NDLI: A blind compressive sensing frame work for accelerated dynamic MRI

Content Provider	IEEE Xplore Digital Library
Author	Lingala, S.G. Jacob, M.
Copyright Year	2012
Description	Author affiliation: Electrical and Computer Engineering, The University of Iowa, Iowa city, IA USA (Jacob, M.) \|\| Biomedical Engineering, The University of Iowa, Iowa city, IA USA (Lingala, S.G.)
Abstract	We propose a novel blind compressive sensing (BCS) frame work to recover dynamic images from under-sampled measurements. This scheme models the the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. The dictionary and the sparse coefficients are simultaneously estimated from the under-sampled measurements. Since the number of degrees of freedom of this model is much smaller than that of current low-rank methods, this scheme is expected to provide improved reconstructions for datasets with considerable inter-frame motion. We develop an efficient majorize-minimize algorithm to solve for the dynamic images. We use a continuation strategy to minimize the convergence of the algorithm to local minima. Numerical comparisons of the BCS scheme with low-rank methods demonstrate the significant improvement in performance in the presence of motion.
Starting Page	1060
Ending Page	1063
File Size	549491
Page Count	4
File Format	PDF
ISBN	9781457718571
ISSN	19457928
e-ISBN	9781457718588
DOI	10.1109/ISBI.2012.6235741
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-02
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Acceleration Magnetic resonance imaging Image reconstruction Compressed sensing Heuristic algorithms
Content Type	Text
Resource Type	Article

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