Comparison of Anatomical and Diffusion MRI for detecting Parkinson’s Disease using Deep Convolutional Neural Network

Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer’s disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification. Clinical Relevance This study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson’s disease.

帕金森病（PD）是一种进行性神经退行性疾病，全球有超过1000万人受其影响。与阿尔茨海默病等其他年龄相关疾病相比，帕金森病的脑萎缩和微观结构异常往往更为细微，因此人们对机器学习方法在放射学扫描中检测帕金森病的能力很感兴趣。基于卷积神经网络（CNN）的深度学习模型可以从原始磁共振成像（MRI）扫描中自动提取有诊断价值的特征，但大多数基于CNN的深度学习模型仅在T1加权脑部MRI上进行过测试。在此，我们研究了弥散加权MRI（dMRI）——一种对微观组织结构特性敏感的MRI变体——作为基于CNN的帕金森病分类模型的附加输入的附加价值。我们的评估使用了来自三个不同队列的数据——长庚大学、宾夕法尼亚大学以及帕金森病进展标记物倡议（PPMI）数据集。我们用这些队列的各种组合来训练CNN，以找到最佳预测模型。尽管有必要在更多样化的数据上进行测试，但基于dMRI的深度学习模型在帕金森病分类方面显示出了潜力。临床相关性：本研究支持使用弥散加权图像作为解剖图像的替代物，用于基于人工智能的帕金森病检测。