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Proceedings Paper

MR images from fewer data
Author(s): Bahareh Vafadar; Philip J. Bones
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Paper Abstract

There is a strong motivation to reduce the amount of acquired data necessary to reconstruct clinically useful MR images, since less data means faster acquisition sequences, less time for the patient to remain motionless in the scanner and better time resolution for observing temporal changes within the body. We recently introduced an improvement in image quality for reconstructing parallel MR images by incorporating a data ordering step with compressed sensing (CS) in an algorithm named `PECS'. That method requires a prior estimate of the image to be available. We are extending the algorithm to explore ways of utilizing the data ordering step without requiring a prior estimate. The method presented here first reconstructs an initial image x1 by compressed sensing (with scarcity enhanced by SVD), then derives a data ordering from x1, R'1 , which ranks the voxels of x1 according to their value. A second reconstruction is then performed which incorporates minimization of the first norm of the estimate after ordering by R'1 , resulting in a new reconstruction x2. Preliminary results are encouraging.

Paper Details

Date Published: 15 October 2012
PDF: 12 pages
Proc. SPIE 8500, Image Reconstruction from Incomplete Data VII, 850003 (15 October 2012); doi: 10.1117/12.930926
Show Author Affiliations
Bahareh Vafadar, Univ. of Canterbury (New Zealand)
Philip J. Bones, Univ. of Canterbury (New Zealand)

Published in SPIE Proceedings Vol. 8500:
Image Reconstruction from Incomplete Data VII
Philip J. Bones; Michael A. Fiddy; Rick P. Millane, Editor(s)

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