Unsupervised Multimodal Image Registration with Adaptative Gradient Guidance

TL;DR

This paper introduces an unsupervised multimodal image registration method that uses adaptive gradient guidance to improve boundary alignment by fusing deformation fields from original images and their gradient maps.

Contribution

It proposes a novel framework that leverages gradient intensity maps and a gated fusion module to enhance boundary accuracy in multimodal registration.

Findings

Effective boundary alignment demonstrated on CT-MRI datasets

Outperforms existing unsupervised registration methods

Improves focus on organ boundaries during registration

Abstract

Multimodal image registration (MIR) is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. It is difficult for the networks to be aware of the mismatched boundaries, resulting in unsatisfactory organ boundary alignment. In this paper, we propose a novel multimodal registration framework, which leverages the deformation fields estimated from both: (i) the original to-be-registered image pair, (ii) their corresponding gradient intensity maps, and adaptively fuses them with the proposed gated fusion module. With the help of auxiliary gradient-space guidance, the network can concentrate more on the spatial relationship of the organ boundary. Experimental results on two clinically acquired CT-MRI datasets demonstrate the effectiveness of our proposed approach.