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A statistical approach to SENSE regularization with arbitrary k ‐space trajectories
Author(s) -
Ying Leslie,
Liu Bo,
Steckner Michael C.,
Wu Gaohong,
Wu Min,
Li ShiJiang
Publication year - 2008
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.21665
Subject(s) - tikhonov regularization , regularization (linguistics) , conjugate gradient method , regularization perspectives on support vector machines , inverse problem , algorithm , mathematics , variance reduction , early stopping , total variation denoising , computation , imaging phantom , computer science , mathematical optimization , noise reduction , artificial intelligence , mathematical analysis , statistics , physics , monte carlo method , optics , artificial neural network
SENSE reconstruction suffers from an ill‐conditioning problem, which increasingly lowers the signal‐to‐noise ratio (SNR) as the reduction factor increases. Ill‐conditioning also degrades the convergence behavior of iterative conjugate gradient reconstructions for arbitrary trajectories. Regularization techniques are often used to alleviate the ill‐conditioning problem. Based on maximum a posteriori statistical estimation with a Huber Markov random field prior, this study presents a new method for adaptive regularization using the image and noise statistics. The adaptive Huber regularization addresses the blurry edges in Tikhonov regularization and the blocky effects in total variation (TV) regularization. Phantom and in vivo experiments demonstrate improved image quality and convergence speed over both the unregularized conjugate gradient method and Tikhonov regularization method, at no increase in total computation time. Magn Reson Med 60:414–421, 2008. © 2008 Wiley‐Liss, Inc.