorithms and pre-existing computational tools that can be exploited and the choices made will affect the accuracy of the in silico results and quality of the reconstructed images. On the one hand, should important physical effects be neglected in either the simulation or reconstruction steps, specific enhancements provided by novel devices may not be represented in the results. On the other hand, over-modeling of device characteristics in either step leads to large computational overheads that can confound timely results. Here, a range of simulation methodologies and toolkits are discussed, as well as reconstruction algorithms that may be employed in emission imaging. The relative advantages and disadvantages of a range of options are highlighted using specific examples from current research scenarios

    Simulation of Clinical PET Studies for the Assessment of Quantification Methods

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    On this PhD thesis we developed a methodology for evaluating the robustness of SUV measurements based on MC simulations and the generation of novel databases of simulated studies based on digital anthropomorphic phantoms. This methodology has been applied to different problems related to quantification that were not previously addressed. Two methods for estimating the extravasated dose were proposed andvalidated in different scenarios using MC simulations. We studied the impact of noise and low counting in the accuracy and repeatability of three commonly used SUV metrics (SUVmax, SUVmean and SUV50). The same model was used to study the effect of physiological muscular uptake variations on the quantification of FDG-PET studies. Finally, our MC models were applied to simulate 18F-fluorocholine (FCH) studies. The aim was to study the effect of spill-in counts from neighbouring regions on the quantification of small regions close to high activity extended sources

    Optimal reconstruction algorithms for high-resolution positron emission tomography

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    Lammertsma, A.A. [Promotor]Boellaard, R. [Copromotor

    Improving the Accuracy of CT-derived Attenuation Correction in Respiratory-Gated PET/CT Imaging

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    The effect of respiratory motion on attenuation correction in Fludeoxyglucose (18F) positron emission tomography (FDG-PET) was investigated. Improvements to the accuracy of computed tomography (CT) derived attenuation correction were obtained through the alignment of the attenuation map to each emission image in a respiratory gated PET scan. Attenuation misalignment leads to artefacts in the reconstructed PET image and several methods were devised for evaluating the attenuation inaccuracies caused by this. These methods of evaluation were extended to finding the frame in the respiratory gated PET which best matched the CT. This frame was then used as a reference frame in mono-modality compensation for misalignment. Attenuation correction was found to affect the quantification of tumour volumes; thus a regional analysis was used to evaluate the impact of mismatch and the benefits of compensating for misalignment. Deformable image registration was used to compensate for misalignment, however, there were inaccuracies caused by the poor signal-to-noise ratio (SNR) in PET images. Two models were developed that were robust to a poor SNR allowing for the estimation of deformation from very noisy images. Firstly, a cross population model was developed by statistically analysing the respiratory motion in 10 4DCT scans. Secondly, a 1D model of respiration was developed based on the physiological function of respiration. The 1D approach correctly modelled the expansion and contraction of the lungs and the differences in the compressibility of lungs and surrounding tissues. Several additional models were considered but were ruled out based on their poor goodness of fit to 4DCT scans. Approaches to evaluating the developed models were also used to assist with optimising for the most accurate attenuation correction. It was found that the multimodality registration of the CT image to the PET image was the most accurate approach to compensating for attenuation correction mismatch. Mono-modality image registration was found to be the least accurate approach, however, incorporating a motion model improved the accuracy of image registration. The significance of these findings is twofold. Firstly, it was found that motion models are required to improve the accuracy in compensating for attenuation correction mismatch and secondly, a validation method was found for comparing approaches to compensating for attenuation mismatch
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