DocumentCode :
677944
Title :
Locally Sparsified Compressive Sensing for Improved MR Image Quality
Author :
Razzaq, Fuleah A. ; Mohamed, Salina ; Bhatti, A. ; Nahavandi, S.
Author_Institution :
Centre for Intell. Syst. Res., Deakin Univ., Geelong, VIC, Australia
fYear :
2013
fDate :
13-16 Oct. 2013
Firstpage :
2163
Lastpage :
2167
Abstract :
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.
Keywords :
biomedical MRI; compressed sensing; image denoising; medical image processing; sampling methods; CS MRI; energy distribution; global constraint; image acquisition; image localization; image quality; image regeneration; improved MR image quality; local constraints; locally sparsified compressive sensing; magnetic resonance imaging; medical images; noise artefacts; rapid imaging; Biomedical imaging; Compressed sensing; Image quality; Image reconstruction; Magnetic resonance imaging; Noise; Compressive Sensing; L1 Minimization; Magnetic Resonance Imaging; Sparse Signals;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on
Conference_Location :
Manchester
Type :
conf
DOI :
10.1109/SMC.2013.370
Filename :
6722123
Link To Document :
بازگشت