Change Detection in Diffusion MRI Using Multivariate Statistical Testing on Tensors

摘要：

This paper presents a longitudinal change detection framework for detecting relevant modifications in diffusion MRI, with application to Multiple Sclerosis (MS). The proposed method is based on multivariate statistical testings which were initially introduced for tensor population comparison. We use these methods in the context of longitudinal change detection by considering several strategies to build sets of tensors characterizing the variability of each voxel. These testing tools have been considered either for the comparison of tensor eigenvalues or eigenvectors, thus enabling to differentiate orientation and diffusivity changes. Results on simulated MS lesion evolutions and on real data are presented. Interestingly, experiments on an MS patient highlight the ability of the proposed approach to detect changes in non evolving lesions (according to conventional MRI) and around lesions (in the normal appearing white matter), which might open promising perspectives for the follow-up of the MS pathology

作者: Antoine Grigis Vincent Noblet Felix Renard Fabrice Heitz Jean-Paul Armspach Lucien Rumbach

作者单位: University of Strasbourg, CNRS, UMR 7005, LSIIT, FranceUniversity of Strasbourg, CNRS, FRE 3280, LIN University of Strasbourg, CNRS, UMR 7005, LSIIT, France University of Strasbourg, CNRS, FRE 3280, LINC-IPB, France

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 117-124

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）