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Accelerating SENSE using compressed sensing
Author(s) -
Liang Dong,
Liu Bo,
Wang JiunJie,
Ying Leslie
Publication year - 2009
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.22161
Subject(s) - compressed sensing , sense (electronics) , computer science , imaging phantom , cartesian coordinate system , encoding (memory) , set (abstract data type) , acceleration , artificial intelligence , sensitivity (control systems) , computer vision , reduction (mathematics) , real time mri , data set , algorithm , pattern recognition (psychology) , magnetic resonance imaging , mathematics , optics , physics , radiology , medicine , geometry , classical mechanics , electronic engineering , electrical engineering , programming language , engineering
Both parallel MRI and compressed sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data. The combination of parallel MRI and CS for further acceleration is of great interest. In this paper, we propose a novel method to combine sensitivity encoding (SENSE), one of the standard methods for parallel MRI, and compressed sensing for rapid MR imaging (SparseMRI), a recently proposed method for applying CS in MR imaging with Cartesian trajectories. The proposed method, named CS‐SENSE, sequentially reconstructs a set of aliased reduced‐field‐of‐view images in each channel using SparseMRI and then reconstructs the final image from the aliased images using Cartesian SENSE. The results from simulations and phantom and in vivo experiments demonstrate that CS‐SENSE can achieve a reduction factor higher than those achieved by SparseMRI and SENSE individually and outperform the existing method that combines parallel MRI and CS. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.