Premium
Improved parallel MR imaging using a coefficient penalized regularization for GRAPPA reconstruction
Author(s) -
Liu Wentao,
Tang Xin,
Ma Yajun,
Gao JiaHong
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.24344
Subject(s) - tikhonov regularization , regularization (linguistics) , imaging phantom , image quality , computer science , iterative reconstruction , correlation coefficient , algorithm , mathematics , artificial intelligence , pattern recognition (psychology) , image (mathematics) , nuclear medicine , inverse problem , mathematical analysis , medicine , machine learning
A novel coefficient penalized regularization method for generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction is developed for improving MR image quality. In this method, the fitting coefficients of the source data are weighted with different penalty factors, which are highly dependent upon the relative displacements from the source data to the target data in k ‐space. The imaging data from both phantom testing and in vivo MRI experiments demonstrate that the coefficient penalized regularization method in GRAPPA reconstruction is able to reduce noise amplification to a greater degree. Therefore, the method enhances the quality of images significantly when compared to the previous least squares and Tikhonov regularization methods. Magn Reson Med 69:1109–1114, 2013. © 2012 Wiley Periodicals, Inc.