A statistical approach to SENSE regularization with arbitrary k-space trajectories.

Abstract:

: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.

journal_name

Magn Reson Med

authors

Ying L,Liu B,Steckner MC,Wu G,Wu M,Li SJ

doi

10.1002/mrm.21665

subject

Has Abstract

pub_date

2008-08-01 00:00:00

pages

414-21

issue

2

eissn

0740-3194

issn

1522-2594

journal_volume

60

pub_type

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