Evaluation of resistive-plate-chamber-based TOF-PET applied to in-beam particle therapy monitoring

Particle therapy is a highly conformal radiotherapy technique which reduces the dose deposited to the surrounding normal tissues. In order to fully exploit its advantages, treatment monitoring is necessary to minimize uncertainties related to the dose delivery. Up to now, the only clinically feasible technique for the monitoring of therapeutic irradiation with particle beams is Positron Emission Tomography (PET). In this work we have compared a Resistive Plate Chamber (RPC)-based PET scanner with a scintillation-crystal-based PET scanner for this application. In general, the main advantages of the RPC-PET system are its excellent timing resolution, low cost, and the possibility of building large area systems. We simulated a partial-ring scannerbeam monitoring, which has an intrinsically low positron yield compared to diagnostic PET. In addition, for in-beam PET there is a further data loss due to the partial ring configuration. In order to improve the performance of the RPC-based scanner, an improved version of the RPC detector (modifying the thickness of the gas and glass layers), providing a larger sensitivity, has been simulated and compared with an axially extended version of the crystal-based device. The improved version of the RPC shows better performance than the prototype, but the extended version of the crystal-based PET outperforms all other options. based on an RPC prototype under construction within the Fondazione per Adroterapia Oncologica (TERA). For comparison with the crystal-based PET scanner we have chosen the geometry of a commercially available PET scanner, the Philips Gemini TF. The coincidence time resolution used in the simulations takes into account the current achievable values as well as expected improvements of both technologies. Several scenarios (including patient data) have been simulated to evaluate the performance of different scanners. Initial results have shown that the low sensitivity of the RPC hampers its application to hadro

Post-Reconstruction Deconvolution of PET Images by Total Generalized Variation Regularization

Improving the quality of positron emission tomography (PET) images, affected by low resolution and high level of noise, is a challenging task in nuclear medicine and radiotherapy. This work proposes a restoration method, achieved after tomographic reconstruction of the images and targeting clinical situations where raw data are often not accessible. Based on inverse problem methods, our contribution introduces the recently developed total generalized variation (TGV) norm to regularize PET image deconvolution. Moreover, we stabilize this procedure with additional image constraints such as positivity and photometry invariance. A criterion for updating and adjusting automatically the regularization parameter in case of Poisson noise is also presented. Experiments are conducted on both synthetic data and real patient images.Comment: First published in the Proceedings of the 23rd European Signal Processing Conference (EUSIPCO-2015) in 2015, published by EURASI

