J-PET Framework: Software platform for PET tomography data reconstruction and analysis

J-PET Framework is an open-source software platform for data analysis, written in C++ and based on the ROOT package. It provides a common environment for implementation of reconstruction, calibration and filtering procedures, as well as for user-level analyses of Positron Emission Tomography data. The library contains a set of building blocks that can be combined by users with even little programming experience, into chains of processing tasks through a convenient, simple and well-documented API. The generic input-output interface allows processing the data from various sources: low-level data from the tomography acquisition system or from diagnostic setups such as digital oscilloscopes, as well as high-level tomography structures e.g. sinograms or a list of lines-of-response. Moreover, the environment can be interfaced with Monte Carlo simulation packages such as GEANT and GATE, which are commonly used in the medical scientific community.Comment: 14 pages, 5 figure

Relevance of accurate Monte Carlo modeling in nuclear medical imaging

Monte Carlo techniques have become popular in different areas of medical physics with advantage of powerful computing systems. In particular, they have been extensively applied to simulate processes involving random behavior and to quantify physical parameters that are difficult or even impossible to calculate by experimental measurements. Recent nuclear medical imaging innovations such as single-photon emission computed tomography (SPECT), positron emission tomography (PET), and multiple emission tomography (MET) are ideal for Monte Carlo modeling techniques because of the stochastic nature of radiation emission, transport and detection processes. Factors which have contributed to the wider use include improved models of radiation transport processes, the practicality of application with the development of acceleration schemes and the improved speed of computers. This paper presents derivation and methodological basis for this approach and critically reviews their areas of application in nuclear imaging. An overview of existing simulation programs is provided and illustrated with examples of some useful features of such sophisticated tools in connection with common computing facilities and more powerful multiple-processor parallel processing systems. Current and future trends in the field are also discussed

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

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

Novel PET Systems and Image Reconstruction with Actively Controlled Geometry

Positron Emission Tomography (PET) provides in vivo measurement of imaging ligands that are labeled with positron emitting radionuclide. Since its invention, most PET scanners have been designed to have a group of gamma ray detectors arranged in a ring geometry, accommodating the whole patient body. Virtual Pinhole PET incorporates higher resolution detectors being placed close to the Region-of-Interest (ROI) within the imaging Field-of-View (FOV) of the whole-body scanner, providing better image resolution and contrast recover. To further adapt this technology to a wider range of diseases, we proposed a second generation of virtual pinhole PET using actively controlled high resolution detectors integrated on a robotic arm. When the whole system is integrated to a commercial PET scanner, we achieved positioning repeatability within 0.5 mm. Monte Carlo simulation shows that by focusing the high-resolution detectors to a specific organ of interest, we can achieve better resolution, sensitivity and contrast recovery. In another direction, we proposed a portable, versatile and low cost PET imaging system for Point-of-Care (POC) applications. It consists of one or more movable detectors in coincidence with a detector array behind a patient. The movable detectors make it possible for the operator to control the scanning trajectory freely to achieve optimal coverage and sensitivity for patient specific imaging tasks. Since this system does not require a conventional full ring geometry, it can be built portable and low cost for bed-side or intraoperative use. We developed a proof-of-principle prototype that consists of a compact high resolution silicon photomultiplier detector mounted on a hand-held probe and a half ring of conventional detectors. The probe is attached to a MicroScribe device, which tracks the location and orientation of the probe as it moves. We also performed Monte Carlo simulations for two POC PET geometries with Time-of-Flight (TOF) capability. To support the development of such PET systems with unconventional geometries, a fully 3D image reconstruction framework has been developed for PET systems with arbitrary geometry. For POC PET and the second generation robotic Virtual Pinhole PET, new challenges emerge and our targeted applications require more efficiently image reconstruction that provides imaging results in near real time. Inspired by the previous work, we developed a list mode GPU-based image reconstruction framework with the capability to model dynamically changing geometry. Ordered-Subset MAP-EM algorithm is implemented on multi-GPU platform to achieve fast reconstruction in the order of seconds per iteration, under practical data rate. We tested this using both experimental and simulation data, for whole body PET scanner and unconventional PET scanners. Future application of adaptive imaging requires near real time performance for large statistics, which requires additional acceleration of this framework

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

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

Approaches Toward Combining Positron Emission Tomography with Magnetic Resonance Imaging

Positron emission tomography (PET) and magnetic resonance imaging (MRI) provide complementary information, and there has been a great deal of research effort to combine these two modalities. A major engineering hurdle is that photomultiplier tubes (PMT), used in conventional PET detectors, are sensitive to magnetic field. This thesis explores the design considerations of different ways of combining small animal PMT-based PET systems with MRI through experimentation, modelling and Monte Carlo simulation. A proof-of-principle hybrid PET and field-cycled MRI system was built and the first multimodality images are shown. A Siemens Inveon PET was exposed to magnetic fields of different strengths and the performance is characterized as a function of field magnitude. The results of this experiment established external magnetic field limits and design studies are shown for wide range of approaches to combining the PET system with various configurations of field-cycled MRI and superconducting MRI systems. A sophisticated Monte Carlo PET simulation workflow based on the GATE toolkit was developed to model the Siemens Inveon PET. Simulated PET data were converted to the raw Siemens list-mode format and were processed and reconstructed using the same processing chain as the data measured on the actual scanner. A general GATE add-on was developed to rapidly generate attenuation correction sinograms using the precise detector geometry and attenuation coefficients built into the emission simulation. Emission simulations and the attenuation correction add-on were validated against measured data. Simulations were performed to study the impact of radiofrequency coil components on PET image quality and to test the suitability of various MR-compatible materials for a dual-modality animal bed

ProTheRaMon : a GATE simulation framework for proton therapy range monitoring using PET imaging

Objective. This paper reports on the implementation and shows examples of the use of the ProTheRaMon framework for simulating the delivery of proton therapy treatment plans and range monitoring using positron emission tomography (PET). ProTheRaMon offers complete processing of proton therapy treatment plans, patient CT geometries, and intra-treatment PET imaging, taking into account therapy and imaging coordinate systems and activity decay during the PET imaging protocol specific to a given proton therapy facility. We present the ProTheRaMon framework and illustrate its potential use case and data processing steps for a patient treated at the Cyclotron Centre Bronowice (CCB) proton therapy center in Krakow, Poland. Approach. The ProTheRaMon framework is based on GATE Monte Carlo software, the CASToR reconstruction package and in-house developed Python and bash scripts. The framework consists of five separated simulation and data processing steps, that can be further optimized according to the user’s needs and specific settings of a given proton therapy facility and PET scanner design. Main results. ProTheRaMon is presented using example data from a patient treated at CCB and the J-PET scanner to demonstrate the application of the framework for proton therapy range monitoring. The output of each simulation and data processing stage is described and visualized. Significance. We demonstrate that the ProTheRaMon simulation platform is a high-performance tool, capable of running on a computational cluster and suitable for multi-parameter studies, with databases consisting of large number of patients, as well as different PET scanner geometries and settings for range monitoring in a clinical environment. Due to its modular structure, the ProTheRaMon framework can be adjusted for different proton therapy centers and/or different PET detector geometries. It is available to the community via github (Borys et al 2022)

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