Prob-BBDM: a Probabilistic Brownian Bridge Diffusion Model for MRI sequence image-to-image translation

Computer Science > Artificial Intelligence [Submitted on 23 Jun 2026] Prob-BBDM: a Probabilistic Brownian Bridge Diffusion Model for MRI sequence image-to-image translation Martin Valls (UFR SFA (Poitiers), XLIM-ASALI, LabCom I3M (Poitiers)), Pascal Bourdon (UFR SFA (Poitiers), LabCom I3M (Poitiers), XLIM-ASALI), Christine Fernandez-Maloigne (LabCom I3M (Poitiers), XLIM-ASALI, UFR SFA (Poitiers)), Guillaume Herpe (CHU Poitiers -- Radio, DACTIM-MIS (Poitiers), LabCom I3M (Poitiers)), David Helbert (UFR SFA (Poitiers), XLIM-ASALI, LabCom I3M (Poitiers)) 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 3.49 mm, 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.24313 [cs.AI]   (or arXiv:2606.24313v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.24313 Focus to learn more Journal reference: Computerized Medical Imaging and Graphics, 2026, 130, pp.102745 Submission history From: David Helbert [view email] [via CCSD proxy] [v1] Tue, 23 Jun 2026 08:47:17 UTC (1,754 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)