Sensors 2010, 10(1), 266-279; doi:10.3390/s100100266
Article

Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian Estimation

1 Dipartimento per le Tecnologie, Università degli Studi di Napoli Parthenope, Naples, Italy 2 Institut TELECOM, TELECOM ParisTech, CNRS LTCI, Paris, France
* Author to whom correspondence should be addressed.
Received: 30 October 2009; in revised form: 24 December 2009 / Accepted: 25 December 2009 / Published: 30 December 2009
(This article belongs to the Special Issue Image Sensors)
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Abstract: Field inhomogeneities in Magnetic Resonance Imaging (MRI) can cause blur or image distortion as they produce off-resonance frequency at each voxel. These effects can be corrected if an accurate field map is available. Field maps can be estimated starting from the phase of multiple complex MRI data sets. In this paper we present a technique based on statistical estimation in order to reconstruct a field map exploiting two or more scans. The proposed approach implements a Bayesian estimator in conjunction with the Graph Cuts optimization method. The effectiveness of the method has been proven on simulated and real data.
Keywords: Magnetic Resonance Imaging; field map estimation; phase unwrapping; bayesian estimation; graph-cuts; Markov Random Field

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MDPI and ACS Style

Baselice, F.; Ferraioli, G.; Shabou, A. Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian Estimation. Sensors 2010, 10, 266-279.

AMA Style

Baselice F, Ferraioli G, Shabou A. Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian Estimation. Sensors. 2010; 10(1):266-279.

Chicago/Turabian Style

Baselice, Fabio; Ferraioli, Giampaolo; Shabou, Aymen. 2010. "Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian Estimation." Sensors 10, no. 1: 266-279.

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