Segmentation de l'aorte à partir d'IRM en flux 4D pour le calcul de biomarqueurs

Aortic dissection and aortic aneurysms are highly lethal pathologies. The latter is ranked as the nineteenth leading cause of death. Aortic aneurysms are defined as a dilatation greater than or equal to the diameter of the aorta. In clinical practice, the growth rate and size of the aneurysm are the main criteria for determining surgical intervention. However, new and better biomarkers of rupture and aortic dissection are needed for personalized treatment and better decision making. In this regard, the analysis of the interaction of blood flow with aortic tissues is a key factor. In recent years, 4D flow magnetic resonance imaging (MRI) has made it possible to acquire information on blood flow throughout the cardiac cycle. Despite the potential of this imaging modality, a previous 4D segmentation step is required to delineate the analysis of the fluid-structure interaction.The aim of this thesis is to automatically segment the aorta from 4D flow MRI and to analyze the feasibility of using these automatic segmentations in the generation of biomarkers such as aortic wall pressure. To this end, we initially evaluated automatic 3D systolic phase segmentation with state-of-the-art methods such as multi-atlas-based and deep learning-based methods. With this study we have shown that deep learning outperforms the segmentation performance of the multi-atlas-based method. Furthermore, it was observed that biomarkers such as aortic wall pressure are more robust when using automatic segmentations from deep learning. Consequently, two deep-learning-based approaches were proposed for aortic segmentation during the complete cardiac cycle. With the analysis of the performance of the 4D segmentation, promising results were obtained and must be confirmed on databases from other hospitals.Parmi les maladies cardiaques, la dissection aortique et les anévrismes aortiques sont des pathologies particulièrement létales. Cette dernière est classée comme la dix-neuvième cause de décès. Les anévrismes aortiques sont définis comme une dilatation supérieure ou égale au diamètre de l'aorte. Dans la pratique clinique, le taux de croissance et la taille de l'anévrisme constituent les principaux critères pour déterminer la nécessité d'une intervention chirurgicale.Cependant, de meilleurs biomarqueurs de rupture et de dissection aortique sont nécessaires afin d'effectuer un traitement personnalisé et une meilleure prise de décision. À cet égard, l'analyse de l'interaction entre le flux sanguin et les tissus aortiques est un facteur clé. Ces dernières années, l'IRM de flux 4D a permis d'acquérir des informations sur le flux sanguin tout au long du cycle cardiaque. La première étape pour profiter du potentiel de cette modalité d'imagerie est l'élaboration d'une méthode de segmentation 4D de l'aorte, permettant d'effectuer à des analyses de l'interaction fluide-structure.L'objectif de cette thèse est de segmenter automatiquement l'aorte à partir de l'IRM de flux 4D et d'analyser la faisabilité de l'utilisation de ces segmentations automatiques dans la génération de biomarqueurs tels que la pression de la paroi aortique. À cette fin, nous avons d'abord évalué la segmentation automatique de la phase systolique en 3D avec des méthodes de pointe, telles que la segmentation basée sur des méthodes d'apprentissage profond. Avec cette étude il a été démontré que l'apprentissage profond surpasse la performance de segmentation de méthodes basés sur l'utilisation de multi-atlas. En outre, il a été observé que les biomarqueurs tels que la pression de la paroi aortique sont plus robustes lorsqu'on utilise des segmentations automatiques par apprentissage profond. Par conséquent, deux approches basées sur l'apprentissage en profondeur ont été proposées pour la segmentation aortique au cours du cycle cardiaque complet. Avec l'analyse des performances de la segmentation 4D, des résultats prometteurs ont été obtenus et doivent être confirmés sur des bases de données d'autres hôpitaux

Comparison of two techniques (in vivo and ex-vivo) for evaluating the elastic properties of the ascending aorta: Prospective cohort study

