Efficient Compressed Sensing Magnetic Resonance Imaging #br#
Based on Compound Regularization

doi:10.3969/j.issn.1006-2475.2016.05.007

Abstract

Abstract:

Compressed sensing(CS) is well utilized to accelerate magnetic resonance imaging(MRI) without degrading images quality. The present compound regularization imaging

models seldom select different regularization tools according to the different structural features of images. The imaging methods thus require high sampling rates to obtain

images with diagnostic value. In this paper, a novel compound regularization CS MRI model integrating two regularization tools: uniform discrete curvelet transform and total

variation, is introduced. It includes spatial image and lowpass subband coefficients total variation regularization, highpass subbands coefficients l1

regularization and kspace data fidelity constraint. Then this CS MRI model formulation is solved via variable splitting and alternating direction method of multipliers.

Simulated results on in vivo data are evaluated by objective indices and visual perception, which indicates that the proposed method outperforms the existing regularization

models established on the total variation and wavelets under the same sampling rate.

Key words: compressed sensing, magnetic resonance imaging, uniform discrete curvelet transform, variable splitting, alternating direction method of multipliers

CLC Number:

TP301.6

SHEN Huijun. Efficient Compressed Sensing Magnetic Resonance Imaging #br# Based on Compound Regularization[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.05.007.

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Efficient Compressed Sensing Magnetic Resonance Imaging #br# Based on Compound Regularization

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