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. Attached Files Name Description MIMEType Size Downloads Title Locally sparsified compressive sensing for improved MR image qualtity Author(s) Razzaq, Fuleah A. Mohamed, Shady orcid.org/0000-0002-8851-1635 Bhatti, Asim orcid.org/0000-0001-6876-1437 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 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 09 Dec 2013, 10:46:46 EST Mon, 09 Dec 2013, 12:43:54 EST Fri, 13 Dec 2013, 08:52:40 EST Mon, 17 Feb 2014, 15:01:21 EST Tue, 18 Feb 2014, 08:48:19 EST Mon, 11 Aug 2014, 10:13:46 EST Wed, 27 Aug 2014, 23:54:59 EST Wed, 21 Oct 2015, 14:56:38 EST Wed, 21 Oct 2015, 18:39:48 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 587 Abstract Views, 5 File Downloads  -  Detailed Statistics Created: Mon, 09 Dec 2013, 10:46:46 EST

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