Zhang Lichi, Zhang Han, Chen Xiaobo, Wang Qian, Yap Pew-Thian, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Patch Based Tech Med Imaging (2017). 2017 Sep;10530:65-73. doi: 10.1007/978-3-319-67434-6_8. Epub 2017 Aug 31.
It has been recently demonstrated that the local BOLD signals in resting-state fMRI (rs-fMRI) can be captured for the white matter (WM) by functional correlation tensors (FCTs). FCTs provide similar orientation information as diffusion tensors (DTs), and also functional information concerning brain dynamics. However, FCTs are susceptible to noise due to the low signal-to-noise ratio nature of WM BOLD signals. Here we introduce a robust FCT estimation method to facilitate individualized diagnosis. , we develop a noise-tolerating patch-based approach to measure spatiotemporal correlations of local BOLD signals. , it is also enhanced by DTs predicted from the input rs-fMRI using a learning-based regression model. We evaluate our trained regressor using the high-resolution HCP dataset. The regressor is then applied to estimate the robust FCTs for subjects in the ADNI2 dataset. We demonstrate for the first time the disease diagnostic value of robust FCTs.
最近有研究表明，在静息态功能磁共振成像（rs-fMRI）中，通过功能相关张量（FCT）可以获取白质（WM）的局部血氧水平依赖（BOLD）信号。FCT提供的方向信息与扩散张量（DT）相似，还能提供有关脑动力学的功能信息。然而，由于WM BOLD信号的低信噪比特性，FCT容易受到噪声影响。在此，我们引入一种稳健的FCT估计方法以促进个体化诊断。我们开发了一种基于块的抗噪方法来测量局部BOLD信号的时空相关性。此外，它还通过使用基于学习的回归模型从输入的rs-fMRI预测的DT得到增强。我们使用高分辨率的人类连接组计划（HCP）数据集评估我们训练的回归器。然后将该回归器应用于估计阿尔茨海默病神经成像计划2（ADNI2）数据集中受试者的稳健FCT。我们首次证明了稳健FCT的疾病诊断价值。
