Sun Junyan, Chen Ruike, Tong Qiqi, Ma Jinghong, Gao Linlin, Fang Jiliang, Zhang Dongling, Chan Piu, He Hongjian, Wu Tao
Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Disease, Beijing, 100053, China.
Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
Brain Inform. 2021 Sep 28;8(1):18. doi: 10.1186/s40708-021-00139-z.
The literature regarding the use of diffusion-tensor imaging-derived metrics in the evaluation of Parkinson's disease (PD) is controversial. This study attempted to assess the feasibility of a deep-learning-based method for detecting alterations in diffusion kurtosis measurements associated with PD.
A total of 68 patients with PD and 77 healthy controls were scanned using scanner-A (3 T Skyra) (DATASET-1). Meanwhile, an additional five healthy volunteers were scanned with both scanner-A and an additional scanner-B (3 T Prisma) (DATASET-2). Diffusion kurtosis imaging (DKI) of DATASET-2 had an extra b shell compared to DATASET-1. In addition, a 3D-convolutional neural network (CNN) was trained from DATASET-2 to harmonize the quality of scalar measures of scanner-A to a similar level as scanner-B. Whole-brain unpaired t test and Tract-Based Spatial Statistics (TBSS) were performed to validate the differences between the PD and control groups using the model-fitting method and CNN-based method, respectively. We further clarified the correlation between clinical assessments and DKI results.
An increase in mean diffusivity (MD) was found in the left substantia nigra (SN) in the PD group. In the right SN, fractional anisotropy (FA) and mean kurtosis (MK) values were negatively correlated with Hoehn and Yahr (H&Y) scales. In the putamen (Put), FA values were positively correlated with the H&Y scales. It is worth noting that these findings were only observed with the deep learning method. There was neither a group difference nor a correlation with clinical assessments in the SN or striatum exceeding the significance level using the conventional model-fitting method.
The CNN-based method improves the robustness of DKI and can help to explore PD-associated imaging features.
关于在帕金森病（PD）评估中使用扩散张量成像衍生指标的文献存在争议。本研究试图评估一种基于深度学习的方法检测与PD相关的扩散峰度测量变化的可行性。
使用扫描仪A（3T Skyra）对68例PD患者和77名健康对照进行扫描（数据集1）。同时，另外5名健康志愿者使用扫描仪A和另一台扫描仪B（3T Prisma）进行扫描（数据集2）。与数据集1相比，数据集2的扩散峰度成像（DKI）有一个额外的b值壳层。此外，从数据集2训练一个三维卷积神经网络（CNN），以将扫描仪A的标量测量质量协调到与扫描仪B相似的水平。分别使用模型拟合方法和基于CNN的方法进行全脑非配对t检验和基于纤维束的空间统计（TBSS），以验证PD组和对照组之间的差异。我们进一步阐明了临床评估与DKI结果之间的相关性。
在PD组左侧黑质（SN）中发现平均扩散率（MD）增加。在右侧SN中，各向异性分数（FA）和平均峰度（MK）值与Hoehn和Yahr（H&Y）分级呈负相关。在壳核（Put）中，FA值与H&Y分级呈正相关。值得注意的是，这些发现仅在深度学习方法中观察到。使用传统模型拟合方法时，SN或纹状体中既没有组间差异，也没有与临床评估的相关性超过显著性水平。
基于CNN的方法提高了DKI的稳健性，并有助于探索与PD相关的成像特征。
