DocumentCode :
2395976
Title :
An efficient algorithm for compressed MR imaging using total variation and wavelets
Author :
Ma, Shiqian ; Yin, Wotao ; Zhang, Yin ; Chakraborty, Amit
Author_Institution :
Dept. of IEOR, Columbia Univ., New York, NY
fYear :
2008
fDate :
23-28 June 2008
Firstpage :
1
Lastpage :
8
Abstract :
Compressed sensing, an emerging multidisciplinary field involving mathematics, probability, optimization, and signal processing, focuses on reconstructing an unknown signal from a very limited number of samples. Because information such as boundaries of organs is very sparse in most MR images, compressed sensing makes it possible to reconstruct the same MR image from a very limited set of measurements significantly reducing the MRI scan duration. In order to do that however, one has to solve the difficult problem of minimizing nonsmooth functions on large data sets. To handle this, we propose an efficient algorithm that jointly minimizes the lscr1 norm, total variation, and a least squares measure, one of the most powerful models for compressive MR imaging. Our algorithm is based upon an iterative operator-splitting framework. The calculations are accelerated by continuation and takes advantage of fast wavelet and Fourier transforms enabling our code to process MR images from actual real life applications. We show that faithful MR images can be reconstructed from a subset that represents a mere 20 percent of the complete set of measurements.
Keywords :
biomedical MRI; data compression; fast Fourier transforms; image coding; least squares approximations; medical image processing; wavelet transforms; MRI scan; compressed MR imaging; compressed sensing; fast Fourier transforms; fast wavelet transforms; iterative operator-splitting framework; least squares measure; total variation; Acceleration; Compressed sensing; Image coding; Image reconstruction; Iterative algorithms; Least squares methods; Magnetic resonance imaging; Mathematics; Signal processing; Signal processing algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Conference_Location :
Anchorage, AK
ISSN :
1063-6919
Print_ISBN :
978-1-4244-2242-5
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2008.4587391
Filename :
4587391
Link To Document :
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