Problem
Research questions and friction points this paper is trying to address.
MRI-to-PET translation
cortical geometry
cross-modal synthesis
Bridging Modalities on the Cortex: Surface-based MRI to PET Translation with a Diffusion Bridge
🤖 AI Summary
为解决PET成像成本高、辐射和可及性问题，提出了一种基于皮层曲面的MRI到PET转换框架DB-SUiT，利用条件球形视觉Transformer提高痴呆症诊断准确性。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
surface-based diffusion bridge
Spherical U-shaped vision Transformer (SUiT)
cortical manifold
cross-modal synthesis
dementia diagnosis
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