Acceleration of GATE Monte Carlo simulations

Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography are forms of medical imaging that produce functional images that reflect biological processes. They are based on the tracer principle. A biologically active substance, a pharmaceutical, is selected so that its spatial and temporal distribution in the body reflects a certain body function or metabolism. In order to form images of the distribution, the pharmaceutical is labeled with gamma-ray-emitting or positron-emitting radionuclides (radiopharmaceuticals or tracers). After administration of the tracer to a patient, an external position-sensitive gamma-ray camera can detect the emitted radiation to form a stack of images of the radionuclide distribution after a reconstruction process. Monte Carlo methods are numerical methods that use random numbers to compute quantities of interest. This is normally done by creating a random variable whose expected value is the desired quantity. One then simulates and tabulates the random variable and uses its sample mean and variance to construct probabilistic estimates. It represents an attempt to model nature through direct simulation of the essential dynamics of the system in question. Monte Carlo modeling is the method of choice for all applications where measurements are not feasible or where analytic models are not available due to the complex nature of the problem. In addition, such modeling is a practical approach in nuclear medical imaging in several important application fields: detector design, quantification, correction methods for image degradations, detection tasks etc. Several powerful dedicated Monte Carlo simulators for PET and/or SPECT are available. However, they are often not detailed nor flexible enough to enable realistic simulations of emission tomography detector geometries while also modeling time dependent processes such as decay, tracer kinetics, patient and bed motion, dead time or detector orbits. Our Monte Carlo simulator of choice, GEANT4 Application for Tomographic Emission (GATE), was specifically designed to address all these issues. The flexibility of GATE comes at a price however. The simulation of a simple prototype SPECT detector may be feasible within hours in GATE but an acquisition with a realistic phantom may take years to complete on a single CPU. In this dissertation we therefore focus on the Achilles’ heel of GATE: efficiency. Acceleration of GATE simulations can only be achieved through a combination of efficient data analysis, dedicated variance reduction techniques, fast navigation algorithms and parallelization. In the first part of this dissertation we consider the improvement of the analysis capabilities of GATE. The static analysis module in GATE is both inflexible and incapable of storing more detail without introducing a large computational overhead. However, the design and validation of the acceleration techniques in this dissertation requires a flexible, detailed and computationally efficient analysis module. To this end, we develop a new analysis framework capable of analyzing any process, from the decay of isotopes to particle interactions and detections in any detector element for any type of phantom. The evaluation of our framework consists of the assessment of spurious activity in 124I-Bexxar PET and of contamination in 131I-Bexxar SPECT. In the case of PET we describe how our framework can detect spurious coincidences generated by non-pure isotopes, even with realistic phantoms. We show that optimized energy thresholds, which can readily be applied in the clinic, can now be derived in order to minimize the contamination. We also show that the spurious activity itself is not spatially uniform. Therefore standard reconstruction and correction techniques are not adequate. In the case of SPECT we describe how it is now possible to classify detections into geometric detections, phantom scatter, penetration through the collimator, collimator scatter and backscatter in the end parts. We show that standard correction algorithms such as triple energy window correction cannot correct for septal penetration. We demonstrate that 124I PET with optimized energy thresholds offer better image quality than 131I SPECT when using standard reconstruction techniques. In the second part of this dissertation we focus on improving the efficiency of GATE with a variance reduction technique called Geometrical Importance Sampling (GIS). We describe how only 0.02% of all emitted photons can reach the crystal surface of a SPECT detector head with a low energy high resolution collimator. A lot of computing power is therefore wasted by tracking photons that will not contribute to the result. A twofold strategy is used to solve this problem: GIS employs Russian Roulette to discard those photons that will not likely contribute to the result. Photons in more important regions on the other hand are split into several photons with reduced weight to increase their survival chance. We show that this technique introduces branches into the particle history. We describe how this can be taken into account by a particle history tree that is used for the analysis of the results. The evaluation of GIS consists of energy spectra validation, spatial resolution and sensitivity for low and medium energy isotopes. We show that GIS reaches acceleration factors between 5 and 13 over analog GATE simulations for the isotopes in the study. It is a general acceleration technique that can be used for any isotope, phantom and detector combination. Although GIS is useful as a safe and accurate acceleration technique, it cannot deliver clinically acceptable simulation times. The main reason lies in its inability to force photons in a specific direction. In the third part of this dissertation we solve this problem for 99mTc SPECT simulations. Our approach is twofold. Firstly, we introduce two variance reduction techniques: forced detection (FD) and convolution-based forced detection (CFD) with multiple projection sampling (MPS). FD and CFD force copies of photons at decay and at every interaction point to be transported through the phantom in a direction sampled within a solid angle toward the SPECT detector head at all SPECT angles simultaneously. We describe how a weight must be assigned to each photon in order to compensate for the forced direction and non-absorption at emission and scatter. We show how the weights are calculated from the total and differential Compton and Rayleigh cross sections per electron with incorporation of Hubbell’s atomic form factor. In the case of FD all detector interactions are modeled by Monte Carlo, while in the case of CFD the detector is modeled analytically. Secondly, we describe the design of an FD and CFD specialized navigator to accelerate the slow tracking algorithms in GEANT4. The validation study shows that both FD and CFD closely match the analog GATE simulations and that we can obtain an acceleration factor between 3 (FD) and 6 (CFD) orders of magnitude over analog simulations. This allows for the simulation of a realistic acquisition with a torso phantom within 130 seconds. In the fourth part of this dissertation we exploit the intrinsic parallel nature of Monte Carlo simulations. We show how Monte Carlo simulations should scale linearly as a function of the number of processing nodes but that this is usually not achieved due to job setup time, output handling and cluster overhead. We describe how our approach is based on two steps: job distribution and output data handling. The job distribution is based on a time-domain partitioning scheme that retains all experimental parameters and that guarantees the statistical independence of each subsimulation. We also reduce the job setup time by the introduction of a parameterized collimator model for SPECT simulations. We reduce the data output handling time by a chain-based output merger. The scalability study is based on a set of simulations on a 70 CPU cluster and shows an acceleration factor of approximately 66 on 70 CPUs for both PET and SPECT.We also show that our method of parallelization does not introduce any approximations and that it can be readily combined with any of the previous acceleration techniques described above

