{"ID":23475249,"CreatedAt":"2026-09-18T01:09:05.407443952Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.20147","arxiv_id":"2609.20147","title":"Bridging Modalities on the Cortex: Surface-based MRI to PET Translation with a Diffusion Bridge","abstract":"Cortical hypometabolism measured by Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a highly sensitive biomarker for dementia diagnosis. However, high costs, radiation exposure, and limited accessibility constrain its clinical utility. While cross-modal synthesis from Magnetic Resonance Imaging (MRI) offers a promising alternative, existing volumetric generation methods do not explicitly account for the highly folded cortical geometry, where disease-related patterns predominantly reside. To address this, we introduce a novel surface-based diffusion bridge framework DB-SUiT for MRI-to-PET translation that operates natively on the cortical manifold. A conditional Spherical U-shaped vision Transformer (SUiT) is specifically designed to model the intricate cross-modal relationships while preserving surface topology. It combines spherical convolutional encoders for multi-scale surface feature extraction with bottleneck Transformers to capture long-range spatial dependencies, while incorporating demographic and subcortical conditions to refine the synthesis. Evaluated on two datasets, including subjects with different dementia types, DB-SUiT demonstrates high-fidelity synthesis that substantially outperforms other baselines. In automated dementia classification, synthesized PET surfaces improve performance over MRI by 14.2% and PET volumes by 11.3%, approaching the performance of real PET surfaces. In a blinded reader study, synthetic PET achieved 85.5% diagnostic accuracy, compared with 75.8% for MRI and 95.2% for real PET. This further demonstrates cross-cohort and cross-pathology generalization, as the model was evaluated without retraining on an external cohort that included a dementia subtype not represented during training. Our code is available at https://github.com/ai-med/DB-SUiT.","short_abstract":"Cortical hypometabolism measured by Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a highly sensitive biomarker for dementia diagnosis. However, high costs, radiation exposure, and limited accessibility constrain its clinical utility. While cross-modal synthesis from Magnetic Resonance Imaging (MRI) offer...","url_abs":"https://arxiv.org/abs/2609.20147","url_pdf":"https://arxiv.org/pdf/2609.20147v1","authors":"[\"Yitong Li\",\"Alexandra Samoylova\",\"Fabian Bongratz\",\"Timo Grimmer\",\"Dennis M. Hedderich\",\"Igor Yakushev\",\"Christian Wachinger\"]","published":"2026-09-17T12:38:33Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Vision Transformer\",\"Diffusion Model\",\"Transformer\"]","has_code":false,"code_links":[{"ID":639800,"CreatedAt":"2026-09-18T01:09:05.407443952Z","UpdatedAt":"2026-09-18T01:09:05.407443952Z","DeletedAt":null,"paper_id":23475249,"paper_url":"https://arxiv.org/abs/2609.20147","paper_title":"Bridging Modalities on the Cortex: Surface-based MRI to PET Translation with a Diffusion Bridge","repo_url":"https://github.com/ai-med/DB-SUiT","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}