Fast Virtual Stenting for Thoracic Endovascular Aortic Repair of Aortic Dissection Using Graph Deep Learning

Abstract

Fast virtual stenting (FVS) is a promising preoperative planning aid for thoracic endovascular aortic repair (TEVAR) of aortic dissection. It aims at digitally predicting the reshaped aortic true lumen (TL) under specific operation plans (stent-graft deployment region and radius) to assess and avoid reoperation risk, but has not yet been applied clinically due to the difficulty in achieving accurate and time-dependent predictions. In this work, we propose a deep-learning-based model for FVS to solve the above problems. It models the FVS task as a time-dependent prediction of inner wall (TL surface) deformation and leverages outer wall (entire aortic surface) to improve it. Two point clouds ($\text{PC}_{\text{iw}}$ and $\text{PC}_{\text{ow}}$) are generated to represent the walls, where patient information, operation plan, and post-operative time are set as the attributes of $\text{PC}_{\text{iw}}$. Afterwards, graphs are constructed based on the PCs and processed by a graph deep network to predict a point-wise inner wall deformation for generating the time-dependent reshaped TL. Our model successfully perceives and utilizes the virtual setting of operation plan and achieves the time-dependent predictions for 108 patients (269 real follow-up visits). Compared with the existing rule-based FVS model, it predicts the long-term reshaped TL with 9%, 5%, and 2% lower mean relative error of volume, surface area, and centerline length, respectively, and supports more accurate clinical measurements of poor outcome risk factors. Overall, our model may be of great significance for predicting reoperation risk, optimizing operation plan, and eventually improving the effectiveness and safety of TEVAR.