用T1w MRI生成高质量DTI图像，助力脑部影像研究与诊断。
Diffusion Bridge Models for 3D Medical Image Translation
- 基于扩散桥模型实现T1w与DTI模态间3D图像相互转换。
- 生成的DTI图像在白质结构和各向异性上与真实数据高度一致。
- 适用于阿尔茨海默病等疾病分类任务，可替代真实数据使用。
弥散张量成像（DTI）能揭示人脑微结构信息，但获取时间长于更易获得的T1加权磁共振成像（T1w MRI）。为解决此问题，我们提出一种用于3D脑影像模态转换的扩散桥模型，可从T1w图像生成高质量的DTI部分各向异性（FA）图像，反之亦然，从而实现跨模态数据增强并减少对大量DTI采集的需求。通过感知相似性、像素级一致性和分布一致性评估，结果表明该方法能有效捕捉解剖结构并保持白质完整性信息。合成数据在性别分类和阿尔茨海默病分类任务中表现接近真实数据，验证了其实际应用价值。该模型为改善神经影像数据集、支持临床决策提供了有前景的解决方案，有望显著推动神经影像研究与临床实践。
原文摘要 · Abstract (English)
Diffusion tensor imaging (DTI) provides crucial insights into the microstructure of the human brain, but it can be time-consuming to acquire compared to more readily available T1-weighted (T1w) magnetic resonance imaging (MRI). To address this challenge, we propose a diffusion bridge model for 3D brain image translation between T1w MRI and DTI modalities. Our model learns to generate high-quality DTI fractional anisotropy (FA) images from T1w images and vice versa, enabling cross-modality data augmentation and reducing the need for extensive DTI acquisition. We evaluate our approach using perceptual similarity, pixel-level agreement, and distributional consistency metrics, demonstrating strong performance in capturing anatomical structures and preserving information on white matter integrity. The practical utility of the synthetic data is validated through sex classification and Alzheimer's disease classification tasks, where the generated images achieve comparable performance to real data. Our diffusion bridge model offers a promising solution for improving neuroimaging datasets and supporting clinical decision-making, with the potential to significantly impact neuroimaging research and clinical practice.
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