Arterial spin labelling : contrôle qualité et super-résolution

Arterial spin labelling (ASL) is a brain perfusion magnetic resonance imaging technique. The objective of this thesis was first to standardize ASL acquisitions in the context of multicenter neuroimaging studies. A quality control procedure has then been proposed. The capacity of existing algorithms to correct for distortions in ASL images has then been evaluated. Super-resolution methods, developed and adapted to single and multi-TI ASL data in the context of this thesis, are then described, and validated on simulated data, images acquired on healthy subjects, and on patients imaged for brain tumors.L'arterial spin labelling (ASL) est une technique d'imagerie par résonance magnétique de la perfusion cérébrale. Les travaux présentés dans cette thèse ont d'abord consisté à standardiser les acquisitions ASL dans le contexte d'études de neuro-imagerie multicentriques. Un processus de contrôle de la qualité des images a par la suite été proposé. Les travaux se sont ensuite orientés vers le post-traitement de données ASL, en évaluant la capacité d'algorithmes existants à y corriger les distorsions. Des méthodes de super-résolution adaptées aux acquisitions ASL mono et multi-TI ont finalement été proposées et validées sur des données simulées, de sujets sains, ou de patients imagés pour suspicion de tumeurs cérébrales

Arterial spin labelling : contrôle qualité et super-résolution

Arterial spin labelling (ASL) is a brain perfusion magnetic resonance imaging technique. The objective of this thesis was first to standardize ASL acquisitions in the context of multicenter neuroimaging studies. A quality control procedure has then been proposed. The capacity of existing algorithms to correct for distortions in ASL images has then been evaluated. Super-resolution methods, developed and adapted to single and multi-TI ASL data in the context of this thesis, are then described, and validated on simulated data, images acquired on healthy subjects, and on patients imaged for brain tumors.L'arterial spin labelling (ASL) est une technique d'imagerie par résonance magnétique de la perfusion cérébrale. Les travaux présentés dans cette thèse ont d'abord consisté à standardiser les acquisitions ASL dans le contexte d'études de neuro-imagerie multicentriques. Un processus de contrôle de la qualité des images a par la suite été proposé. Les travaux se sont ensuite orientés vers le post-traitement de données ASL, en évaluant la capacité d'algorithmes existants à y corriger les distorsions. Des méthodes de super-résolution adaptées aux acquisitions ASL mono et multi-TI ont finalement été proposées et validées sur des données simulées, de sujets sains, ou de patients imagés pour suspicion de tumeurs cérébrales

Recalage d'images du fond d'oeil pour la construction d'un atlas des structures anatomiques de la rétine

RÉSUMÉ Le développement de nouvelles modalités d'imagerie et les progrès réalisés en analyse et en archivage d'images ont fait évoluer la pratique de l'ophtalmologie ces dernières décennies. Ces avancées sont motivées par le fait que les images de la rétine, également appelées images de fond d'oeil, sont les seules qui permettent d'observer le réseau vasculaire rétinien de manière non-invasive et d'obtenir des informations concernant un grand nombre de pathologies. La quantité d'images rétiniennes acquises afin de suivre des patients atteints de différentes maladies est donc en constante augmentation, ce qui pose des problèmes dans la prise en charge de la population. De plus, l'évaluation qualitative de ces images par des experts est soumise à des problèmes de reproductibilité. De ces problématiques croît la nécessité de développer des méthodes automatiques d'analyse des images de fond d'oeil et d'aide au diagnostic. Certaines des pathologies évaluées à l'aide de ces images affectant la structure du réseau vasculaire rétinien, la segmentation automatique de la vasculature est l'un des enjeux particulièrement importants aujourd'hui. Diverses méthodes de segmentation de la vasculature rétinienne sont proposées dans la littérature, l'une des principales difficultés à surmonter étant de segmenter les vaisseaux sanguins les plus fins. Notre hypothèse est que l'utilisation d'atlas pourrait améliorer la détection de ces petits vaisseaux. Les atlas sont des modèles moyens de la répartition des structures au sein de populations qui, une fois alignés sur des images à segmenter, permettent de localiser les structures d'interêt. L'objectif général de ce projet est de proposer des méthodes de recalage automatique d'images de fonds d'oeil afin de construire un atlas des principales structures anatomiques de la rétine. Puisque les images de fonds d'oeil utilisées pour construire un tel atlas sont planes, leur contenu dépend de la configuration selon laquelle elles ont été imagées. Nous proposons donc aussi d'étudier la possibilité d'une reconstruction de la surface des rétines en trois dimensions afin d'obtenir un atlas 3D indépendant de cette configuration. Cette extension à la troisième dimension implique le recours à plusieurs images de chaque fond d'oeil qui doivent être recalées afin de ne conserver que les déformations liées à la profondeur de la rétine. Trois objectifs spécifiques sont donc à distinguer. Le premier consiste à proposer une méthode de recalage automatique intra-sujet pour permettre la reconstruction en trois dimensions. Le deuxième vise à adapter le recalage au cas de l'alignement d'images de sujets différents dans le but de construire l'atlas 2D, qui est utile dans le cas d'images acquises selon des configurations similaires. Le troisième objectif est d'étudier la possibilité de reconstruire----------ABSTRACT The development of new imaging modalities and the progress in image analysis and archiving systems have changed the practice of ophtalmology for the last decades. These advances are driven by the fact that retinal images, also known as fundus images, are the only ones allowing a non-invasive observation of the human vasculature and getting information about many pathologies. The amount of retinal images acquired to examinate patients with various diseases is constantly increasing, which poses problems in the management of the population. In addition, the qualitative evaluation of these images by experts is subject to problems of reproducibility. These issues increase the need to develop automatic retinal image analysis and diagnosis support. Some pathologies are aecting the structure of the retinal vasculature. Therefore, the automatic retinal vessels segmentation is a particularly important issue today. Various methods have been proposed in the literature concerning retinal vessels segmentation. One of the key challenges is to segment the nest blood vessels. Our hypothesis is that the use of atlases could improve the detection of these small vessels. Atlases mean the distribution of anatomical structures within populations and, once aligned with images to segment, allow the localisation of the structures of interest. The general objective of this project is to propose a method for automatic images registration to build an atlas of the principal anatomical structures of the retina. Since the fundus images used to build this atlas are two dimensional, their content depends on the conguration of their acquisition. We propose to study the possibility of reconstructing the retinal surface in three dimensions to obtain an atlas not depending on this conguration. This extension to the third dimension involves the use of multiple images of each retina which must be registered in order to keep only the deformations related to the depth of the retina. Thus, three specic objectives are to distinguish. The rst is to provide an automatic intra-subject registration method to allow the three-dimensional reconstruction. The second is to adapt the registration algorithm to the alignment of images of dierent subjects in order to build the 2D atlas, which is useful in the case of images acquired under similar congurations. The third objective is to study the possibility to reconstruct the retinal surface in three dimensions. To meet the rst objective, which is to propose a method to automatically register images of a single retina, an algorithm evaluating the quadratic deformations between the images has been developed. It allows taking into account transformations introduced by the movements of the eye relative to the camera between several acquisitions and considering deformation