A virtual imaging platform for multi-modality medical image simulation.

International audienceThis paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workflow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011

Investigation of the Effects of Image Signal-to-Noise Ratio on TSPO PET Quantification of Neuroinflammation

Neuroinflammation may be imaged using positron emission tomography (PET) and the tracer [11C]-PK11195. Accurate and precise quantification of 18 kilodalton Translocator Protein (TSPO) binding parameters in the brain has proven difficult with this tracer, due to an unfavourable combination of low target concentration in tissue, low brain uptake of the tracer and relatively high non-specific binding, all of which leads to higher levels of relative image noise. To address these limitations, research into new radioligands for the TSPO, with higher brain uptake and lower non-specific binding relative to [11C]-PK11195, is being conducted world-wide. However, factors other than radioligand properties are known to influence signal-to-noise ratio in quantitative PET studies, including the scanner sensitivity, image reconstruction algorithms and data analysis methodology. The aim of this thesis was to investigate and validate computational tools for predicting image noise in dynamic TSPO PET studies, and to employ those tools to investigate the factors that affect image SNR and reliability of TSPO quantification in the human brain. The feasibility of performing multiple (n≥40) independent Monte Carlo simulations for each dynamic [11C]-PK11195 frame- with realistic modelling of the radioactivity source, attenuation and PET tomograph geometries- was investigated. A Beowulf-type high performance computer cluster, constructed from commodity components, was found to be well suited to this task. Timing tests on a single desktop computer system indicated that a computer cluster capable of simulating an hour-long dynamic [11C]-PK11195 PET scan, with 40 independent repeats, and with a total simulation time of less than 6 weeks, could be constructed for less than 10,000 Australian dollars. A computer cluster containing 44 computing cores was therefore assembled, and a peak simulation rate of 2.84x105 photon pairs per second was achieved using the GEANT4 Application for Tomographic Emission (GATE) Monte Carlo simulation software. A simulated PET tomograph was developed in GATE that closely modelled the performance characteristics of several real-world clinical PET systems in terms of spatial resolution, sensitivity, scatter fraction and counting rate performance. The simulated PET system was validated using adaptations of the National Electrical Manufacturers Association (NEMA) quality assurance procedures within GATE. Image noise in dynamic TSPO PET scans was estimated by performing n=40 independent Monte Carlo simulations of an hour-long [11C]-PK11195 scan, and of an hour- long dynamic scan for a hypothetical TSPO ligand with double the brain activity concentration of [11C]-PK11195. From these data an analytical noise model was developed that allowed image noise to be predicted for any combination of brain tissue activity concentration and scan duration. The noise model was validated for the purpose of determining the precision of kinetic parameter estimates for TSPO PET. An investigation was made into the effects of activity concentration in tissue, radionuclide half-life, injected dose and compartmental model complexity on the reproducibility of kinetic parameters. Injecting 555 MBq of carbon-11 labelled TSPO tracer produced similar binding parameter precision to 185 MBq of fluorine-18, and a moderate (20%) reduction in precision was observed for the reduced carbon-11 dose of 370 MBq. Results indicated that a factor of 2 increase in frame count level (relative to [11C]-PK11195, and due for example to higher ligand uptake, injected dose or absolute scanner sensitivity) is required to obtain reliable binding parameter estimates for small regions of interest when fitting a two-tissue compartment, four-parameter compartmental model. However, compartmental model complexity had a similarly large effect, with the reduction of model complexity from the two-tissue compartment, four-parameter to a one-tissue compartment, two-parameter model producing a 78% reduction in coefficient of variation of the binding parameter estimates at each tissue activity level and region size studied. In summary, this thesis describes the development and validation of Monte Carlo methods for estimating image noise in dynamic TSPO PET scans, and analytical methods for predicting relative image noise for a wide range of tissue activity concentration and acquisition durations. The findings of this research suggest that a broader consideration of the kinetic properties of novel TSPO radioligands, with a view to selection of ligands that are potentially amenable to analysis with a simple one-tissue compartment model, is at least as important as efforts directed towards reducing image noise, such as higher brain uptake, in the search for the next generation of TSPO PET tracers