Topics in image reconstruction for high resolution positron emission tomography

Les problèmes mal posés représentent un sujet d'intérêt interdisciplinaire qui surgires dans la télédétection et des applications d'imagerie. Cependant, il subsiste des questions cruciales pour l'application réussie de la théorie à une modalité d'imagerie. La tomographie d'émission par positron (TEP) est une technique d'imagerie non-invasive qui permet d'évaluer des processus biochimiques se déroulant à l'intérieur d'organismes in vivo. La TEP est un outil avantageux pour la recherche sur la physiologie normale chez l'humain ou l'animal, pour le diagnostic et le suivi thérapeutique du cancer, et l'étude des pathologies dans le coeur et dans le cerveau. La TEP partage plusieurs similarités avec d'autres modalités d'imagerie tomographiques, mais pour exploiter pleinement sa capacité à extraire le maximum d'information à partir des projections, la TEP doit utiliser des algorithmes de reconstruction d'images à la fois sophistiquée et pratiques. Plusieurs aspects de la reconstruction d'images TEP ont été explorés dans le présent travail. Les contributions suivantes sont d'objet de ce travail: Un modèle viable de la matrice de transition du système a été élaboré, utilisant la fonction de réponse analytique des détecteurs basée sur l'atténuation linéaire des rayons y dans un banc de détecteur. Nous avons aussi démontré que l'utilisation d'un modèle simplifié pour le calcul de la matrice du système conduit à des artefacts dans l'image. (IEEE Trans. Nucl. Sei., 2000) );> La modélisation analytique de la dépendance décrite à l'égard de la statistique des images a simplifié l'utilisation de la règle d'arrêt par contre-vérification (CV) et a permis d'accélérer la reconstruction statistique itérative. Cette règle peut être utilisée au lieu du procédé CV original pour des projections aux taux de comptage élevés, lorsque la règle CV produit des images raisonnablement précises. (IEEE Trans. Nucl. Sei., 2001) Nous avons proposé une méthodologie de régularisation utilisant la décomposition en valeur propre (DVP) de la matrice du système basée sur l'analyse de la résolution spatiale. L'analyse des caractéristiques du spectre de valeurs propres nous a permis d'identifier la relation qui existe entre le niveau optimal de troncation du spectre pour la reconstruction DVP et la résolution optimale dans l'image reconstruite. (IEEE Trans. Nucl. Sei., 2001) Nous avons proposé une nouvelle technique linéaire de reconstruction d'image événement-par-événement basée sur la matrice pseudo-inverse régularisée du système. L'algorithme représente une façon rapide de mettre à jour une image, potentiellement en temps réel, et permet, en principe, la visualisation instantanée de distribution de la radioactivité durant l'acquisition des données tomographiques. L'image ainsi calculée est la solution minimisant les moindres carrés du problème inverse régularisé.Abstract: Ill-posed problems are a topic of an interdisciplinary interest arising in remote sensing and non-invasive imaging. However, there are issues crucial for successful application of the theory to a given imaging modality. Positron emission tomography (PET) is a non-invasive imaging technique that allows assessing biochemical processes taking place in an organism in vivo. PET is a valuable tool in investigation of normal human or animal physiology, diagnosing and staging cancer, heart and brain disorders. PET is similar to other tomographie imaging techniques in many ways, but to reach its full potential and to extract maximum information from projection data, PET has to use accurate, yet practical, image reconstruction algorithms. Several topics related to PET image reconstruction have been explored in the present dissertation. The following contributions have been made: (1) A system matrix model has been developed using an analytic detector response function based on linear attenuation of [gamma]-rays in a detector array. It has been demonstrated that the use of an oversimplified system model for the computation of a system matrix results in image artefacts. (IEEE Trans. Nucl. Sci., 2000); (2) The dependence on total counts modelled analytically was used to simplify utilisation of the cross-validation (CV) stopping rule and accelerate statistical iterative reconstruction. It can be utilised instead of the original CV procedure for high-count projection data, when the CV yields reasonably accurate images. (IEEE Trans. Nucl. Sci., 2001); (3) A regularisation methodology employing singular value decomposition (SVD) of the system matrix was proposed based on the spatial resolution analysis. A characteristic property of the singular value spectrum shape was found that revealed a relationship between the optimal truncation level to be used with the truncated SVD reconstruction and the optimal reconstructed image resolution. (IEEE Trans. Nucl. Sci., 2001); (4) A novel event-by-event linear image reconstruction technique based on a regularised pseudo-inverse of the system matrix was proposed. The algorithm provides a fast way to update an image potentially in real time and allows, in principle, for the instant visualisation of the radioactivity distribution while the object is still being scanned. The computed image estimate is the minimum-norm least-squares solution of the regularised inverse problem

