MR2US-Pro: Prostate MR to Ultrasound Image Translation and Registration Based on Diffusion Models

TL;DR

This paper introduces a novel, fully unsupervised diffusion-based framework for accurate prostate MRI to ultrasound image registration, utilizing a probe-location-independent 3D TRUS reconstruction and anatomy-aware modality translation.

Contribution

It proposes a probe-location-independent 3D TRUS reconstruction method and a diffusion-guided cross-modal registration framework that maps both modalities into a pseudo intermediate space.

Findings

Outperforms state-of-the-art registration methods in accuracy

Achieves realistic deformations without supervision

Effectively handles boundary and internal structural variations

Abstract

The diagnosis of prostate cancer increasingly depends on multimodal imaging, particularly magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS). However, accurate registration between these modalities remains a fundamental challenge due to the differences in dimensionality and anatomical representations. In this work, we present a novel framework that addresses these challenges through a two-stage process: TRUS 3D reconstruction followed by cross-modal registration. Unlike existing TRUS 3D reconstruction methods that rely heavily on external probe tracking information, we propose a totally probe-location-independent approach that leverages the natural correlation between sagittal and transverse TRUS views. With the help of our clustering-based feature matching method, we enable the spatial localization of 2D frames without any additional probe tracking information. For the registration stage, we introduce an unsupervised diffusion-based framework guided by modality translation. Unlike existing methods that translate one modality into another, we map both MR and US into a pseudo intermediate modality. This design enables us to customize it to retain only registration-critical features, greatly easing registration. To further enhance anatomical alignment, we incorporate an anatomy-aware registration strategy that prioritizes internal structural coherence while adaptively reducing the influence of boundary inconsistencies. Extensive validation demonstrates that our approach outperforms state-of-the-art methods by achieving superior registration accuracy with physically realistic deformations in a completely unsupervised fashion.