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Algorithms 2014, 7(3), 276-294; doi:10.3390/a7030276
Article

Group Sparse Reconstruction of Multi-Dimensional Spectroscopic Imaging in Human Brain in vivo

1
, 2
 and 1,2,*
1 Department of Biomedical Engineering, UCLA, Los Angeles, CA 90025, USA 2 Bio-Medical Physics IDP, UCLA, Los Angeles, CA 90025, USA
* Author to whom correspondence should be addressed.
Received: 26 February 2014 / Revised: 29 April 2014 / Accepted: 26 May 2014 / Published: 26 June 2014
(This article belongs to the Special Issue Data Compression for the Life Sciences)
Download PDF [11707 KB, 27 June 2014; original version 26 June 2014]

Abstract

Four-dimensional (4D) Magnetic Resonance Spectroscopic Imaging (MRSI) data combining 2 spatial and 2 spectral dimensions provides valuable biochemical information in vivo; however, its 20–40 min acquisition time is too long to be used for a clinical protocol. Data acquisition can be accelerated by non-uniformly under-sampling (NUS) the ky t1 plane, but this causes artifacts in the spatial-spectral domain that must be removed by non-linear, iterative reconstruction. Previous work has demonstrated the feasibility of accelerating 4D MRSI data acquisition through NUS and iterative reconstruction using Compressed Sensing (CS), Total Variation (TV), and Maximum Entropy (MaxEnt) reconstruction. Group Sparse (GS) reconstruction is a variant of CS that exploits the structural sparsity of transform coefficients to achieve higher acceleration factors than traditional CS. In this article, we derive a solution to the GS reconstruction problem within the Split Bregman iterative framework that uses arbitrary transform grouping patterns of overlapping or non-overlapping groups. The 4D Echo-Planar Correlated Spectroscopic Imaging (EP-COSI) gray matter brain phantom and in vivo brain data are retrospectively under-sampled 2×, 4×, 6×, 8×, and 10___ and reconstructed using CS, TV, MaxEnt, and GS with overlapping or non-overlapping groups. Results show that GS reconstruction with overlapping groups outperformed the other reconstruction methods at each NUS rate for both phantom and in vivo data. These results can potentially reduce the scan time of a 4D EP-COSI brain scan from 40 min to under 5 min in vivo.
Keywords: group sparsity; compressed sensing; Split Bregman; convex optimization; spectroscopic imaging group sparsity; compressed sensing; Split Bregman; convex optimization; spectroscopic imaging
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Burns, B.L.; Wilson, N.E.; Thomas, M.A. Group Sparse Reconstruction of Multi-Dimensional Spectroscopic Imaging in Human Brain in vivo. Algorithms 2014, 7, 276-294.

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