Optimization of the Parameters of the YAP-(S)PETII Scanner for SPECT Acquisition

Abstract Single Photon Emission Computed Tomography (SPECT) could be considered as a milestone in terms of biomedical imaging technique, which visualizes Functional processes in-vivo, based on the emission of gamma rays produced within the body. The most distinctive feature of SPECT from other imaging modalities is that it is based on the tracer principle, discovered by George Charles de Hevesy in the first decade of the twentieth century. As known by everyone, the metabolism of an organism is composed of atoms within a molecule which can be replaced by one of its radioactive isotopes. By using this principle, we are able to follow and detect pathways of the photons which are emitted from the radioactive element inside the metabolism. SPECT produces images by using a gamma camera which consists of two major functional components, the collimator and the radiation detector. The collimator is a thick sheet of a heavy metal like lead, tungsten of gold with densely packed small holes and is put just in front of the photon detector. The radiation detector converts the gamma rays into scintillation light photons. In conventional SPECT, scanners utilize a parallel hole collimator. Defining a small solid angle, each collimator hole is located somewhere along this line and the photons might reach the detector by passing through these holes. Subsequently, we can create projection images of the radioisotope distribution. The quantity of photons which come to the radiation detector through the collimator holes specifies the image quality regarding signal to noise ratio. One of the crucial parts of all SPECT scanners is the collimator design. The main part of this dissertation is to investigate performance characteristics of YAP-(S)PETII scanner collimator and to obtain collimator characteristics curves for optimization purposes. Before starting the collimator performance investigation of YAP-(S)PETII scanner, we first performed simulation of it in SPECT mode with point source Tc-99m to measure collimator and system efficiency by using GATE–the Geant4 Application for Emission Tomography. GATE is an advanced, flexible, precise, opensource Monte Carlo toolkit developed by the international OpenGATE collaboration and dedicated to the numerical simulations in medical imaging. We obtained the results of collimator and system efficiency in terms of collimator length, radius and septa by using GATE_v4. Then, we compared our results with analytical formulation of efficiency and resolution. For those simulation experiments, we found that the difference between the simulated and the analytical results with regard to approximated geometrical collimator efficiency formulation of H. Anger, is within 20%. Then, we wrote a new ASCII sorter algorithm, which reads ASCII output of GATE_v4 and then creates a sinogram and reconstructs it to see the final simulation results. At the beginning, we used the analytical reconstruction method, filtered back projection (FBP), but this method produces severely blurred images. To solve this problem and increase our image quality, we tried different mathematical filters, like ramp, sheep-logan, low-pass cosine filters. After all of those studies mentioned above, we learned that GATE_v4 is not practical to measure collimator efficiency and resolution. On the other hand, the results of GATE_v4 did not show directly septal penetrated photon ratio. Under the light of these findings, we decided to develop a new user-friendly ray tracing program for optimization of low energy general purpose (LEGP) parallelhole collimators. In addition, we tried to evaluate the image quality and quantify the impact of high-energy contamination for I-123 isotope imaging. Due to its promising chemical characteristics, Iodine-123 is increasingly used in SPECT studies. 159 keV photons are used for imaging, however, high-energy photons result in an error in the projection data primarily by penetration of the collimator and scattering inside the crystal with energy close to the photons used for imaging. One of the way to minimize this effect is using a double energy window (DEW) method, because, it decreases noise in main (sensitive) energy window. By using this method, we tried to determine the difference between simulated and experimental projection results and scattered photon ratio (Sk) value of YAP-(S)PETII scanner for I-123 measurements. The main drawback of GATE simulations is that they are CPU-intensive. In this dissertation to handle this problem, we did the feasibility study of the Fully Monte Carlo based implementation of the system matrix derivation of YAP-(S)PETII scanner by using XtreemOS platform. To manage lifecycle of the simulation on the top XtreemOS, we developed a set of scripts. The main purpose of our study is to integrate a distributed platform like XtreemOS to reduce the overall simulation completion time and increase the feasibility of SPECT simulations in a research environment and establish an accurate and fast method for deriving the system matrix of the YAP-(S)PETII scanner by using Monte Carlo simulation approach. We developed also the ML-EM Algorithm to the reconstruct our GATE simulation results and to derive the system matrix directly from GATE output. In addition to the accuracy consideration, we intend to develop a flexible matrix derivation method and GATE output reconstruction tool

