CONFERENCE PROCEEDINGS
Advanced Search
Home > Journals > J. Electron. Imag. > Volume 22 > Issue 2 > Article
Open Access
19 February 2013 Fast magnetic resonance imaging simulation with sparsely encoded wavelet domain data in a compressive sensing framework
Zheng Liu, Brian S. Nutter, Sunanda D. Mitra
Author Affiliations +
Journal of Electronic Imaging, Vol. 22, Issue 2, 021009 (February 2013). https://doi.org/10.1117/1.JEI.22.2.021009
Abstract
Randomly encoded compressive sensing (CS) has potential in fast acquisition of magnetic resonance imaging (MRI) data in most naturally compressible images. However, there is no guaranteed good performance for general applications by any of the traditional CS-MRI theoretical schemes developed so far. On the other hand, recent research demonstrates that adaptive sampling exploiting the tree structure of nonzero wavelet coefficients of signals allows more control over the sensing procedure in the form of feedback and improve the CS performance significantly. Following recent research strategies in CS-MRI, well-known adaptive sampling strategies in the wavelet domain, as used in image compression, to encode the MRI data yielding good reconstruction quality are introduced. Based on this underlying characteristic, adaptive k -space trajectories are designed with tailored spatially selective RF excitation pulses created by Battle-Lemarie wavelet functions. The input vectors formed from these significant samples of multilevel wavelet decomposed images are used in a CS framework for reconstruction of MR images. This MR image reconstruction uses a CS algorithm based on the minimization of total-variation regularized signal to provide stable results. The simulated results show that this approach can reduce almost 70% of MR image acquisition time and achieve good reconstructed image quality.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Citation Download Citation
Zheng Liu, Brian S. Nutter, and Sunanda D. Mitra "Fast magnetic resonance imaging simulation with sparsely encoded wavelet domain data in a compressive sensing framework," Journal of Electronic Imaging 22(2), 021009 (19 February 2013). https://doi.org/10.1117/1.JEI.22.2.021009
Published: 19 February 2013
JOURNAL ARTICLE
 13 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Lens.org Logo
CITATIONS
Cited by 4 scholarly publications.
Advertisement
Advertisement
KEYWORDS
Magnetic resonance imaging
Wavelets
Computer programming
Data acquisition
Brain
Neuroimaging
Signal to noise ratio
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top