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Ascending aortic aneurysm is a pathology that is important to be supervised and treated. During the years the aorta dilates, it becomes stiff, and its elastic properties decrease. In some cases, the aortic wall can rupture leading to aortic dissection with a high mortality rate. The main reference standard to measure when the patient needs to undertake surgery is the aortic diameter. However, the aortic diameter was shown not to be sufficient to predict aortic dissection, implying other characteristics should be considered. Therefore, the main objective of this work is to assess in-vivo the elastic properties of four different quadrants of the ascending aorta and compare the results with equivalent properties obtained ex-vivo. The database consists of 73 cine-MRI sequences of thoracic aorta acquired in axial orientation at the level of the pulmonary trunk. All the patients have dilated aorta and surgery is required. The exams were acquired just prior to surgery, each consisting of 30 slices on average across the cardiac cycle. Multiple deep learning architectures have been explored with different hyperparameters and settings to automatically segment the contour of the aorta on each image and then automatically calculate the aortic compliance. A semantic segmentation U-Net network outperforms the rest explored networks with a Dice score of 98.09% (±0.96%) and a Hausdorff distance of 4.88 mm (±1.70 mm). Local aortic compliance and local aortic wall strain were calculated from the segmented surfaces for each quadrant and then compared with elastic properties obtained ex-vivo. Good agreement was observed between Young's modulus and in-vivo strain. Our results suggest that the lateral and posterior quadrants are the stiffest. In contrast, the medial and anterior quadrants have the lowest aortic stiffness. The in-vivo stiffness tendency agrees with the values obtained ex-vivo. We can conclude that our automatic segmentation method is robust and compatible with clinical practice (thanks to a graphical user interface), while the in-vivo elastic properties are reliable and compatible with the ex-vivo ones. [ABSTRACT FROM AUTHOR]

Association of quadricuspid aortic valve (QAV) with ascending aortic aneurysms (AsAA) is rare. A 63-year-old female with hypertension was found (on MRI) to have an ascending aortic aneurysm (52 mm in maximum diameter) and dilatation at the level of the sinotubular junction (38 mm in diameter) associated with quadricuspid aortic valve. An ascending aortic wall replacement surgery was performed. In this study, we focus on the behavior of the aorta associated with QAV considering the in vitro biomechanical characteristics and histology. The properties of QAV are closer to bicuspid aortic valve than tricuspid aortic valve, but with higher wall thickness. [ABSTRACT FROM AUTHOR]

Introduction: 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. [ABSTRACT FROM AUTHOR]

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. To develop an automatic deep learning model to segment the aorta in 4D from 4D flow MRI. Segmentation is addressed with a U-Net based segmentation model that treats each 4D flow MRI frame as an independent sample. Performance is measured with respect to Dice score (DS) and Hausdorff distance (HD). In addition, the maximum and minimum surface areas at the level of the ascending aorta are measured and compared with those obtained from cine-MRI. The segmentation performance was 0. 90 ± 0. 02 for the DS and the mean HD was 9. 58 ± 4. 36 mm. A correlation coefficient of r = 0.85 was obtained for the maximum surface and r = 0.86 for the minimum surface between the 4D flow MRI and cine-MRI. The proposed automatic approach of 4D aortic segmentation from 4D flow MRI seems to be accurate enough to contribute to the wider use of this imaging technique in the analysis of pathologies such as TAA. [ABSTRACT FROM AUTHOR]