Doctor of Philosophy

dissertationSingle Photon Emission Computed Tomography (SPECT) myocardial perfusion imaging (MPI), a noninvasive and effective method for diagnosing coronary artery disease (CAD), is the most commonly performed SPECT procedure. Hence, it is not surprising that there is a tremendous market need for dedicated cardiac SPECT scanners. In this dissertation, a novel dedicated stationary cardiac SPECT system that using a segmented-parallel-hole collimator is investigated in detail. This stationary SPECT system can acquire true dynamic SPECT images and is inexpensive to build. A segmented-parallel-hole collimator was designed to fit the existing general-purpose SPECT cameras without any mechanical modifications of the scanner while providing higher detection sensitivity. With a segmented-parallel-hole collimator, each detector was segmented to seven sub-detector regions, providing seven projections simultaneously. Fourteen view-angles over 180 degree were obtained in total with two detectors positioned at 90 degree apart. The whole system was able to provide an approximate 34-fold gain in sensitivity over the conventional single-head SPECT system. The potential drawbacks of the stationary cardiac SPECT system are data truncation from small field of view (FOV) and limited number of view angles. A tailored maximum-likelihood expectation-maximization (ML-EM) algorithm was derived for reconstruction of truncated projections with few view angles. The artifacts caused by truncation and insufficient number of views were suppressed by reducing the image updating step sizes of the pixels outside the FOV. The performance of the tailored ML-EM algorithm was verified by computer simulations and phantom experiments. Compared with the conventional ML-EM algorithm, the tailored ML-EM algorithm successfully suppresses the streak artifacts outside the FOV and reduces the distortion inside the FOV. At 10 views, the tailored ML-EM algorithm has a much lower mean squared error (MSE) and higher relative contrast. In addition, special attention was given to handle the zero-valued projections in the image reconstruction. There are two categories of zero values in the projection data: one is outside the boundary of the object and the other is inside the object region, which is caused by count starvation. A positive weighting factor c was introduced to the ML-EM algorithm. By setting c>1 for zero values outside the projection, the boundary in the image is well preserved even at extremely low iterations. The black lines, caused by the zero values inside the object region, are completely removed by setting 0< c<1. Finally, the segmented-parallel-hole collimator was fabricated and calibrated using a point source. Closed-form explicit expressions for the slant angles and rotation radius were derived from the proposed system geometry. The geometric parameters were estimated independently or jointly. Monte Carlo simulations and real emission data were used to evaluate the proposed calibration method and the stationary cardiac system. The simulation results show that the difference between the estimated and the actual value is less than 0.1 degree for the slant angles and the 5 mm for the rotation radius, which is well below the detector's intrinsic resolution

Reconstruction Algorithms for Novel Joint Imaging Techniques in PET

Positron emission tomography (PET) is an important functional in vivo imaging modality with many clinical applications. Its enormously wide range of applications has made both research and industry combine it with other imaging modalities such as X-ray computed tomography (CT) or magnetic resonance imaging (MRI). The general purpose of this work is to study two cases in PET where the goal is to perform image reconstruction jointly on two data types. The first case is the Beta-Gamma image reconstruction. Positron emitting isotopes, such as 11C, 13N, and 18F, can be used to label molecules, and tracers, such as 11CO2, are delivered to plants to study their biological processes, particularly metabolism and photosynthesis, which may contribute to the development of plants that have higher yield of crops and biomass. Measurements and resulting images from PET scanners are not quantitative in young plant structures or in plant leaves due to low positron annihilation in thin objects. To address this problem we have designed, assembled, modeled, and tested a nuclear imaging system (Simultaneous Beta-Gamma Imager). The imager can simultaneously detect positrons (β+) and coincidence-gamma rays (γ). The imaging system employs two planar detectors; one is a regular gamma detector which has a LYSO crystal array, and the other is a phoswich detector which has an additional BC-404 plastic scintillator for beta detection. A forward model for positrons is proposed along with a joint image reconstruction formulation to utilize the beta and coincidence-gamma measurements for estimating radioactivity distribution in plant leaves. The joint reconstruction algorithm first reconstructs the beta and gamma images independently to estimate the thickness component of the beta forward model, and then jointly estimates the radioactivity distribution in the object. We have validated the physics model and the reconstruction framework through a phantom imaging study and imaging a tomato leaf that has absorbed 11CO2. The results demonstrate that the simultaneously acquired beta and coincidence-gamma data, combined with our proposed joint reconstruction algorithm, improved the quantitative accuracy of estimating radioactivity distribution in thin objects such as leaves. We used the Structural Similarity (SSIM) index for comparing the leaf images from the Simultaneous Beta-Gamma Imager with the ground truth image. The jointly reconstructed images yield SSIM indices of 0.69 and 0.63, whereas the separately reconstructed beta alone and gamma alone images had indices of 0.33 and 0.52, respectively. The second case is the virtual-pinhole PET technology, which has shown that higher resolution and contrast recovery can be gained by adding a high resolution PET insert with smaller crystals to a conventional PET scanner. Such enhancements are obtained when the insert is placed in proximity of the region of interest (ROI) and in coincidence with the conventional PET scanner. Intuitively, the insert may be positioned within the scanner\u27s axial field-of-view (FOV) and radially closer to the ROI than the scanner\u27s ring. One of the complicating factors of this design is the insert\u27s blocking the scanner\u27s lines-of-response (LORs). Such data may be compensated through attenuation and scatter correction in image reconstruction. However, a potential solution is to place the insert outside of the scanner\u27s axial FOV and to move the body to be in proximity of the insert. We call this imaging strategy the surveillance mode. As the main focus of this work, we have developed an image reconstruction framework for the surveillance mode imaging. The preliminary results show improvement in spatial resolution and contrast recovery. Any improvement in contrast recovery should result in enhancement in tumor detectability, which will be of high clinical significance