Patch-based super-resolution for arterial spin labeling MRI

International audiencen

Patch-based super-resolution for arterial spin labeling MRI

International audiencen

Patch-Based Super-Resolution of Arterial Spin Labeling Magnetic Resonance Images

International audienceArterial spin labeling is a magnetic resonance perfusion imaging technique that, while providing results comparable to methods currently considered as more standard concerning the quantification of the cerebral blood flow, is subject to limitations related to its low signal-to-noise ratio and low resolution. In this work, we investigate the relevance of using a non-local patch-based super-resolution method driven by a high resolution structural image to increase the level of details in arterial spin labeling images. This method is evaluated by comparison with other image dimension increasing techniques on a simulated dataset, on images of healthy subjects and on images of subjects diagnosed with brain tumors, who had a dynamic susceptibility contrast acquisition. The influence of an increase of ASL images resolution on partial volume effects is also investigated in this work

A study on loss functions and decision thresholds for the segmentation of multiple sclerosis lesions on spinal cord MRI

Multiple sclerosis (MS) patients often present hyper-intense T2-w lesions in the spinal cord. The severe imbalance between background and lesion classes poses a major challenge to Deep Learning segmentation approaches, requiring for ad hoc strategies. Careful selection of the loss function and adjustment of the conventional 0.5-thresholding may help mitigating this issue. Our results show the performance advantages of loss functions based on the Tversky Index and the benefits of threshold tuning over more standard settings and the state-of-the-art model for MS lesion segmentation on spinal cord MRI

Expert Variability and Deep Learning Performance in Spinal Cord Lesion Segmentation for Multiple Sclerosis Patients

Accepted at 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS).© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.International audienceMultiple sclerosis (MS) patients often present with lesions in spinal cord magnetic resonance (MR) volumes. However, accurately detecting these lesions is challenging and prone to inter-and intra-rater variability. Deep learning-based methods have the potential to aid clinicians in detecting and segmenting MS lesions, but can also be affected by rater variability. This study assesses the inter-and intra-rater variability in manual segmentation of spinal cord lesions, and evaluates raters and a state-of-the-art nnU-Net model against a ground truth (GT) segmentation of a senior expert. Four experts segmented twelve spinal cord MR volumes from six patients twice, at a time distance of two weeks. Considerable inter-and intra-rater variability were observed, with the total number of detected lesions ranging from 28 to 60, depending on the rater. Moreover, the segmented volumes of individual lesions varied substantially between raters. All raters and the model achieved high precision when evaluated against the senior expert GT, but sensitivity was notably lower. These results motivate the need for more sensitive automated methods to aid clinicians in lesion detection, and suggest that consideration should be given to inter-rater variability when training and evaluating automated methods