Aortic 3D Deformation Reconstruction using 2D X-ray Fluoroscopy and 3D Pre-operative Data for Endovascular Interventions

Abstract

Current clinical endovascular interventions rely on 2D guidance for catheter manipulation. Although an aortic 3D surface is available from the pre-operative CT/MRI imaging, it cannot be used directly as a 3D intra-operative guidance since the vessel will deform during the procedure. This paper aims to reconstruct the live 3D aortic deformation by fusing the static 3D model from the pre-operative data and the 2D live imaging from fluoroscopy. In contrast to some existing deformation reconstruction frameworks which require 3D observations such as RGB-D or stereo images, fluoroscopy only presents 2D information. In the proposed framework, a 2D-3D registration is performed and the reconstruction process is formulated as a non-linear optimization problem based on the deformation graph approach. Detailed simulations and phantom experiments are conducted and the result demonstrates the reconstruction accuracy and robustness, as well as the potential clinical value of this framework.

Deformable Model-to-Image Registration Toward Augmented Reality-Guided Endovascular Interventions

“…Existing image registration methods to reconstruct vascular deformation can be broadly categorized into optimizationbased approaches, which rely on iterative optimization processes, and learning-based approaches that leverage neural networks. Zhang et al [11] proposed a method to reconstruct a deformed intra-operative 3D aortic model using a pre-operative 3D model and intra-operative fluoroscopy images. They formulated the deformation estimation process as a non-linear optimization problem based on the deformation graph approach, utilizing the comparison between pre-operative model projection contours and intra-operative segmented aortic shape contours.…”