Variational segmentation of vector-valued images with gradient vector flow

International audienceIn this paper, we extend the gradient vector flow field for robust variational segmentation of vector-valued images. Rather than using scalar edge information, we define a vectorial edge map derived from a weighted local structure tensor of the image that enables the diffusion of the gradient vectors in accurate directions through the 4DGVF equation. To reduce the contribution of noise in the structure tensor, image channels are weighted according to a blind estimator of contrast. The method is applied to biological volume delineation in dynamic PET imaging, and validated on realistic Monte Carlo simulations of numerical phantoms as well as on real images

A GATE-based Monte Carlo simulation of a dual-layer pixelized gadolinium oxyorthosilicate (GSO) detector performance and response for micro PET scanner

The purpose of this study was to simulate the GSO detector of a micro PET using GATE simulation platform. The performance and responses of the simulated GSO detector assembly were evaluated by comparing the simulated data to the experimental and XCOM data to validate the simulation platform and procedure. Based on NEMA NU-4 2008 protocols, the performance of GSO detector in terms of sensitivity was simulated and compared to the experimental data. Similarly, the GSO detector response to photons interaction was simulated and compared against the XCOM data for absorbed intensity ratio in the GSO detector and survived intensity ratio in Pb blocks. Results showed that simulated and experimental sensitivities agreed well with R2 of 0.995 and two overlapping bands at 95% confidence. An agreement with R2 of 0.972 and 0.973 as well as with overlapping bands at 95% confidence was obtained in simulated and XCOM data for absorbed and survived intensity ratio in the GSO detector and Pb blocks, respectively. The observed agreements demonstrate the accuracy of the simulation method to mimic the behaviour of the GSO detector. The validated GATE algorithm for micro PET scanner is therefore recommended for simulation and optimisation of collimator design in further studies. Keywords: GATE simulation, Experimental data, XCOM data, GSO detector, micro PET. &nbsp

