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Article Dans Une Revue Journal of Computational and Graphical Statistics Année : 2016

SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging

Résumé

High angular resolution diffusion imaging (HARDI) has recently been of great interest in mapping the orientation of intravoxel crossing fibers, and such orientation information allows one to infer the connectivity patterns prevalent among different brain regions and possible changes in such connectivity over time for various neurodegenerative and neuropsychiatric diseases. The aim of this article is to propose a penalized multiscale adaptive regression model (PMARM) framework to spatially and adaptively infer the orientation distribution function (ODF) of water diffusion in regions with complex fiber configurations. In PMARM, we reformulate the HARDI imaging reconstruction as a weighted regularized least-square regression (WRLSR) problem. Similarity and distance weights are introduced to account for spatial smoothness of HARDI, while preserving the unknown discontinuities (e.g., edges between white matter and gray matter) of HARDI. The L1 penalty function is introduced to ensure the sparse solutions of ODFs, while a scaled L1 weighted estimator is calculated to correct the bias introduced by the L1 penalty at each voxel. In PMARM, we integrate the multiscale adaptive regression models, the propagation-separation method, and Lasso (least absolute shrinkage and selection operator) to adaptively estimate ODFs across voxels. Experimental results indicate that PMARM can reduce the angle detection errors on fiber crossing area and provide more accurate reconstruction than standard voxel-wise methods.

Dates et versions

hal-01485555 , version 1 (09-03-2017)

Identifiants

Citer

Shangbang Rao, Joseph G. Ibrahim, Jian Cheng, Pew-Thian Yap, Hongtu Zhu. SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging. Journal of Computational and Graphical Statistics, 2016, ⟨10.1080/10618600.2015.1105750⟩. ⟨hal-01485555⟩
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