4D-CAT: Synthesis of 4D Coronary Artery Trees from Systole and Diastole

TL;DR

This paper introduces a novel method to synthesize 4D coronary artery models from limited phase imaging by predicting deformation fields and interpolating vessel motion, aiding in more accurate cardiac diagnostics.

Contribution

The method uniquely combines deformation field prediction with neural networks to generate continuous 4D coronary artery models from sparse imaging phases.

Findings

Successfully registers non-rigid vascular points

Generates accurate 4D coronary artery trees

Validates with experimental results

Abstract

The three-dimensional vascular model reconstructed from CT images is widely used in medical diagnosis. At different phases, the beating of the heart can cause deformation of vessels, resulting in different vascular imaging states and false positive diagnostic results. The 4D model can simulate a complete cardiac cycle. Due to the dose limitation of contrast agent injection in patients, it is valuable to synthesize a 4D coronary artery trees through finite phases imaging. In this paper, we propose a method for generating a 4D coronary artery trees, which maps the systole to the diastole through deformation field prediction, interpolates on the timeline, and the motion trajectory of points are obtained. Specifically, the centerline is used to represent vessels and to infer deformation fields using cube-based sorting and neural networks. Adjacent vessel points are aggregated and interpolated based on the deformation field of the centerline point to obtain displacement vectors of different phases. Finally, the proposed method is validated through experiments to achieve the registration of non-rigid vascular points and the generation of 4D coronary trees.