k-t FOCUSS: A General Compressed Sensing Framework for High Resolution Dynamic MRI

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A model-based dynamic MRI called k-t BLAST/SENSE has drawn significant attention from the MR Imaging community because of its Improved spatio-temporal resolution. Recently, we showed that the k-t BLAST/SENSE corresponds to the special case of a new dynamic MRI algorithm called k-t FOCUSS that is optimal from a compressed sensing perspective. The main contribution of this article Is an extension of k-t FOCUSS to a more general framework with prediction and residual encoding, where the prediction provides an initial estimate and the residual encoding takes care of the remaining residual signals. Two prediction methods, RIGR and motion estimation/compensation scheme, are proposed, which significantly sparsify the residual signals. Then, using a more sophisticated random sampling pattern and optimized temporal transform, the residual signal can be effectively estimated from a very small number of k-t samples. Experimental results show that excellent reconstruction can be achieved even from severely limited k-t samples without allasing artifacts. Magn Reson Mad 61:103-116,2009. (C) 2008 Wiley-Liss, Inc.
Publisher
JOHN WILEY & SONS INC
Issue Date
2009-01
Language
English
Article Type
Article
Keywords

STATE FREE PRECESSION; MINIMUM NORM ALGORITHM; MAGNETIC-RESONANCE; SIGNAL RECONSTRUCTION; BLAST

Citation

MAGNETIC RESONANCE IN MEDICINE, v.61, no.1, pp.103 - 116

ISSN
0740-3194
DOI
10.1002/mrm.21757
URI
http://hdl.handle.net/10203/94988
Appears in Collection
AI-Journal Papers(저널논문)
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