TY - CHAP

T1 - Iterative subspace screening for rapid sparse estimation of brain tissue microstructural properties

AU - Yap, Pew Thian

AU - Zhang, Yong

AU - Shen, Dinggang

N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.

PY - 2015

Y1 - 2015

N2 - Diffusion magnetic resonance imaging (DMRI) is a powerful imaging modality due to its unique ability to extract microstructural information by utilizing restricted diffusion to probe compartments that are much smaller than the voxel size. Quite commonly, a mixture of models is fitted to the data to infer microstructural properties based on the estimated parameters. The fitting process is often non-linear and computationally very intensive. Recent work by Daducci et al. has shown that speed improvement of several orders of magnitude can be achieved by linearizing and recasting the fitting problem as a linear system, involving the estimation of the volume fractions associated with a set of diffusion basis functions that span the signal space. However, to ensure coverage of the signal space, sufficiently dense sampling of the parameter space is needed. This can be problematic because the number of basis functions increases exponentially with the number of parameters, causing computational intractability.We propose in this paper a method called iterative subspace screening (ISS) for tackling this ultrahigh dimensional problem. ISS requires only solving the problem in a medium-size subspace with a dimension that is much smaller than the original space spanned by all diffusion basis functions but is larger than the expected cardinality of the support of the solution. The solution obtained for this subspace is used to screen the basis functions to identify a new subspace that is pertinent to the target problem. These steps are performed iteratively to seek both the solution subspace and the solution itself. We apply ISS to the estimation of the fiber orientation distribution function (ODF) and demonstrate that it improves estimation robustness and accuracy.

AB - Diffusion magnetic resonance imaging (DMRI) is a powerful imaging modality due to its unique ability to extract microstructural information by utilizing restricted diffusion to probe compartments that are much smaller than the voxel size. Quite commonly, a mixture of models is fitted to the data to infer microstructural properties based on the estimated parameters. The fitting process is often non-linear and computationally very intensive. Recent work by Daducci et al. has shown that speed improvement of several orders of magnitude can be achieved by linearizing and recasting the fitting problem as a linear system, involving the estimation of the volume fractions associated with a set of diffusion basis functions that span the signal space. However, to ensure coverage of the signal space, sufficiently dense sampling of the parameter space is needed. This can be problematic because the number of basis functions increases exponentially with the number of parameters, causing computational intractability.We propose in this paper a method called iterative subspace screening (ISS) for tackling this ultrahigh dimensional problem. ISS requires only solving the problem in a medium-size subspace with a dimension that is much smaller than the original space spanned by all diffusion basis functions but is larger than the expected cardinality of the support of the solution. The solution obtained for this subspace is used to screen the basis functions to identify a new subspace that is pertinent to the target problem. These steps are performed iteratively to seek both the solution subspace and the solution itself. We apply ISS to the estimation of the fiber orientation distribution function (ODF) and demonstrate that it improves estimation robustness and accuracy.

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U2 - 10.1007/978-3-319-24553-9_28

DO - 10.1007/978-3-319-24553-9_28

M3 - Chapter

AN - SCOPUS:84947475403

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 223

EP - 230

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -