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.

Biomechanical properties of 3D printable material usable for synthetic personalized healthy human aorta.

With the development of three-dimensional (3D) printing, 3D-printed products have been widely used in medical fields, such as plastic surgery, orthopedics, dentistry, . In cardiovascular research, 3D-printed models are becoming more realistic in shape. However, from a biomechanical point of view, only a few studies have explored printable materials that can represent the properties of the human aorta.

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

Objective: 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.

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

The 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 , are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment.

Segmentation of the aorta in systolic phase from 4D flow MRI: multi-atlas vs. deep learning.

Objective: In the management of the aortic aneurysm, 4D flow magnetic resonance Imaging provides valuable information for the computation of new biomarkers using computational fluid dynamics (CFD). However, accurate segmentation of the aorta is required. Thus, our objective is to evaluate the performance of two automatic segmentation methods on the calculation of aortic wall pressure.

4D segmentation of the thoracic aorta from 4D flow MRI using deep learning.

Background: 4D flow MRI allows the analysis of hemodynamic changes in the aorta caused by pathologies such as thoracic aortic aneurysms (TAA). For personalized management of TAA, new biomarkers are required to analyze the effect of fluid structure iteration which can be obtained from 4D flow MRI. However, the generation of these biomarkers requires prior 4D segmentation of the aorta.

Aortic local biomechanical properties in ascending aortic aneurysms.

Ascending aortic aneurysm (AsAA) is a high-risk cardiovascular disease with an increased incidence over years. In this study, we compared different risk factors based on the pre-failure behavior (from a biomechanical point of view) obtained ex-vivo from an equi-biaxial tensile test. A total of 100 patients (63 ± 12 years, 72 males) with AsAA replacement, were recruited.