Locally sparsified compressive sensing for improved MR image qualtity

Razzaq, Fuleah A., Mohamed, Shady, Bhatti, Asim and Nahavandi, Saeid 2013, Locally sparsified compressive sensing for improved MR image qualtity, in SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N.J., pp. 2163-2167.

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Title Locally sparsified compressive sensing for improved MR image qualtity
Author(s) Razzaq, Fuleah A.
Mohamed, ShadyORCID iD for Mohamed, Shady orcid.org/0000-0002-8851-1635
Bhatti, AsimORCID iD for Bhatti, Asim orcid.org/0000-0001-6876-1437
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)
Conference location Manchester, England
Conference dates 13-16 Oct. 2013
Title of proceedings SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE Systems, Man and Cybernetics Conference
Start page 2163
End page 2167
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) magnetic resonance imaging
compressive sensing
sparse signals
fourier transform
signal-to noise ratio (SNR)
L I minimization
Summary The fact that medical images have redundant information is exploited by researchers for faster image acquisition. Sample set or number of measurements were reduced in order to achieve rapid imaging. However, due to inadequate sampling, noise artefacts are inevitable in Compressive Sensing (CS) MRI. CS utilizes the transform sparsity of MR images to regenerate images from under sampled data. Locally sparsified Compressed Sensing is an extension of simple CS. It localises sparsity constraints for sub-regions rather than using a global constraint. This paper, presents a framework to use local CS for improving image quality without increasing sampling rate or without making the acquisition process any slower. This was achieved by exploiting local constraints. Localising image into independent sub-regions allows different sampling rates within image. Energy distribution of MR images is not even and most of noise occurs due to under-sampling in high energy regions. By sampling sub-regions based on energy distribution, noise artefacts can be minimized. Experiments were done using the proposed technique. Results were compared with global CS and summarized in this paper.
Language eng
Field of Research 080401 Coding and Information Theory
Socio Economic Objective 929999 Health not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058818

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