Dosimétrie clinique en radiothérapie moléculaire

La radiothérapie moléculaire (RTM) est une radiothérapie systémique, où le produit radiopharmaceutique se lie spécifiquement sur les tumeurs pour détruire sélectivement les cibles cancéreuses tout en préservant les organes sains. Lutathera® (177Lu-DOTATATE) est un radiopharmaceutique récemment approuvé par la FDA/EMA pour le traitement des tumeurs neuroendocrines gastro-entéro-pancréatiques (GEP-NETs). Dans la pratique clinique, les patients reçoivent une activité fixe de Lutathera®, 4 cycles de 7,4 GBq, en supposant que la pharmacocinétique du radiopharmaceutique est même entre les patients. La dosimétrie spécifique au patient permet un changement de paradigme majeur dans l'administration de la RTM, passant d'une approche "taille unique" à une véritable médecine personnalisée où l'activité administrée est évaluée spécifiquement sur la base de l'irradiation délivrée à chaque patient. Pour ce faire, il faut généralement déterminer la distribution spatiale du radiopharmaceutique dans les organes par imagerie à différents moments (imagerie quantitative), estimer le nombre total de désintégrations radioactives en intégrant l'activité dans le temps (évaluation pharmacocinétique) et calculer la dose absorbée à partir des caractéristiques physiques du radionucléide et du transport de l'énergie dans les tissus du patient. Actuellement, il n'existe pas de procédures normalisées pour effectuer la dosimétrie clinique. En outre, l'évaluation des incertitudes associées à la procédure de dosimétrie n'est pas triviale. Le projet DosiTest a été lancé pour évaluer les incertitudes associées à chacune des étapes du flux de travail de la dosimétrie clinique, via une inter-comparaison multicentrique basée sur la modélisation de Monte Carlo (MC). La première phase de la thèse a consisté à comparer les analyses dosimétriques effectuées par différents centres utilisant le même logiciel et le même protocole sur le même ensemble de données de patients dans le cadre du projet IAEA-CRP E23005 afin d'évaluer la précision de la dosimétrie clinique. À notre connaissance, c'est la première fois qu'une comparaison dosimétrique multicentrique d'un seul ensemble de données cliniques sur un patient a été entreprise en utilisant le même protocole et le même logiciel par de nombreux centres dans le monde entier. Elle a mis en évidence le besoin crucial d'établir des points de contrôle et d'effectuer des vérifications de bon sens pour éliminer les disparités significatives entre les résultats et distinguer les pratiques erronées de la variabilité inter-opérateurs acceptable. Un résultat important de ce travail a été le manque d'assurance qualité en dosimétrie de médecine nucléaire clinique et la nécessité de développer des procédures de contrôle qualité. Alors que la dosimétrie gagne en popularité en médecine nucléaire, les meilleures pratiques doivent être adoptées pour garantir la fiabilité, la traçabilité et la reproductibilité des résultats. Cela met également en avant la nécessité de dispenser une formation suffisante après l'acquisition des progiciels relativement nouveaux, au-delà de quelques jours. Ceci est clairement insuffisant dans le contexte d'un domaine émergent où l'expérience professionnelle fait souvent défaut. Ensuite, l'étude de l'exactitude de la dosimétrie clinique nécessite de générer des ensembles de données de test, afin de définir la vérité de base par rapport à laquelle les procédures de dosimétrie clinique peuvent être comparées. La deuxième section de la thèse traite de la simulation de l'imagerie TEMP scintigraphique tridimensionnelle en implémentant le mouvement du détecteur d'auto-contournement dans la boîte à outils Monte Carlo GATE. Après la validation des projections TEMP/TDM sur des modèles anthropomorphes, une série d'images réalistes de patients cliniques a été générée. La dernière partie de la thèse a établi la preuve de concept du projet DosiTest, en utilisant un ensemble de données TEMP/TDM virtuelles (simulées) à différents moments, avec différentes gamma-caméras, permettant de comparer différentes techniques dosimétriques et d'évaluer la faisabilité clinique du projet dans certains départements de médecine nucléaire.Molecular radiotherapy (MRT) is a systemic radiotherapy where the radiopharmaceutical binds specifically to tumours to selectively destroy cancer targets while sparing healthy organs. Lutathera® (177Lu-DOTATATE) is a radiopharmaceutical that was recently FDA/EMA approved for the treatment of the GastroEnteroPancreatic NeuroEndocrine Tumours (GEP-NETs). In clinical practice, patients are administered with a fixed activity of Lutathera®, assuming that radiopharmaceutical distribution is the same for all patients. Patient-specific dosimetry allows for a major paradigm shift in the administration of MRT from "one-size-fits-all" approach, to real personalised medicine where administered activity is assessed specifically on the base of the irradiation delivered to each patient. This usually requires determining the spatial distribution of the radiopharmaceutical in various organs via imaging at different times (quantitative imaging), estimating the total number of radioactive decays by integrating activity over time (pharmacokinetic assessment) and calculating the absorbed dose using the physical characteristics of the radionuclide and implementing radiation transport in patient's tissues. Currently, there are no standardised procedures to perform clinical dosimetry. In addition, the assessment of the uncertainties associated with the dosimetry procedure is not trivial. The DosiTest project (http://www.dositest.org/) was initiated to evaluate uncertainties associated with each of the steps of the clinical dosimetry workflow, via a multicentric inter-comparison based on Monte Carlo (MC) modelling. The first phase of the thesis compared dosimetry analysis performed by various centres using the same software and protocol on the same patient dataset as a part of IAEA-CRP E23005 project in order to appraise the precision of clinical dosimetry. To our knowledge, this is the first time that a multi-centric dosimetry comparison of a single clinical patient dataset has been undertaken using the same protocol and software by many centres worldwide. It highlighted the critical need to establish checkpoints and conduct sanity checks to eliminate significant disparities among results, and distinguish erroneous practice with acceptable inter-operator variability. A significant outcome of this work was the lack of quality assurance in clinical nuclear medicine dosimetry and the need for the development of quality control procedures. While dosimetry is gaining popularity in nuclear medicine, best practices should be adopted to ensure that results are reliable, traceable, and reproducible. It also brings forward the need to deliver sufficient training after the acquisition of the relatively new software packages beyond a couple of days. This is clearly insufficient in a context of an emerging field where the professional experience is quite often lacking. Next, the study of clinical dosimetry accuracy requires generating test datasets, to define the ground truth against which clinical dosimetry procedures can be benchmarked. The second section of the thesis addressed the simulation of three-dimensional scintigraphic SPECT imaging by implementing auto-contouring detector motion in the GATE Monte Carlo toolkit. Following the validation of SPECT/CT projections on anthropomorphic models, a series of realistic clinical patient images were generated. The last part of the thesis established the proof of concept of the DosiTest project, using a virtual (simulated) SPECT/CT dataset at various time points, with various gamma cameras, enabling comparison of various dosimetric techniques and to assess the clinical feasibility of the project in selected nuclear medicine departments

