使用扩散模型的MR与超声图像模态翻译与配准
Modality Translation and Registration of MR and Ultrasound Images Using Diffusion Models
AI总结：
本文提出基于层次化特征解耦的解剖一致性模态翻译网络ACMT，通过引入中间伪模态同时翻译MR和US图像，在保留关键解剖边界的同时缓解模态差异，显著提升多模态前列腺图像配准精度。
摘要：
多模态MR-US配准对于前列腺癌诊断至关重要。然而，由于显著的模态差异，该任务仍具挑战性。现有方法往往无法对齐关键边界，同时对无关细节过度敏感。为解决这一问题，我们提出了一种基于层次化特征解耦设计的解剖一致性模态翻译（ACMT）网络。我们利用浅层特征保持纹理一致性，利用深层特征保持边界保留。与将一种模态转换为另一种模态的传统模态翻译方法不同，我们的ACMT引入了定制化的中间伪模态设计。MR和US图像均向该中间域进行翻译，有效解决了传统翻译方法在下游配准任务中面临的瓶颈。实验表明，我们的方法在保留关键解剖边界以实现精确配准的同时，缓解了模态特异性差异。定量评估显示，与最先进的模态翻译方法相比，我们的方法具有更优的模态相似性。此外，下游配准实验证实，我们翻译后的图像取得了最佳的配准性能，凸显了我们框架在多模态前列腺图像配准中的鲁棒性。
英文摘要：
Multimodal MR-US registration is critical for prostate cancer diagnosis. However, this task remains challenging due to significant modality discrepancies. Existing methods often fail to align critical boundaries while being overly sensitive to irrelevant details. To address this, we propose an anatomically coherent modality translation (ACMT) network based on a hierarchical feature disentanglement design. We leverage shallow-layer features for texture consistency and deep-layer features for boundary preservation. Unlike conventional modality translation methods that convert one modality into another, our ACMT introduces the customized design of an intermediate pseudo modality. Both MR and US images are translated toward this intermediate domain, effectively addressing the bottlenecks faced by traditional translation methods in the downstream registration task. Experiments demonstrate that our method mitigates modality-specific discrepancies while preserving crucial anatomical boundaries for accurate registration. Quantitative evaluations show superior modality similarity compared to state-of-the-art modality translation methods. Furthermore, downstream registration experiments confirm that our translated images achieve the best alignment performance, highlighting the robustness of our framework for multi-modal prostate image registration.
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