Iterative Subspace Screening for Rapid Sparse Estimation of Brain Tissue Microstructural Properties.

Iterative Subspace Screening for Rapid Sparse Estimation of Brain Tissue Microstructural Properties. Med Image Comput Comput Assist Interv. 2015 Oct;9349:223-230 Authors: Yap PT, Zhang Y, Shen D 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 ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research