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Locally sparsified compressive sensing in magnetic resonance imaging

Nahavandi, Saeid, Razzaq, Fuleah A., Mohamed, Shady, Bhatti, Asim and Brotchie, Peter 2015, Locally sparsified compressive sensing in magnetic resonance imagingIntegrated systems: innovations and applications, Springer, New York, N.Y., pp.195-210, doi: 10.1007/978-3-319-15898-3_12.

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Title Locally sparsified compressive sensing in magnetic resonance imaging
Author(s) Nahavandi, Saeid
Razzaq, Fuleah A.
Mohamed, ShadyORCID iD for Mohamed, Shady
Bhatti, AsimORCID iD for Bhatti, Asim
Brotchie, Peter
Title of book Integrated systems: innovations and applications
Publication date 2015
Start page 195
End page 210
Total pages 16
Publisher Springer
Place of Publication New York, N.Y.
Summary Magnetic Resonance Imaging (MRI) is a widely used technique for acquiring images of human organs/tissues. Due to its complex imaging process, it consumes a lot of time to produce a high quality image. Compressive Sensing (CS) has been used by researchers for rapid MRI. It uses a global sparsity constraint with variable density random sampling and L1 minimisation. This work intends to speed up the imaging process by exploiting the non-uniform sparsity in the MR images. Locally Sparsified CS suggests that the image can be even better sparsified by applying local sparsity constraints. The image produced by local CS can further reduce the sample set. This paper establishes the basis for a methodology to exploit non-uniform nature of sparsity and to make the MRI process time efficient by using local sparsity constraints.
ISBN 9783319158976
Language eng
DOI 10.1007/978-3-319-15898-3_12
Field of Research 090609 Signal Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2015, Springer
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Document type: Book Chapter
Collection: Centre for Intelligent Systems Research
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