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Proceedings Paper

A total-variation-based regularization strategy in magnetic resonance imaging
Author(s): Germana Landi; Elena Loli Piccolomini; Fabiana Zama
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Paper 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.

Paper Details

Date Published: 22 October 2004
PDF: 11 pages
Proc. SPIE 5562, Image Reconstruction from Incomplete Data III, (22 October 2004); doi: 10.1117/12.559393
Show Author Affiliations
Germana Landi, Univ. degli Studi di Bologna (Italy)
Elena Loli Piccolomini, Univ. degli Studi di Bologna (Italy)
Fabiana Zama, Univ. degli Studi di Bologna (Italy)

Published in SPIE Proceedings Vol. 5562:
Image Reconstruction from Incomplete Data III
Philip J. Bones; Michael A. Fiddy; Rick P. Millane, Editor(s)

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