Longitudinal change detection: Inference on the diffusion tensor along white matter pathways

被引:5
作者
Grigis, Antoine [1 ]
Noblet, Vincent [1 ]
Blanc, Frederic [1 ]
Heitz, Fabrice [1 ]
de Seze, Jerome [2 ]
Kremer, Stephane [1 ]
Armspach, Jean-Paul [1 ]
机构
[1] Univ Strasbourg, CNRS, ICube, FMTS Strasbourg, Strasbourg, France
[2] Univ Strasbourg, INSERM, FMTS Strasbourg, Strasbourg, France
关键词
Longitudinal change detection; Multivariate statistics; Diffusion weighted MRI; White matter pathways; Neuromyelitis optica; COGNITIVE FUNCTIONS; NEUROMYELITIS-OPTICA; DTI; REGISTRATION; TRACTOGRAPHY; STATISTICS; BOOTSTRAP;
D O I
10.1016/j.media.2013.01.004
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Diffusion weighted magnetic resonance imaging (DW-MRI) makes it possible to probe brain connections in vivo. This paper presents a change detection framework that relies on white matter pathways with application to neuromyelitis optica (NMO). The objective is to detect local or global fiber diffusion property modifications between two longitudinal DW-MRI acquisitions of a patient. To this end, we develop two frameworks based on statistical tests on tensor eigenvalues to detect local or global changes along the white matter pathways: a pointwise test that compares tensor populations extracted in bundles cross sections and a fiberwise test that compares paired tensors along all the fiber bundles. Experiments on both synthetic and real data highlight the benefit of considering fiber based statistical tests compared to standard voxelwise strategies. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:375 / 386
页数:12
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