International audienceIntroduction Aneurysms of the ascending aorta (AA) correspond to a dilatation of the ascending aorta that progressively evolves over several years. The main complication of aneurysms of the ascending aorta is type A aortic dissection, which is associated with very high rates of morbidity and mortality. Prophylactic ascending aorta replacement guidelines are currently based on maximal AA diameter. However, this criterion is imperfect. Stretching tests on the aorta carried out ex-vivo make it possible to determine the elastic properties of healthy and aneurysmal aortic fragments (tension test, resistance before rupture). For several years now, cardiac magnetic resonance imaging (MRI) has provided another means of evaluating the elastic properties of the aorta. This imaging technique has the advantage of being non-invasive and of establishing aortic compliance (local measurement of stiffness) without using contrast material by measuring the variation of the aortic surface area during the cardiac cycle, and pulse wave velocity (overall stiffness of the aorta). Materials and methods Prospective single-center study including 100 patients with ascending aortic aneurysm requiring surgery. We will perform preoperative cine-MRI and biomechanical laboratory stretching tests on aortic samples collected during the cardiac procedure. Images will be acquired with a 3T MRI with only four other acquisitions in addition to the conventional protocol. These additional sequences are a Fast Low Angle Shot (FLASH)-type sequence performed during a short breath-hold in the transverse plane at the level of the bifurcation of the pulmonary artery, and phase-contrast sequences that encodes velocity at the same localization, and also in planes perpendicular to the aorta at the levels of the sino-tubular junction and the diaphragm for the descending aorta. For ex-vivo tests, the experiments will be carried out by a biaxial tensile test machine (ElectroForce®). Each specimen will be stretched with 10 times of 10% preconditioning and at a rate of 10 mm/min until rupture. During the experiment, the tissue is treated under a 37°C saline bath. The maximum elastic modulus from each sample will be calculated. Results The aim of this study is to obtain local patient-specific elastic modulus distribution of the ascending aorta from biaxial tensile tests and to assess elastic properties of the aorta using MRI, then to evaluate the correlation between biaxial tests and MRI measurements. Discussion Our research hypothesis is that there is a correlation between the evaluation of the elastic properties of the aorta from cardiac MRI and from stretching tests performed ex-vivo on aorta samples collected during ascending aorta replacement

Multi-atlas Segmentation of the Aorta from 4D Flow MRI: Comparison of Several Fusion Strategies

International audienc

Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets

A thoracic aortic aneurysm is an abnormal dilatation of the aorta that can progress and lead to rupture. The decision to conduct surgery is made by considering the maximum diameter, but it is now well known that this metric alone is not completely reliable. The advent of 4D flow magnetic resonance imaging has allowed for the calculation of new biomarkers for the study of aortic diseases, such as wall shear stress. However, the calculation of these biomarkers requires the precise segmentation of the aorta during all phases of the cardiac cycle. The objective of this work was to compare two different methods for automatically segmenting the thoracic aorta in the systolic phase using 4D flow MRI. The first method is based on a level set framework and uses the velocity field in addition to 3D phase contrast magnetic resonance imaging. The second method is a U-Net-like approach that is only applied to magnitude images from 4D flow MRI. The used dataset was composed of 36 exams from different patients, with ground truth data for the systolic phase of the cardiac cycle. The comparison was performed based on selected metrics, such as the Dice similarity coefficient (DSC) and Hausdorf distance (HD), for the whole aorta and also three aortic regions. Wall shear stress was also assessed and the maximum wall shear stress values were used for comparison. The U-Net-based approach provided statistically better results for the 3D segmentation of the aorta, with a DSC of 0.92 ± 0.02 vs. 0.86 ± 0.5 and an HD of 21.49 ± 24.8 mm vs. 35.79 ± 31.33 mm for the whole aorta. The absolute difference between the wall shear stress and ground truth slightly favored the level set method, but not significantly (0.754 ± 1.07 Pa vs. 0.737 ± 0.79 Pa). The results showed that the deep learning-based method should be considered for the segmentation of all time steps in order to evaluate biomarkers based on 4D flow MRI

Assessment of shape-based features ability to predict the ascending aortic aneurysm growth

International audienceThe current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

International audienceObjective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict the ascending aortic aneurysm growth. Material and methods: 70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified. Results: the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth. Conclusion: global shape features might provide an important contribution for predicting the aneurysm growth