Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning
We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixin...
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| Format: | Article |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/44191/ |
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| author | Pisharady, Pramod Kumar Sotiropoulos, Stamatios N. Duarte-Carvajalino, Julio M. Sapiro, Guillermo Lenglet, Christophe |
| author_facet | Pisharady, Pramod Kumar Sotiropoulos, Stamatios N. Duarte-Carvajalino, Julio M. Sapiro, Guillermo Lenglet, Christophe |
| author_sort | Pisharady, Pramod Kumar |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates. |
| first_indexed | 2025-11-14T19:54:40Z |
| format | Article |
| id | nottingham-44191 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:54:40Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-441912020-05-04T18:52:08Z https://eprints.nottingham.ac.uk/44191/ Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning Pisharady, Pramod Kumar Sotiropoulos, Stamatios N. Duarte-Carvajalino, Julio M. Sapiro, Guillermo Lenglet, Christophe We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates. Elsevier 2017-06-29 Article PeerReviewed Pisharady, Pramod Kumar, Sotiropoulos, Stamatios N., Duarte-Carvajalino, Julio M., Sapiro, Guillermo and Lenglet, Christophe (2017) Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning. NeuroImage . ISSN 1095-9572 Sparse Bayesian learning; Compressive sensing; Linear unmixing; Diffusion MRI; Fiber orientation; Sparse signal recovery http://www.sciencedirect.com/science/article/pii/S1053811917305232 doi:10.1016/j.neuroimage.2017.06.052 doi:10.1016/j.neuroimage.2017.06.052 |
| spellingShingle | Sparse Bayesian learning; Compressive sensing; Linear unmixing; Diffusion MRI; Fiber orientation; Sparse signal recovery Pisharady, Pramod Kumar Sotiropoulos, Stamatios N. Duarte-Carvajalino, Julio M. Sapiro, Guillermo Lenglet, Christophe Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning |
| title | Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning |
| title_full | Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning |
| title_fullStr | Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning |
| title_full_unstemmed | Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning |
| title_short | Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning |
| title_sort | estimation of white matter fiber parameters from compressed multiresolution diffusion mri using sparse bayesian learning |
| topic | Sparse Bayesian learning; Compressive sensing; Linear unmixing; Diffusion MRI; Fiber orientation; Sparse signal recovery |
| url | https://eprints.nottingham.ac.uk/44191/ https://eprints.nottingham.ac.uk/44191/ https://eprints.nottingham.ac.uk/44191/ |