Share Email Print
cover

Proceedings Paper

Effect of regularization parameter and scan time on crossing fibers with constrained compressed sensing
Author(s): Fatma Elzahraa A. ElShahaby; Bennett A. Landman; Jerry L. Prince
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Diffusion tensor imaging (DTI) is an MR imaging technique that uses a set of diffusion weighted measurements in order to determine the water diffusion tensor at each voxel. In DTI, a single dominant fiber orientation is calculated at each measured voxel, even if multiple populations of fibers are present within this voxel. A new approach called Crossing Fiber Angular Resolution of Intra-voxel structure (CFARI) for processing diffusion weighted magnetic resonance data has been recently introduced. Based on compressed sensing, CFARI is able to resolve intra-voxel structure from limited number of measurements, but its performance as a function of the scan and algorithm parameters is poorly understood at present. This paper describes simulation experiments to help understand CFARI performance tradeoffs as a function of the data signal-to-noise ratio and the algorithm regularization parameter. In the compressed sensing criterion, the choice of the regularization parameter β is critical. If β is too small, then the solution is the conventional least squares solution, while if β is too large then the solution is identically zero. The correct selection of β turns out to be data dependent, which means that it is also spatially varying. In this paper, simulations using two random tensors with different diffusivities having the same fractional anisotropy but with different principle eigenvalues are carried out. Results reveal that for a fixed scan time, acquisition of repeated measurements can improve CFARI performance and that a spatially variable, data adaptive regularization parameter is beneficial in stabilizing results.

Paper Details

Date Published: 15 March 2011
PDF: 8 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79624J (15 March 2011); doi: 10.1117/12.878382
Show Author Affiliations
Fatma Elzahraa A. ElShahaby, The Johns Hopkins Univ. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)
Jerry L. Prince, The Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

© SPIE. Terms of Use
Back to Top