Deep Learning Assisted Automatic Intra-operative 3D Aortic Deformation Reconstruction

Abstract

Endovascular interventions rely on 2D X-ray fluoroscopy for 3D catheter manipulation. The dynamic nature of aorta prevents the pre-operative CT/MRI data to be used directly as the live 3D guidance since the vessel deforms during the surgery. This paper provides a framework that reconstructs the live 3D aortic shape by fusing a 3D static pre-operative model and the 2D intra-operative fluoroscopic images. The proposed framework recovers aortic 3D shape automatically and computationally efficient. A deep learning approach is adopted as the front-end for extracting features from fluoroscopic images. A signed distance field based correspondence method is employed for avoiding the repeated feature-vertex matching while maintaining the correspondence accuracy. The warp field of 3D deformation is estimated by solving a non-linear least squares problem based on the embedded deformation graph. Detailed phantom experiments are conducted, and the results demonstrate the accuracy of the proposed framework as well as the potential clinical value of the technique.