Math @ Duke

Publications [#329136] of Guillermo Sapiro
Papers Published
 Pisharady, PK; Sotiropoulos, SN; Sapiro, G; Lenglet, C, A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multishell Diffusion MRI.,
Medical image computing and computerassisted intervention : MICCAI ... International Conference on Medical Image Computing and ComputerAssisted Intervention, vol. 10433
(September, 2017),
pp. 602610, ISBN 9783319661810 [doi]
(last updated on 2018/07/21)
Abstract: We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (undersampled qspace) multishell diffusion MRI data. The multishell data is represented in a dictionary form using a nonmonoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear unmixing framework, using a sparse Bayesian learning algorithm. A localized learning of hyperparameters at each voxel and for each possible fiber orientations improves the parameter estimation. Our experiments using synthetic data from the ISBI 2012 HARDI reconstruction challenge and invivo data from the Human Connectome Project demonstrate the improvements.


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