A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint

TL;DR

This paper introduces a novel unsupervised multi-contrast MR image registration framework that is both accurate and efficient, utilizing a coarse-to-fine architecture with dual constraints, outperforming existing methods in clinical datasets.

Contribution

The paper presents a new end-to-end coarse-to-fine network with dual consistency constraints and a prior-based loss for improved multi-contrast MR image registration.

Findings

Achieves a Dice score of 0.8397 on stroke lesion registration.

About 10 times faster than SyN (Affine) on CPU.

Maintains high robustness on challenging data with limited information.

Abstract

Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless, the efficiency and performance of the existing registration algorithms can still be improved. In this paper, we propose a novel unsupervised learning-based framework to achieve accurate and efficient multi-contrast MR image registrations. Specifically, an end-to-end coarse-to-fine network architecture consisting of affine and deformable transformations is designed to improve the robustness and achieve end-to-end registration. Furthermore, a dual consistency constraint and a new prior knowledge-based loss function are developed to enhance the registration performances. The proposed method has been evaluated on a clinical dataset containing 555 cases, and encouraging performances have been achieved. Compared to the commonly utilized registration methods, including VoxelMorph, SyN, and LT-Net, the proposed method achieves better registration performance with a Dice score of 0.8397 in identifying stroke lesions. With regards to the registration speed, our method is about 10 times faster than the most competitive method of SyN (Affine) when testing on a CPU. Moreover, we prove that our method can still perform well on more challenging tasks with lacking scanning information data, showing high robustness for the clinical application.