An artificial intelligence-based platform for automatically estimating time-averaged wall shear stress in the ascending aorta

Figures

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  Figure 1. The architecture of the platform. The purpose of this platform is to output the time-averaged wall shear stress values and distribution of the ascending aorta wall in the original computed tomography angiography image. Briefly, the network consists of two parts. The first part is automatic extraction of the aortic wall, and the coordinate information for each point in the point cloud (right) is retrieved from the vessel wall (left). The second is the time-averaged wall shear stress automatic estimation platform. After extracting the coordinate information of ascending aorta wall from the first part, we carried out the time-averaged wall shear stress estimation of each point through the PointNet-based deep learning algorithm, and finally the time-averaged wall shear stress distribution and values of ascending aorta are output. CTAs, computed tomography angiographies; TAWSS, time-averaged wall shear stress.

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  Figure 2. The process of computational fluid dynamics analysis and time-averaged wall shear stress calculation. Shortly, we first labelled the mask of aorta, containing aortic root, aortic sinus, sinotubular junction, ascending aorta, arch, descending aorta and main branches (B) from raw computed tomography angiography (A). Then ascending aortas were cropped, smoothed and refined (C). Tetrahedral meshes and six boundary-fitted prism layers for every case were generated (D). And boundary conditions were assigned to each case (E). Root mean ssquare was used to monitor the calculation process (F). Only when the root mean square value for a timestep was less than 10−6 can we carry out the calculation for the next timestep. Lastly, the calculation results, including time-averaged wall shear stress, wall shear stress, and so on, were obtained after post-processing (G). CFD, computational fluid dynamics; CTA, computed tomography angiographies; RMS, root mean square; STJ, sinotubular junction; TAWSS, time-averaged wall shear stress; WSS, wall shear stress.

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  Figure 3. The results of ascending aorta automatic segmentation. The first line shows the raw computed tomography angiography of ascending aorta, and we manually labelled the wall of aorta (the second line). After training, the mask of aorta (the third line) and the aortic wall (the last line) can be extracted automatically. CTAs, computed tomography angiographies.

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  Figure 4. The results of time-averaged wall shear stress estimation platform. The figure shows the estimated results of patients with three different states. (A) A healthy volunteer (diameter of aorta = 32 mm). (B) An aneurysm patient (diameter = 47 mm), and (C) a patient with significant aortic sinus (56 mm) and aorta (52 mm) dilatation with Marfan syndrome. The first column was the ground truth calculated from computational fluid dynamics. The second column was the results of this platform, both distribution and values of which had a good consistency with the ground truth. The third column was the error of each estimation. The final column had the Bland–Altman plots, reflecting the WSS derived using both CFD and deep learning techniques. This model had a high accuracy in time-averaged wall shear stress estimation along with various aortic diseases. CFD, computational fluid dynamics; TAWSS, time-averaged wall shear stress.