{"ID":2854059,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.15556","arxiv_id":"2510.15556","title":"Diffusion Bridge Networks Simulate Clinical-grade PET from MRI for Dementia Diagnostics","abstract":"Positron emission tomography (PET) with 18F-Fluorodeoxyglucose (FDG) is an established tool in the diagnostic workup of patients with suspected dementing disorders. However, compared to the routinely available magnetic resonance imaging (MRI), FDG-PET remains significantly less accessible and substantially more expensive. Here, we present SiM2P, a 3D diffusion bridge-based framework that learns a probabilistic mapping from MRI and auxiliary patient information to simulate FDG-PET images of diagnostic quality. In a blinded clinical reader study, two neuroradiologists and two nuclear medicine physicians rated the original MRI and SiM2P-simulated PET images of patients with Alzheimer's disease, behavioral-variant frontotemporal dementia, and cognitively healthy controls. SiM2P significantly improved the overall diagnostic accuracy of differentiating between three groups from 75.0% to 84.7% (p\u003c0.05). Notably, the simulated PET images received higher diagnostic certainty ratings and achieved superior interrater agreement compared to the MRI images. Finally, we developed a practical workflow for local deployment of the SiM2P framework. It requires as few as 20 site-specific cases and only basic demographic information. This approach makes the established diagnostic benefits of FDG-PET imaging more accessible to patients with suspected dementing disorders, potentially improving early detection and differential diagnosis in resource-limited settings. Our code is available at https://github.com/Yiiitong/SiM2P.","short_abstract":"Positron emission tomography (PET) with 18F-Fluorodeoxyglucose (FDG) is an established tool in the diagnostic workup of patients with suspected dementing disorders. However, compared to the routinely available magnetic resonance imaging (MRI), FDG-PET remains significantly less accessible and substantially more expensi...","url_abs":"https://arxiv.org/abs/2510.15556","url_pdf":"https://arxiv.org/pdf/2510.15556v1","authors":"[\"Yitong Li\",\"Ralph Buchert\",\"Benita Schmitz-Koep\",\"Timo Grimmer\",\"Björn Ommer\",\"Dennis M. Hedderich\",\"Igor Yakushev\",\"Christian Wachinger\"]","published":"2025-10-17T11:42:11Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false,"code_links":[{"ID":608111,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2854059,"paper_url":"https://arxiv.org/abs/2510.15556","paper_title":"Diffusion Bridge Networks Simulate Clinical-grade PET from MRI for Dementia Diagnostics","repo_url":"https://github.com/Yiiitong/SiM2P","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}