Iterative Subspace Screening for Rapid Sparse Estimation of Brain Tissue Microstructural Properties
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,
- PDF / 755,542 Bytes
- 8 Pages / 439.363 x 666.131 pts Page_size
- 68 Downloads / 167 Views
Department of Radiology and Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, USA 2 Department of Psychiatry and Behavioral Sciences, Stanford University, USA [email protected]
Abstract. 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.
1 Introduction Microstructural tissue properties can be inferred with the help of diffusion MRI (DMRI) thanks to its sensitivity to the restricted motion of water molecules owing to barriers such as cellular membranes. Microstructural information is typically obtained from diffusion parameters estimated via fitting to the acquired data some biophysical models. Fitting models such as the tensor model [1] is relatively simple and straightforward. But fitting models that are more sophisticated, such as the multi-compartmental models used in AxCaliber [2] and NODDI [3], is much more involved with significantly greater c Springer International Publishing Switzerland 2015 N. Navab et al. (Eds.): MICCAI 2015, Part I, LNCS 9349, pp. 223–230, 2015. DOI: 10.1007/978-3-319-24553-9_28
224
P.-T. Yap, Y. Zhang, and D. Shen
computational load. Computational complexity is further increased when certain structure is imposed on the solution, such as sparsity [4,5]. In a recent work called AMICO [6], the authors show that it is possible to speed up AxCaliber an
Data Loading...