Reconstruction Algorithms for Novel Joint Imaging Techniques in PET

Positron emission tomography (PET) is an important functional in vivo imaging modality with many clinical applications. Its enormously wide range of applications has made both research and industry combine it with other imaging modalities such as X-ray computed tomography (CT) or magnetic resonance imaging (MRI). The general purpose of this work is to study two cases in PET where the goal is to perform image reconstruction jointly on two data types. The first case is the Beta-Gamma image reconstruction. Positron emitting isotopes, such as 11C, 13N, and 18F, can be used to label molecules, and tracers, such as 11CO2, are delivered to plants to study their biological processes, particularly metabolism and photosynthesis, which may contribute to the development of plants that have higher yield of crops and biomass. Measurements and resulting images from PET scanners are not quantitative in young plant structures or in plant leaves due to low positron annihilation in thin objects. To address this problem we have designed, assembled, modeled, and tested a nuclear imaging system (Simultaneous Beta-Gamma Imager). The imager can simultaneously detect positrons (β+) and coincidence-gamma rays (γ). The imaging system employs two planar detectors; one is a regular gamma detector which has a LYSO crystal array, and the other is a phoswich detector which has an additional BC-404 plastic scintillator for beta detection. A forward model for positrons is proposed along with a joint image reconstruction formulation to utilize the beta and coincidence-gamma measurements for estimating radioactivity distribution in plant leaves. The joint reconstruction algorithm first reconstructs the beta and gamma images independently to estimate the thickness component of the beta forward model, and then jointly estimates the radioactivity distribution in the object. We have validated the physics model and the reconstruction framework through a phantom imaging study and imaging a tomato leaf that has absorbed 11CO2. The results demonstrate that the simultaneously acquired beta and coincidence-gamma data, combined with our proposed joint reconstruction algorithm, improved the quantitative accuracy of estimating radioactivity distribution in thin objects such as leaves. We used the Structural Similarity (SSIM) index for comparing the leaf images from the Simultaneous Beta-Gamma Imager with the ground truth image. The jointly reconstructed images yield SSIM indices of 0.69 and 0.63, whereas the separately reconstructed beta alone and gamma alone images had indices of 0.33 and 0.52, respectively. The second case is the virtual-pinhole PET technology, which has shown that higher resolution and contrast recovery can be gained by adding a high resolution PET insert with smaller crystals to a conventional PET scanner. Such enhancements are obtained when the insert is placed in proximity of the region of interest (ROI) and in coincidence with the conventional PET scanner. Intuitively, the insert may be positioned within the scanner\u27s axial field-of-view (FOV) and radially closer to the ROI than the scanner\u27s ring. One of the complicating factors of this design is the insert\u27s blocking the scanner\u27s lines-of-response (LORs). Such data may be compensated through attenuation and scatter correction in image reconstruction. However, a potential solution is to place the insert outside of the scanner\u27s axial FOV and to move the body to be in proximity of the insert. We call this imaging strategy the surveillance mode. As the main focus of this work, we have developed an image reconstruction framework for the surveillance mode imaging. The preliminary results show improvement in spatial resolution and contrast recovery. Any improvement in contrast recovery should result in enhancement in tumor detectability, which will be of high clinical significance