Sparse Sampling in MRI
 Philip J. Bones,
 Bing Wu
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Abstract
The significant time necessary to record each resonance echo from the volume being imaged in magnetic resonance imaging (MRI) has led to much effort to develop methods which take fewer measurements. Faster methods mean less time for the patient in the scanner, increased efficiency in the use of expensive scanning facilities, improved temporal resolution in studies involving moving organs or flows, and they lessen the probability that patient motion adversely affects the quality of the images. Images like those of the human body possess the property of sparsity, that is the property that in some transform space they can be represented much more compactly than in image space. The technique of compressed sensing, which aims to exploit sparsity, has therefore been adapted for use in MRI. This, coupled with the use of multiple receiving coils (parallel MRI) and the use of various forms of prior knowledge (e.g., support constraints in space and time), has resulted in significantly faster image acquisitions with only a modest penalty in the computational effort required for reconstruction. We describe the background motivation for adopting sparse sampling and show evidence of the sparse nature of biological image data sets. We briefly present the theory behind parallel MRI reconstruction, compressed sensing and the application of various forms of prior knowledge to image reconstruction. We summarize the work of other groups in applying these concepts to MRI and our own contributions. We finish with a brief conjecture on the possibilities for future development in the area.
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 Title
 Sparse Sampling in MRI
 Book Title
 Medical Image Processing
 Book Subtitle
 Techniques and Applications
 Pages
 pp 319339
 Copyright
 2011
 DOI
 10.1007/9781441997791_14
 Print ISBN
 9781441997692
 Online ISBN
 9781441997791
 Series Title
 Biological and Medical Physics, Biomedical Engineering
 Series ISSN
 16187210
 Publisher
 Springer New York
 Copyright Holder
 Springer Science+Business Media, LLC
 Additional Links
 Topics
 Industry Sectors
 eBook Packages
 Editors

 Geoff Dougherty ^{(ID1)}
 Editor Affiliations

 ID1. , Applied Physics and Medical Imaging, California State University Channel Isla
 Authors

 Philip J. Bones ^{(1)}
 Bing Wu ^{(2)}
 Author Affiliations

 1. University of Canterbury, Christchurch, New Zealand
 2. Duke University, Durham, NC, USA
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