Fouque Anne-Laure, Fillard Pierre, Bargiacchi Anne, Cachia Arnaud, Zilbovicius Monica, Thyreau Benjamin, Le Floch Edith, Ciuciu Philippe, Duchesnay Edouard
CEA, Neurospin, LNAO, Saclay, France.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):9-16. doi: 10.1007/978-3-642-23629-7_2.
In this paper, we propose to use the full diffusion tensor to perform brain-wide score prediction on diffusion tensor imaging (DTI) using the log-Euclidean framework., rather than the commonly used fractional anisotropy (FA). Indeed, scalar values such as the FA do not capture all the information contained in the diffusion tensor. Additionally, full tensor information is included in every step of the pre-processing pipeline: registration, smoothing and feature selection using voxelwise multivariate regression analysis. This approach was tested on data obtained from 30 children and adolescents with autism spectrum disorder and showed some improvement over the FA-only analysis.
在本文中，我们提议使用完整的扩散张量，在对数欧几里得框架下对扩散张量成像（DTI）进行全脑评分预测，而不是常用的分数各向异性（FA）。事实上，诸如FA这样的标量值并不能捕捉扩散张量中包含的所有信息。此外，全张量信息包含在预处理流程的每一步中：配准、平滑以及使用体素级多变量回归分析进行特征选择。该方法在从30名患有自闭症谱系障碍的儿童和青少年获取的数据上进行了测试，并且显示出相较于仅使用FA的分析有一定改进。
