Journal
MAGNETIC RESONANCE IN MEDICINE
Volume 61, Issue 1, Pages 145-152Publisher
WILEY
DOI: 10.1002/mrm.21799
Keywords
SENSE; parallel imaging; total variation regularization; Bregman iteration; compressed sensing
Funding
- National Science Foundation [CBET-0731226]
- UW System
- UWM Research Foundation
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In parallel imaging, the signal-to-noise ratio (SNR) of sensitivity encoding (SENSE) reconstruction is usually degraded by the ill-conditioning problem, which becomes especially serious at large acceleration factors. Existing regularization methods have been shown to alleviate the problem. However, they usually suffer from image artifacts at high acceleration factors due to the large data inconsistency resulting from heavy regularization. In this paper, we propose Bregman iteration for SENSE regularization. Unlike the existing regularization methods where the regularization function is fixed, the method adaptively updates the regularization function using the Bregman distance at different iterations, such that the iteration gradually removes the aliasing artifacts and recovers fine structures before the noise finally comes back. With a discrepancy principle as the stopping criterion, our results demonstrate that the reconstructed image using Bregman iteration preserves both sharp edges lost in Tikhonov regularization and fines structures missed in total variation (TV) regularization, while reducing more noise and aliasing artifacts. Magn Reson Med 61: 145-152, 2009. (C) 2009 Wiley-Liss, Inc.
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