A Virtual Imaging Platform for Multi-Modality Medical Image Simulation

This paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workίow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011

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

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

Stochastic Optimisation Methods Applied to PET Image Reconstruction

Positron Emission Tomography (PET) is a medical imaging technique that is used to pro- vide functional information regarding physiological processes. Statistical PET reconstruc- tion attempts to estimate the distribution of radiotracer in the body but this methodology is generally computationally demanding because of the use of iterative algorithms. These algorithms are often accelerated by the utilisation of data subsets, which may result in con- vergence to a limit set rather than the unique solution. Methods exist to relax the update step sizes of subset algorithms but they introduce additional heuristic parameters that may result in extended reconstruction times. This work investigates novel methods to modify subset algorithms to converge to the unique solution while maintaining the acceleration benefits of subset methods. This work begins with a study of an automatic method for increasing subset sizes, called AutoSubsets. This algorithm measures the divergence between two distinct data subset update directions and, if significant, the subset size is increased for future updates. The algorithm is evaluated using both projection and list mode data. The algorithm’s use of small initial subsets benefits early reconstruction but unfortunately, at later updates, the subsets size increases too early, which impedes convergence rates. The main part of this work investigates the application of stochastic variance reduction optimisation algorithms to PET image reconstruction. These algorithms reduce variance due to the use of subsets by incorporating previously computed subset gradients into the update direction. The algorithms are adapted for the application to PET reconstruction. This study evaluates the reconstruction performance of these algorithms when applied to various 3D non-TOF PET simulated, phantom and patient data sets. The impact of a number of algorithm parameters are explored, which includes: subset selection methodologies, the number of subsets, step size methodologies and preconditioners. The results indicate that these stochastic variance reduction algorithms demonstrate superior performance after only a few epochs when compared to a standard PET reconstruction algorithm