Liu Bilan, Qiu Xing, Zhu Tong, Tian Wei, Hu Rui, Ekholm Sven, Schifitto Giovanni, Zhong Jianhui
Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA.
Phys Med Biol. 2016 Mar 21;61(6):2497-513. doi: 10.1088/0031-9155/61/6/2497. Epub 2016 Mar 7.
Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.
特定受试者的纵向扩散张量成像（DTI）研究对于病变病理变化及疾病演变的调查至关重要。扩散张量成像的空间回归分析（SPREAD）是一种基于非参数置换的统计框架，它结合了空间回归和重采样技术，无需先验假设即可在个体水平上有效检测全脑内局部纵向扩散变化。然而，边界模糊和错位限制了其灵敏度，尤其是在检测不规则形状病变时。在本研究中，我们通过纳入三维（3D）非线性各向异性扩散滤波方法提出了一种改进的SPREAD（称为改进的SPREAD或iSPREAD）方法，该方法通过非线性尺度空间方法提供保边图像平滑。使用模拟和体内人脑数据对基于iSPREAD的统计推断进行了评估，并与原始SPREAD方法进行了比较。结果表明，通过采用非线性各向异性滤波，SPREAD方法的灵敏度和准确性得到了显著提高。iSPREAD能够以更高的灵敏度、准确性和增强的统计功效识别大脑中特定受试者的纵向变化，特别是当DTI中相邻图像像素之间的空间相关性不均匀时。
