Chapter title |
A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI
|
---|---|
Chapter number | 69 |
Book title |
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, September 2017
|
DOI | 10.1007/978-3-319-66182-7_69 |
Pubmed ID | |
Book ISBNs |
978-3-31-966181-0, 978-3-31-966182-7
|
Authors |
Pramod Kumar Pisharady, Stamatios N. Sotiropoulos, Guillermo Sapiro, Christophe Lenglet |
Abstract |
We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential 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 un-mixing 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 in-vivo data from the Human Connectome Project demonstrate the improvements. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 11 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 2 | 18% |
Student > Doctoral Student | 2 | 18% |
Other | 1 | 9% |
Lecturer | 1 | 9% |
Student > Master | 1 | 9% |
Other | 1 | 9% |
Unknown | 3 | 27% |
Readers by discipline | Count | As % |
---|---|---|
Neuroscience | 3 | 27% |
Engineering | 2 | 18% |
Computer Science | 1 | 9% |
Unknown | 5 | 45% |