用布朗桥扩散模型将脑皮层形状转为逼真MRI，解决结构失真问题。
3D Shape-to-Image Brownian Bridge Diffusion for Brain MRI Synthesis from Cortical Surfaces
- 基于布朗桥扩散，直接从皮层形状生成3D MRI图像。
- 重建结构几何精度显著优于传统体素方法，能模拟亚像素级萎缩。
- 生成图像质量高、多样性好，适合研究脑发育与疾病演化。
尽管医学图像生成取得进展，现有方法仍难以生成解剖学上合理的3D结构。在合成脑MRI中，典型沟回常缺失，重建的皮层表面呈现分散而非密集卷曲状态。为此，我们提出Cor2Vox，首个基于扩散模型、将连续皮层形状先验映射为合成脑MRI的方法。通过利用布朗桥过程，实现形状轮廓与医学图像之间的直接结构化映射。具体地，我们将布朗桥扩散模型拓展至3D，并支持多种互补形状表示。实验表明，相较于以往体素基方法，本方法在重建结构几何精度上显著提升。此外，Cor2Vox在图像质量和多样性方面表现优异，非目标结构（如颅骨）变化丰富。最后，我们展示了该方法在亚像素级别模拟皮层萎缩的能力。代码已开源：https://github.com/ai-med/Cor2Vox。
原文摘要 · Abstract (English)
Despite recent advances in medical image generation, existing methods struggle to produce anatomically plausible 3D structures. In synthetic brain magnetic resonance images (MRIs), characteristic fissures are often missing, and reconstructed cortical surfaces appear scattered rather than densely convoluted. To address this issue, we introduce Cor2Vox, the first diffusion model-based method that translates continuous cortical shape priors to synthetic brain MRIs. To achieve this, we leverage a Brownian bridge process which allows for direct structured mapping between shape contours and medical images. Specifically, we adapt the concept of the Brownian bridge diffusion model to 3D and extend it to embrace various complementary shape representations. Our experiments demonstrate significant improvements in the geometric accuracy of reconstructed structures compared to previous voxel-based approaches. Moreover, Cor2Vox excels in image quality and diversity, yielding high variation in non-target structures like the skull. Finally, we highlight the capability of our approach to simulate cortical atrophy at the sub-voxel level. Our code is available at https://github.com/ai-med/Cor2Vox.
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