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Location constrained approximate message passing for compressed sensing MRI
Author(s) -
Sung Kyunghyun,
Daniel Bruce L.,
Hargreaves Brian A.
Publication year - 2013
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.24468
Subject(s) - thresholding , computer science , compressed sensing , message passing , regularization (linguistics) , iterative method , computation , algorithm , constraint (computer aided design) , regular polygon , mathematical optimization , artificial intelligence , mathematics , image (mathematics) , parallel computing , geometry
Iterative thresholding methods have been extensively studied as faster alternatives to convex optimization methods for solving large‐sized problems in compressed sensing. A novel iterative thresholding method called LCAMP (Location Constrained Approximate Message Passing) is presented for reducing computational complexity and improving reconstruction accuracy when a nonzero location (or sparse support) constraint can be obtained from view shared images. LCAMP modifies the existing approximate message passing algorithm by replacing the thresholding stage with a location constraint, which avoids adjusting regularization parameters or thresholding levels. This work is first compared with other conventional reconstruction methods using random one‐dimention signals and then applied to dynamic contrast‐enhanced breast magnetic resonance imaging to demonstrate the excellent reconstruction accuracy (less than 2% absolute difference) and low computation time (5–10 s using Matlab) with highly undersampled three‐dimentional data (244 × 128 × 48; overall reduction factor = 10). Magn Reson Med 70:370–381, 2013. © 2012 Wiley Periodicals, Inc.