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

• URL: http://arxiv.org/abs/2506.00591v1
• Date: Sat, 31 May 2025 14:55:03 GMT
• Title: MR2US-Pro: Prostate MR to Ultrasound Image Translation and Registration Based on Diffusion Models
• Authors: Xudong Ma, Nantheera Anantrasirichai, Stefanos Bolomytis, Alin Achim,
• Abstract summary: We present a novel framework that addresses the challenges through a two-stage process: TRUS 3D reconstruction followed by cross-modal registration.<n>We propose a totally probe-location-independent approach that leverages the natural correlation between sagittal and transverse TRUS views.<n>For the registration stage, we introduce an unsupervised diffusion-based framework guided by modality translation.
• Score: 7.512221808783586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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