用常规MRI生成扩散影像，提升阿尔茨海默病早期诊断准确率
Multi-modal MRI-Based Alzheimer's Disease Diagnosis with Transformer-based Image Synthesis and Transfer Learning
- 基于T1w MRI通过3D TransUNet模型合成FA和MD图
- 合成图像与真实dMRI相关性超0.94，分类准确率提升5%
- 特别改善轻度认知障碍检测，适合临床无dMRI设备场景
阿尔茨海默病（AD）是一种进行性神经退行性疾病，病理变化在临床症状出现前多年即已开始，因此早期检测至关重要。临床上常用T1加权（T1w）磁共振成像识别脑部宏观改变，但这些变化通常在疾病晚期才显现。扩散MRI（dMRI）能探测更早期的微结构异常，其指标如各向异性分数（FA）和平均扩散率（MD）可提供白质完整性与神经退行性的互补信息。然而，dMRI扫描耗时且易受运动伪影影响，限制了其在临床人群中的常规应用。为此，本文提出一种3D TransUNet图像合成框架，直接从T1w MRI预测FA和MD图。模型生成高质量合成图，结构相似性指数（SSIM）超过0.93，与真实dMRI的皮尔逊相关性大于0.94。将其整合至多模态诊断模型中，可使阿尔茨海默病分类准确率从78.75%提升至83.75%，并显著提高轻度认知障碍（MCI）检测能力达12.5%。研究证明，高质量的扩散微结构信息可从常规T1w MRI中推断，有效将多模态成像优势迁移至缺乏扩散数据的场景。该方法减少扫描时间，同时保留结构与微结构信息，有望提升临床诊疗的可及性、效率与准确性。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a progressive neurodegenerative disorder in which pathological changes begin many years before the onset of clinical symptoms, making early detection essential for timely intervention. T1-weighted (T1w) Magnetic Resonance Imaging (MRI) is routinely used in clinical practice to identify macroscopic brain alterations, but these changes typically emerge relatively late in the disease course. Diffusion MRI (dMRI), in contrast, is sensitive to earlier microstructural abnormalities by probing water diffusion in brain tissue. dMRI metrics, including fractional anisotropy (FA) and mean diffusivity (MD), provide complementary information about white matter integrity and neurodegeneration. However, dMRI acquisitions are time-consuming and susceptible to motion artifacts, limiting their routine use in clinical populations. To bridge this gap, I propose a 3D TransUNet image synthesis framework that predicts FA and MD maps directly from T1w MRI. My model generates high-fidelity maps, achieving a structural similarity index (SSIM) exceeding 0.93 and a strong Pearson correlation (>0.94) with ground-truth dMRI. When integrated into a multi-modal diagnostic model, these synthetic features boost AD classification accuracy by 5% (78.75%->83.75%) and, most importantly, improve mild cognitive impairment (MCI) detection by 12.5%. This study demonstrates that high-quality diffusion microstructural information can be inferred from routinely acquired T1w MRI, effectively transferring the benefits of multi-modality imaging to settings where diffusion data are unavailable. By reducing scan time while preserving complementary structural and microstructural information, the proposed approach has the potential to improve the accessibility, efficiency, and accuracy of AD diagnosis in clinical practice.
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