A Rigid Registration Method in TEVAR

TL;DR

This paper introduces a novel deep learning-based method for rigid registration of X-ray and CT images in TEVAR procedures, eliminating the need for auxiliary markers and improving clinical applicability.

Contribution

It proposes a direct image matching approach using deep features to estimate spatial correspondence without auxiliary devices, enhancing intra-interventional registration.

Findings

Achieves accurate registration meeting clinical needs

Improves speed of the registration process

Eliminates reliance on auxiliary markers

Abstract

Since the mapping relationship between definitized intra-interventional X-ray and undefined pre-interventional Computed Tomography(CT) is uncertain, auxiliary positioning devices or body markers, such as medical implants, are commonly used to determine this relationship. However, such approaches can not be widely used in clinical due to the complex realities. To determine the mapping relationship, and achieve a initializtion post estimation of human body without auxiliary equipment or markers, proposed method applies image segmentation and deep feature matching to directly match the X-ray and CT images. As a result, the well-trained network can directly predict the spatial correspondence between arbitrary X-ray and CT. The experimental results show that when combining our approach with the conventional approach, the achieved accuracy and speed can meet the basic clinical intervention needs, and it provides a new direction for intra-interventional registration.