Abstract
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.
Abstract (translated)
阿尔茨海默病（AD）是一种进行性神经退行性疾病，其病理变化早在临床症状出现前几年就开始了。因此，早期检测对于及时干预至关重要。T1加权（T1w）磁共振成像（MRI）在临床上被常规用于识别大脑的宏观改变，但这些变化通常是在疾病的晚期才显现出来。相比之下，弥散MRI（dMRI）通过探测脑组织中的水扩散来敏感地捕捉更早出现的微观结构异常。dMRI指标包括分数各向异性（FA）和平均扩散率（MD），提供了关于白质完整性和神经退行性变性的补充信息。然而，由于采集时间长且容易受到运动伪影的影响，弥散成像在临床人群中常规使用受到了限制。 为了弥补这一差距，我提出了一种基于3D TransUNet的图像合成框架，该框架能够直接从T1w MRI预测FA和MD图谱。我的模型生成了高保真的图谱，在结构相似性指数（SSIM）方面超过了0.93，并且与真实dMRI数据之间的皮尔逊相关系数大于0.94。当将这些合成特征整合到多模态诊断模型中时，它们能够提升AD分类的准确性5%（从78.75%提升至83.75%），更重要的是，在轻度认知障碍（MCI）检测方面提高了12.5%。 这项研究表明，可以通过常规获取的T1w MRI推断高质量的弥散微结构信息，从而在没有弥散数据的情况下也能实现多模态成像的优势。通过减少扫描时间同时保留互补的结构性和微观结构信息，所提出的这种方法有潜力提高阿尔茨海默病临床诊断中的可及性、效率和准确性。
URL
https://arxiv.org/abs/2601.11614
