MR2US-Pro：基于扩散模型的前列腺 MR 到超声图像翻译与配准
MR2US-Pro: Prostate MR to Ultrasound Image Translation and Registration Based on Diffusion Models
AI总结：
本文提出MR2US-Pro框架，通过无探头跟踪的TRUS三维重建和基于扩散模型的无监督跨模态配准，将MR与US映射到伪中间模态并结合解剖感知策略，实现高精度前列腺图像配准。
摘要：
前列腺癌的诊断越来越依赖于多模态成像，特别是磁共振成像（MRI）和经直肠超声（TRUS）。然而，由于维度和解剖表示上的差异，这些模态之间的精确配准仍然是一个根本性挑战。在这项工作中，我们提出了一个新的框架，通过两阶段过程来应对这些挑战：TRUS 三维重建，然后是跨模态配准。与现有严重依赖外部探头跟踪信息的 TRUS 三维重建方法不同，我们提出了一种完全独立于探头位置的方法，利用矢状面和横断面 TRUS 视图之间的自然相关性。借助我们基于聚类的特征匹配方法，我们能够在没有任何额外探头跟踪信息的情况下实现二维帧的空间定位。对于配准阶段，我们引入了一个由模态翻译引导的无监督扩散框架。与现有将一种模态翻译成另一种模态的方法不同，我们将 MR 和 US 都映射到一个伪中间模态。这种设计使我们能够对其进行定制，只保留对配准至关重要的特征，从而大大简化配准。为了进一步增强解剖对齐，我们纳入了一种解剖感知配准策略，该策略优先考虑内部结构一致性，同时自适应地减少边界不一致的影响。大量验证表明，我们的方法以完全无监督的方式实现了卓越的配准精度和物理真实的形变，超越了最先进的方法。
英文摘要：
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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