3D Shape-to-Image Brownian Bridge Diffusion for Brain MRI Synthesis from Cortical Surfaces
Journal:
arXiv
Published Date:
Feb 18, 2025
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 https://github.com/ai-med/Cor2Vox.
