Diffusion Bridge Networks
Simulate Clinical-grade PET from MRI
for Dementia Diagnostics

Yitong Li1,2, Ralph Buchert3, Benita Schmitz-Koep4, Timo Grimmer5, Björn Ommer2,6, Dennis M. Hedderich‡4, Igor Yakushev‡7 Christian Wachinger‡1,2
‡Shared senior authors.
1Lab for AI in Medical Imaging, Technical University of Munich (TUM), Germany 2Munich Center for Machine Learning (MCML), Germany 3Department of Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Germany 4Department of Neuroradiology, TUM University Hospital, Germany 5Department of Neurology, TUM University Hospital, Germany 6CompVis, Ludwig-Maximilians-University Munich (LMU), Germany 7Department of Nuclear Medicine, TUM University Hospital, Germany
Paper arXiv Code

SiM2P simulates PET from MRI with high fidelity

(AD: Alzheimer's Disease, FTD: Frontotemporal Dementia, CN: Normal Control)

82 years old female with AD

66 years old female with AD

71 years old male with FTD

60 years old female with FTD

69 years old female as CN

70 years old male as CN

Diffusion Bridge Framework


SiM2P employs a 3D diffusion bridge to simulate PET (SimPET) from structural MRI, conditioned on available auxiliary data. We validated the diagnostic utility of SimPET in a blinded clinical reader study, where SimPET showed significantly higher accuracy than MRI.

Enhanced Diagnostic Accuracy

Improved Diagnostic Accuracy


In a blinded clinical reader study, SimPET from SiM2P improved diagnostic accuracy by up to 12% over MRI across a range of tasks, with substantially increased interrater reliability.

Enhanced Diagnostic Accuracy

High Pathological Sensitivity


SiM2P offers high pathological sensitivity even in scenarios where structural MRI lacks sufficient pathological atrophy, such as in early-stage or atypical presentations of AD.

SiM2P with high pathological specificity

Local Deployment


With Local-Adapt, clinical sites can calibrate publicly pre-trained SiM2P models with as few as 20 local samples, while maintaining high-quality and accurate site-specific PET synthesis.

local adapt performance

Abstract

Positron emission tomography (PET) with 18-Fluorodeoxyglucose (FDG) is an established tool for diagnosing suspected dementing disorders. However, compared to routinely available magnetic resonance imaging (MRI), FDG-PET remains substantially less accessible and more expensive.

Here, we present SiM2P, a 3D diffusion bridge framework that learns a robust mapping from MRI and auxiliary patient information to simulate high-fidelity FDG-PET of diagnostic quality.

In a blinded clinical reader study, clinicians rated the original MRI and SiM2P-simulated PET of patients with Alzheimer's disease, behavioral-variant frontotemporal dementia, and cognitively healthy controls. SiM2P significantly improved the diagnostic accuracy of differentiating between three groups from 75.0% to 84.7% (p<0.05). Notably, the simulated PET achieved high diagnostic certainty and superior interrater reliability.

Finally, we developed a practical local deployment workflow requiring only minimal site-specific cases and basic demographics. Our approach extends the accessibility of established diagnostic benefits of FDG-PET, improving early and differential dementia diagnostics.

BibTeX


      @article{li2025diffusion,
              title={Diffusion Bridge Networks Simulate Clinical-grade PET from MRI for Dementia Diagnostics},
              author={Li, Yitong and Buchert, Ralph and Schmitz-Koep, Benita and Grimmer, Timo and Ommer, Bj{\"o}rn and Hedderich, Dennis M and Yakushev, Igor and Wachinger, Christian},
              journal={arXiv preprint arXiv:2510.15556},
              year={2025}
              }