# Atlas-Based Evaluation of Hemodynamic in Ascending Thoracic Aortic Aneurysms
## 4 Discussion  
This study proposed an atlas-based disease assessment by means of computational flow analysis from statistical shape analysis of ATAAs. Using a large number of patients' CT scans, we developed two SSMs according to the type of the aortic valve morphology to determine the mean shape and its geometrical variation likely caused by the complex nature of the aneurysm disease. Then, the ATAA-related atlases were virtually deformed into possible aneurysm configurations by applying shape deformation to quantify the hemodynamic of critical ATAA geometries. Results highlighted that principal shape modes like the aneurysm size and tortuosity are linked to the flow parameters.. Among these, the systolic WSS exhibited the largest difference between low to high shape deviations. The atlas-based disease evaluation here proposed has demonstrated the ability to reveal the hemodynamic impairment induced by specific aneurysm features and aortic valve phenotypes (i.e., the BAV versus TAV phenotype). This approach can reveal important insights on the aneurysm hemodynamic, especially for patients who are considered to have borderline aneurysm anatomies, and thus challenging clinical decision-making. BAV and hypertension are common predisposing factors to aneurysm development and progression [2]. The larger the blood pressure is, the higher the intramural stress exerted on the aortic wall. In a different way, bicuspid morphologies can determine hemodynamic disturbances on the diseased aortic wall with differences occurring for the anterior-posterior valve fusion versus the right-left cusp fusion [32]. Several investigations  
Figure 8: Pressure (**top**), flow velocity (**center**), and WSS (**bottom**) for BAV and TAV ATAA models for Mode 3 at \(\pm 1.5\)\(\sigma\). including computational fluid dynamics [19,33], fluid-solid interaction analysis [34], and 4D flow MRI [32,35] have clearly demonstrated the value of the WSS parameter on the weakening of the ATAA wall when combined with biomarker assessment [23,36]. Moreover, clinical evidence has demonstrated varying degrees of aortic dilatation patterns, each with distinct clinical course [37]. This heterogenous manifestation of the aneurysm disease and progression has driven the research of many investigators that developed new classification schemes based on the patterns of ATAA shape (i.e., the tubular dilated aorta versus the aortic root phenotype) and local diameter measurements taken at several anatomic levels of the ascending aorta [38,39]. However, none of the proposed classification schemes showed good prognostic significance because failed to fully capture the 3D configuration of the aneurysmal aorta. Using the atlas-based evaluation of aneurysm disease proposed here, we can observe shape features at a level of detail hitherto impossible with conventional diagnostic imaging. Once the SSM atlas is computed, the impact of morphological features on the resulting hemodynamic parameters can be obtained, deforming the dilated aorta and its valve for developing realistic models built upon real patient geometries. Such an approach has the potential to generate large cohorts of ATAA models that can be used to predict the biomechanical response of the aneurysmal aorta using deep learning techniques, as recently reported by [PERSON] and colleagues [40]. Shape modes in the current SSM were related to computational flow analyses obtained by varying each shape mode upon \(\pm\)1.5 \(\sigma\) of the mean template. While Modes 1 and 2 (accounting for nearly 70% of total shape variation) represented changes in the ascending aortic geometry, the remaining modes were associated with alterations in the aortic valve morphologies, including the orientation, rotation, and size of the aortic valve. These findings corroborate the PCA-based analysis developed by [PERSON] et al. [41] for the healthy aorta. They reported that the main shape contributors are vessel size, aortic arch unfolding, and symmetry.