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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., Competing Interests: The authors have declared that no competing interests exist.

Objective: The aim of our study was to evaluate the impact of aortic root replacement by graft on the elastic properties of the descending thoracic aorta using cardiac magnetic resonance imaging (MRI) and automatic post-processing., Materials and Methods: Nineteen patients were operated for an aortic root aneurysm. Cardiac MRI was performed before and after surgery to measure aortic compliance. Images were acquired on a 1.5 T MRI with a conventional aortic MRI protocol plus one additional kinetic sequence orientated perpendicularly to the aorta at the level of pulmonary trunk. The contours of the ascending and descending aortas were detected automatically for each phase with homemade software., Results: Mean time between surgical procedure and earliest post-operative MRI was 18.2 ± 7.1 months. There was no significant difference between the pre- and earliest post-operative mean descending aorta areas and no significant modification in descending aortic compliance after aortic root replacement (1.43 ± 0.84 vs 1.37 ± 0.58 mm 2 /mmHg, p = 0.47). Pre- and post-operative systolic and diastolic blood pressure were similar. There was a significant decrease in ascending aortic compliance after surgery (2.52 ± 1.24 vs 0.91 ± 0.52 mm 2 /mmHg; p < 0.0001)., Discussion: The aortic root replacement by graft was not associated with changes in elastic properties of the descending aorta at short term., Clinical Registration Number: NCT03817008.

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. Methods: Automatic segmentation of the aorta was performed with methods based on deep learning and multi-atlas using the systolic phase in the 4D flow MRI magnitude image of 36 patients. Using mesh morphing, isotopological meshes were generated, and CFD was performed to calculate the aortic wall pressure. Node-to-node comparisons of the pressure results were made to identify the most robust automatic method respect to the pressures obtained with a manually segmented model. Results: Deep learning approach presented the best segmentation performance with a mean Dice similarity coefficient and a mean Hausdorff distance (HD) equal to 0.92+/− 0.02 and 21.02+/− 24.20 mm, respectively. At the global level HD is affected by the performance in the abdominal aorta. Locally, this distance decreases to 9.41+/− 3.45 and 5.82+/− 6.23 for the ascending and descending thoracic aorta, respectively. Moreover, with respect to the pressures from the manual segmentations, the differences in the pressures computed from deep learning were lower than those computed from multi-atlas method. Conclusion: To reduce biases in the calculation of aortic wall pressure, accurate segmentation is needed, particularly in regions with high blood flow velocities. Thus, the deep learning segmen-tation method should be preferred. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 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. [ABSTRACT FROM AUTHOR]

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]

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. Equi-biaxial tensile tests of AsAA walls were performed on freshly sampled aortic wall tissue after ascending aortic replacement. The aneurysmal aortic walls were divided into four quadrants (medial, anterior, lateral, and posterior) and two directions (longitudinal and circumferential) were considered. The stiffness was represented by the maximum Young modulus (MYM). Based on patient information, the following subgroups were considered: age, gender, hypertension, obesity, dyslipidemia, diabetes, smoking history, aortic insufficiency, aortic stenosis, coronary artery disease, aortic diameter and aortic valve type. In general, when the aortic diameter increased, the aortic wall became thicker. In terms of the MYM, the longitudinal direction was significantly higher than that in the circumferential direction. In the multivariant analysis, the impact factors of age (p = 0.07), smoking (p = 0.05), diabetes (p = 0.03), aortic stenosis (p = 0.02), coronary artery disease (p < 10−3), and aortic diameters (p = 0.02) were significantly influencing the MYM. There was no significant MYM difference when the patients presented arterial hypertension, dyslipidemia, obesity, or bicuspid aortic valve. To conclude, the pre-failure aortic stiffness is multi-factorial, according to our population of 100 patients with AsAA. Our research on the topic of "Aortic local biomechanical properties in case of ascending aortic aneurysms" is about the biomechanical properties on one hundred aortic samples according to the aortic wall quadrants and the direction. More than ten factors and risks which may impact ascending aortic aneurysms have been studied. According to our knowledge, so far, this article involved the largest population on this topic. It will be our pleasure to share this information with all the readers. [Display omitted] [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]

Objective: The aim of our study was to evaluate the impact of aortic root replacement by graft on the elastic properties of the descending thoracic aorta using cardiac magnetic resonance imaging (MRI) and automatic post-processing. Materials and methods: Nineteen patients were operated for an aortic root aneurysm. Cardiac MRI was performed before and after surgery to measure aortic compliance. Images were acquired on a 1.5 T MRI with a conventional aortic MRI protocol plus one additional kinetic sequence orientated perpendicularly to the aorta at the level of pulmonary trunk. The contours of the ascending and descending aortas were detected automatically for each phase with homemade software. Results: Mean time between surgical procedure and earliest post-operative MRI was 18.2 ± 7.1 months. There was no significant difference between the pre- and earliest post-operative mean descending aorta areas and no significant modification in descending aortic compliance after aortic root replacement (1.43 ± 0.84 vs 1.37 ± 0.58 mm2/mmHg, p = 0.47). Pre- and post-operative systolic and diastolic blood pressure were similar. There was a significant decrease in ascending aortic compliance after surgery (2.52 ± 1.24 vs 0.91 ± 0.52 mm2/mmHg; p < 0.0001). Discussion: The aortic root replacement by graft was not associated with changes in elastic properties of the descending aorta at short term. Clinical registration number: NCT03817008. [ABSTRACT FROM AUTHOR]

Keywords: Biaxial tensile test; ascending aortic aneurysms; youth modulus EN Biaxial tensile test ascending aortic aneurysms youth modulus S413 S414 2 01/29/21 20191002 NES 191002 1. Introduction An aneurysm of the ascending aorta (AsAA) can progressively evolve over years and can involve an aortic dissection and/or rupture. Methods Pathologic ascending aortic tissue samples (n = 20) were obtained from patients undergoing elective surgical repair of AsAA. [Extracted from the article]

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]