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.

翻译：尽管医学图像生成领域近期取得了进展，现有方法仍难以生成解剖学上合理的三维结构。在合成脑磁共振图像（MRI）中，特征性脑沟常常缺失，重建的皮层表面呈现分散而非密集卷曲的形态。为解决这一问题，我们提出了Cor2Vox，这是首个基于扩散模型、将连续皮层形状先验转换为合成脑MRI的方法。为实现这一目标，我们利用布朗桥过程，该过程允许在形状轮廓与医学图像之间建立直接的结构化映射。具体而言，我们将布朗桥扩散模型的概念适配至三维空间，并将其扩展以兼容多种互补的形状表示方法。实验表明，与先前基于体素的方法相比，本方法在重建结构的几何精度上取得了显著提升。此外，Cor2Vox在图像质量与多样性方面表现优异，能在非目标结构（如颅骨）中产生高度变异。最后，我们重点展示了该方法在亚体素水平模拟皮层萎缩的能力。代码发布于 https://github.com/ai-med/Cor2Vox。