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Locally sparsified compressive sensing for improved MR image qualtity

conference contribution
posted on 2013-01-01, 00:00 authored by F Razzaq, Shady MohamedShady Mohamed, Asim BhattiAsim Bhatti, Saeid 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.



IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)


2163 - 2167




Manchester, England

Place of publication

Piscataway, N.J.

Start date


End date




Publication classification

E1 Full written paper - refereed

Copyright notice

2013, IEEE

Title of proceedings

SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics