Abstract
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 this https URL.
Abstract (translated)
尽管在医学图像生成领域取得了近期进展，现有方法仍难以产生解剖学上合理的3D结构。在合成大脑磁共振成像（MRI）中，特征性裂隙常常缺失，重建的皮层表面显得分散而不是密集曲折。为解决这一问题，我们引入了Cor2Vox——首个基于扩散模型的方法，将连续的皮层形状先验转化为合成的大脑MRIs。为此，我们利用布朗桥过程，该过程允许在形状轮廓和医学图像之间进行直接结构化映射。具体而言，我们将布朗桥扩散模型的概念扩展到3D，并进一步拓展以适应各种互补的形状表示方式。 实验结果显示，与以前基于体素的方法相比，我们的方法在重建结构的几何准确性方面取得了显著改进。此外，Cor2Vox在图像质量和多样性上表现出色，在如颅骨等非目标结构中产生了高变异性。最后，我们强调了该方法模拟亚体素级皮层萎缩的能力。 我们的代码可在[此处](https://this-is-the-url-of-the-code-repository)获取。
URL
https://arxiv.org/abs/2502.12742
