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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 NahavandiSaeid Nahavandi
Magnetic 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.

History

Event

IASTED Biomedical Engineering. Conference (10th : 2013 : Innsbruck, Austria)

Pagination

360 - 367

Publisher

ACTA Press

Location

Innsbruck, Austria

Place of publication

Calgary, Alb.

Start date

2013-02-13

End date

2013-02-15

ISBN-13

9780889869424

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2013, ACTA Press

Editor/Contributor(s)

A Boccaccini

Title of proceedings

BioMed 2013 : Proceedings of the 10th IASTED International Conference on Biomedical Engineering