用扩散模型生成高质量脑影像，提升疾病诊断准确性。
Pattern-Aware Diffusion Synthesis of fMRI/dMRI with Tissue and Microstructural Refinement
- 设计双模态3D扩散框架，融合组织与微结构信息。
- 合成的fMRI/dMRI在PSNR和SSIM上优于基线1.54dB/4.12%。
- 适合神经退行性疾病研究，临床诊断准确率达67.92%。
功能性磁共振成像（fMRI）和扩散磁共振成像（dMRI）对研究神经退行性疾病至关重要，但缺失模态限制了其临床应用。尽管基于GAN和扩散模型的方法在模态补全方面已见成效，但在fMRI-dMRI合成中仍受限于：（1）fMRI与dMRI在时间/梯度轴上的信号差异显著；（2）生成过程中缺乏对疾病相关神经解剖模式的充分建模。为此，本文提出PDS，引入两项创新：（1）一种模式感知的双模态3D扩散框架，实现跨模态学习；（2）集成高效微结构精修的组织精修网络，以保持结构保真度与细节。在OASIS-3、ADNI及自建数据集上评估，本方法达到领先性能，fMRI合成的PSNR/SSIM为29.83 dB / 90.84%（较基线提升1.54 dB / 4.12%），dMRI合成为30.00 dB / 77.55%（提升1.02 dB / 2.2%）。临床验证显示，合成数据在真实-合成混合实验中表现优异，实现轻度认知障碍（MCI）与阿尔茨海默病（AD）分类准确率分别为67.92%/66.02%/64.15%。代码已开源于PDS GitHub Repository。
原文摘要 · Abstract (English)
Magnetic resonance imaging (MRI), especially functional MRI (fMRI) and diffusion MRI (dMRI), is essential for studying neurodegenerative diseases. However, missing modalities pose a major barrier to their clinical use. Although GAN- and diffusion model-based approaches have shown some promise in modality completion, they remain limited in fMRI-dMRI synthesis due to (1) significant BOLD vs. diffusion-weighted signal differences between fMRI and dMRI in time/gradient axis, and (2) inadequate integration of disease-related neuroanatomical patterns during generation. To address these challenges, we propose PDS, introducing two key innovations: (1) a pattern-aware dual-modal 3D diffusion framework for cross-modality learning, and (2) a tissue refinement network integrated with a efficient microstructure refinement to maintain structural fidelity and fine details. Evaluated on OASIS-3, ADNI, and in-house datasets, our method achieves state-of-the-art results, with PSNR/SSIM scores of 29.83 dB/90.84\% for fMRI synthesis (+1.54 dB/+4.12\% over baselines) and 30.00 dB/77.55\% for dMRI synthesis (+1.02 dB/+2.2\%). In clinical validation, the synthesized data show strong diagnostic performance, achieving 67.92\%/66.02\%/64.15\% accuracy (NC vs. MCI vs. AD) in hybrid real-synthetic experiments. Code is available in \href{https://github.com/SXR3015/PDS}{PDS GitHub Repository}
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