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Proceedings Paper

Compressed sensing MRI using higher order multi-scale FREBAS for sparsifying transform function
Author(s): S. Ito; K. Ito; M. Shibuya; Y. Yamada
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Paper Abstract

In recent years, compressed sensing (CS) has attracted considerable attention in areas such as rapid magnetic resonance (MR) imaging. Signal sparsity is an essential condition for compressed sensing. In this work, a multi-scale sparsifying transform based on the Fresnel transform (FREBAS) is adopted in order to improve the quality of CS images. The experimental results demonstrate that by increasing the sparsity of the image in the FREBAS transform domain, curved features in MR images can be more faithfully reconstructed than is possible using the traditional wavelet transform or curvelet transform particularly for low sampling rates in k-space. In addition, proposed method is robust to the selection of sampling trajectory of NMR signal.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94130H (20 March 2015); doi: 10.1117/12.2078004
Show Author Affiliations
S. Ito, Utsunomiya Univ. (Japan)
K. Ito, Utsunomiya Univ. (Japan)
M. Shibuya, Utsunomiya Univ. (Japan)
Y. Yamada, Utsunomiya Univ. (Japan)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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