Tomographie par Émission de positron à faible dose par échantillonnage d’Images et apprentissage automatique

Cette thèse étudie le problème de dose de radiation dans les études de Tomographie par Émission de Positons (PET). Trois aspects du PET-scan sont analysées. La première partie de cette thèse est dédiée à la technologie PET-scan. Deux techniques sont développées pour le PET-scan à faible dose : l’AR-PET. Une première stratégie de sélection et de placement de photomultiplicateurs est proposée, augmentant la résolution énergétique. Une technique de localisation d’impacts des photons gamma dans les cristaux solides de scintillation est développé. Cette technique est basée sur des réseaux de neurones artificiels et sur une acquisition unique de champ. Nous montrons qu’une augmentation de la sensibilité du détecteur est obtenue. Dans la deuxième partie de cette thèse, la reconstruction de l’image PET avec l’aide de maillages est étudiée. Un algorithme de reconstruction qui utilise une série de maillages 2D pour décrire la distribution 3D du radiotraceur est proposé, résultant en une diminution du nombre de points d’échantillonnage et rendant possible l’optimisation et la parallélisation des maillages. Enfin, la génération de l’image d’atténuation au moyen de réseaux de neurones artificiels profonds est explorée. L’apprentissage du réseau de neurones se fait à travers une transformation d’images PET FDG sans correction d’atténuation pour produire une image de tomodensitométrie (CT) synthétique. La conclusion des travaux de cette thèse pose la base pour l’usage de PET-scan à bas coût et a faible dose, via l’usage d’une image d’atténuation artificielle.This thesis explores the reduction of the patient radiation dose in screening Positron Emission Tomography (PET) studies. It analyses three aspects of PET imaging, which can reduce the patient dose: the data acquisition, the image reconstruction and the attenuation map generation. The first part of the thesis is dedicated to the PET scanner technology. Two optimization techniques are developed for a novel low-cost and low-dose scanner, the AR-PET scanner. First a photomultiplier selection and placement strategy is created, improving the energy resolution. The second work focuses on the localization of gamma events on solid scintillation crystals. The method is based on neural networks and a single flood acquisition, resulting in an increased detector’s sensitivity. In the second part, the PET image reconstruction on mesh support is studied. A mesh-based reconstruction algorithm is proposed which uses a series of 2D meshes to describe the 3D radiotracer distribution. It is shown that with this reconstruction strategy the number of sample points can be reduced without loosing accuracy and enabling parallel mesh optimization. Finally the attenuation map generation using deep neural networks is explored. A neural network is trained to learn the mapping from non attenuation corrected FDG PET images to a synthetic Computerized Tomography. With these approaches, this thesis lays a base for a low-cost and low-dose PET screening system, dispensing the need of a computed tomography image in exchange of an artificial attenuation map

Tomographie par Émission de positron à faible dose par échantillonnage d’Images et apprentissage automatique

This thesis explores the reduction of the patient radiation dose in screening Positron Emission Tomography (PET) studies. It analyses three aspects of PET imaging, which can reduce the patient dose: the data acquisition, the image reconstruction and the attenuation map generation. The first part of the thesis is dedicated to the PET scanner technology. Two optimization techniques are developed for a novel low-cost and low-dose scanner, the AR-PET scanner. First a photomultiplier selection and placement strategy is created, improving the energy resolution. The second work focuses on the localization of gamma events on solid scintillation crystals. The method is based on neural networks and a single flood acquisition, resulting in an increased detector’s sensitivity. In the second part, the PET image reconstruction on mesh support is studied. A mesh-based reconstruction algorithm is proposed which uses a series of 2D meshes to describe the 3D radiotracer distribution. It is shown that with this reconstruction strategy the number of sample points can be reduced without loosing accuracy and enabling parallel mesh optimization. Finally the attenuation map generation using deep neural networks is explored. A neural network is trained to learn the mapping from non attenuation corrected FDG PET images to a synthetic Computerized Tomography. With these approaches, this thesis lays a base for a low-cost and low-dose PET screening system, dispensing the need of a computed tomography image in exchange of an artificial attenuation map.Cette thèse étudie le problème de dose de radiation dans les études de Tomographie par Émission de Positons (PET). Trois aspects du PET-scan sont analysées. La première partie de cette thèse est dédiée à la technologie PET-scan. Deux techniques sont développées pour le PET-scan à faible dose : l’AR-PET. Une première stratégie de sélection et de placement de photomultiplicateurs est proposée, augmentant la résolution énergétique. Une technique de localisation d’impacts des photons gamma dans les cristaux solides de scintillation est développé. Cette technique est basée sur des réseaux de neurones artificiels et sur une acquisition unique de champ. Nous montrons qu’une augmentation de la sensibilité du détecteur est obtenue. Dans la deuxième partie de cette thèse, la reconstruction de l’image PET avec l’aide de maillages est étudiée. Un algorithme de reconstruction qui utilise une série de maillages 2D pour décrire la distribution 3D du radiotraceur est proposé, résultant en une diminution du nombre de points d’échantillonnage et rendant possible l’optimisation et la parallélisation des maillages. Enfin, la génération de l’image d’atténuation au moyen de réseaux de neurones artificiels profonds est explorée. L’apprentissage du réseau de neurones se fait à travers une transformation d’images PET FDG sans correction d’atténuation pour produire une image de tomodensitométrie (CT) synthétique. La conclusion des travaux de cette thèse pose la base pour l’usage de PET-scan à bas coût et a faible dose, via l’usage d’une image d’atténuation artificielle

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset