Paper
22 October 2004 A total-variation-based regularization strategy in magnetic resonance imaging
Germana Landi, Elena Loli Piccolomini, Fabiana Zama
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Abstract
In this paper we present some variational functionals for the regularization of Magnetic Resonance (MR) images, usually corrupted by noise and artifacts. The mathematical problem has a Tikhonov-like formulation, where the regularization functional is a nonlinear variational functional. The problem is numerically solved as an optimization problem with a quasi-Newton algorithm. The algorithm has been applied to MR images corrupted by noise and to dynamic MR images corrupted by truncation artifacts due to limited resolution. The results on test problems obtained from simulated and real data are presented. The functionals actually reduce noise and artifacts, provided that a good regularizing parameter is used.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Germana Landi, Elena Loli Piccolomini, and Fabiana Zama "A total-variation-based regularization strategy in magnetic resonance imaging", Proc. SPIE 5562, Image Reconstruction from Incomplete Data III, (22 October 2004); https://doi.org/10.1117/12.559393
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Cited by 1 scholarly publication.
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KEYWORDS
Magnetic resonance imaging

Image filtering

Data acquisition

Brain

Image resolution

Neuroimaging

Spatial resolution

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