A statistical approach to SENSE regularization with arbitrary k-space trajectories. Magn Reson Med 2008 Aug;60(2):414-21
Date
07/31/2008Pubmed ID
18666100DOI
10.1002/mrm.21665Scopus ID
2-s2.0-49049115524 (requires institutional sign-in at Scopus site) 39 CitationsAbstract
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.
Author List
Ying L, Liu B, Steckner MC, Wu G, Wu M, Li SJMESH terms used to index this publication - Major topics in bold
AlgorithmsBrain
Data Interpretation, Statistical
Heart
Humans
Image Enhancement
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging
Phantoms, Imaging
Reproducibility of Results
Sample Size
Sensitivity and Specificity
Signal Processing, Computer-Assisted