跨模态皮层桥接：基于扩散桥的皮层表面MRI到PET转换

中文摘要 AI 辅助

通过氟代脱氧葡萄糖正电子发射断层扫描（FDG-PET）测量的皮层低代谢是痴呆诊断的高灵敏度生物标志物。然而，高成本、辐射暴露和有限的可及性限制了其临床实用性。虽然从磁共振成像（MRI）进行跨模态合成提供了一种有前景的替代方案，但现有的体积生成方法并未明确考虑高度折叠的皮层几何结构，而疾病相关模式主要存在于该结构中。为解决这一问题，我们提出了一种新颖的基于表面的扩散桥框架DB-SUiT，用于MRI到PET转换，该框架原生地在皮层流形上运行。我们专门设计了一个条件球形U形视觉Transformer（SUiT）来建模复杂的跨模态关系，同时保持表面拓扑。它结合了球形卷积编码器用于多尺度表面特征提取，以及瓶颈Transformer用于捕获长距离空间依赖性，同时纳入人口统计学和皮层下条件以优化合成。在两个数据集（包括患有不同类型痴呆的受试者）上的评估表明，DB-SUiT实现了高保真合成，显著优于其他基线方法。在自动痴呆分类中，合成的PET表面将性能相较于MRI提高了14.2%，相较于PET体积提高了11.3%，接近真实PET表面的性能。在一项盲法阅片者研究中，合成PET达到了85.5%的诊断准确率，而MRI为75.8%，真实PET为95.2%。这进一步证明了跨队列和跨病理的泛化能力，因为该模型在未重新训练的情况下，在一个包含训练期间未出现的痴呆亚型的外部队列上进行了评估。我们的代码可在以下https URL获取。

英文摘要

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.

发表机构

• Technical University of Munich (TUM)（慕尼黑工业大学）
• TUM University Hospital（慕尼黑工业大学附属医院）
• Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）

机构由 AI 辅助整理，请以论文原文为准。