Prob-BBDM: A Probabilistic Brownian Bridge Diffusion Model for MRI sequence image-to-image translation.
Authors
Affiliations (3)
Affiliations (3)
- I3M common laboratory CNRS-Siemens Healthinners, University Hospital and University of Poitiers, Poitiers, 86000, France; XLIM Laboratory, CNRS UMR 7252, University of Poitiers, Poitiers, 86000, France. Electronic address: [email protected].
- I3M common laboratory CNRS-Siemens Healthinners, University Hospital and University of Poitiers, Poitiers, 86000, France; XLIM Laboratory, CNRS UMR 7252, University of Poitiers, Poitiers, 86000, France.
- I3M common laboratory CNRS-Siemens Healthinners, University Hospital and University of Poitiers, Poitiers, 86000, France; Laboratory of Applied Mathematics, DACTIM-MIS, CNRS UMR 7348, University of Poitiers, Poitiers, 86000, France.
Abstract
AI-driven image-to-image synthesis is rapidly advancing, with growing applications in medical imaging. Multi-modal image analysis plays a crucial role in optimizing examination quality, yet acquiring multiple imaging modalities in clinical settings remains resource-intensive and time-consuming, especially for 3D imaging. To address this challenge, we propose a novel image-to-image translation model based on Brownian Bridge Diffusion Models (BBDM), which synthesizes magnetic resonance imaging (MRI) sequences from 2D axial slices. Our approach integrates a variational encoder-guided diffusion mechanism, leveraging probabilistic image distributions to enhance synthesis quality. Evaluated on the BraTS 2021 dataset, our Probabilistic-BBDM (Prob-BBDM) achieves superior performance across multiple translation tasks, reaching up to 88.46% SSIM and 26.09 dB PSNR, with consistent improvements over baselines. Notably, our diffusion process requires only 4 steps, making it computationally efficient while maintaining high-quality synthesis. To further validate generalizability, we test Prob-BBDM on an external third-party dataset, demonstrating consistent performance across domains. Additionally, we assess the clinical utility of the synthesized slices by using them as input to a pre-trained segmentation model. Tumor segmentation yields a Dice score of 88.71% and an HD95 of 3.49mm, confirming that the synthesized slices preserve critical diagnostic information. These results highlight the potential of Prob-BBDM for high-quality, efficient, and generalizable MRI synthesis, offering a promising step toward improved medical image translation. https://gitlab.xlim.fr/mvalls/Prob-BBDM.