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Defining sub-regions in locally sparsified compressive sensing MRI
conference contribution
posted on 2013-01-01, 00:00 authored by F Razzaq, Shady MohamedShady Mohamed, Asim BhattiAsim Bhatti, Saeid NahavandiMagnetic Resonance Imaging (MRI) is an important imaging technique. However, it is a time consuming process. The aim of this study is to make the imaging process ef?cient. MR images are sparse in the sensing domain and Compressive Sensing exploits this sparsity. Locally sparsi?ed Compressed Sensing is a specialized case of CS which sub-divides the image and sparsi?es each region separately; later samples are taken based on sparsity level in that region. In this paper, a new structured approach is presented for de?ning the size and locality of sub-regions in image. Experiments were done on the regions de?ned by proposed framework and local sparsity constraints were used to achieve high sparsity level and to reduce the sample set. Experimental results and their comparison with global CS is presented in the paper.
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IASTED Biomedical Engineering. Conference (10th : 2013 : Innsbruck, Austria)Pagination
360 - 367Publisher
ACTA PressLocation
Innsbruck, AustriaPlace of publication
Calgary, Alb.Publisher DOI
Start date
2013-02-13End date
2013-02-15ISBN-13
9780889869424Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, ACTA PressEditor/Contributor(s)
A BoccacciniTitle of proceedings
BioMed 2013 : Proceedings of the 10th IASTED International Conference on Biomedical EngineeringUsage metrics
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