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Locally sparsified compressive sensing for improved MR image qualtity
conference contributionposted on 2013-01-01, 00:00 authored by F Razzaq, Shady MohamedShady Mohamed, Asim BhattiAsim Bhatti, Saeid NahavandiSaeid Nahavandi
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.
EventIEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)
Pagination2163 - 2167
Place of publicationPiscataway, N.J.
Publication classificationE1 Full written paper - refereed
Copyright notice2013, IEEE
Title of proceedingsSMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics
magnetic resonance imagingcompressive sensingsparse signalsfourier transformsignal-to noise ratio (SNR)L I minimizationScience & TechnologyTechnologyComputer Science, CyberneticsComputer Science, Information SystemsEngineering, Electrical & ElectronicComputer ScienceEngineeringL1 MinimizationRECONSTRUCTION