Field map reconstruction in magnetic resonance imaging using Bayesian estimation

• Published in: Sensors (Basel, Switzerland) Vol. 10; no. 1; pp. 266 - 279
• Main Authors: Baselice, Fabio, Ferraioli, Giampaolo, Shabou, Aymen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2010; Molecular Diversity Preservation International (MDPI)
• Subjects: Algorithms; Bayes Theorem; bayesian estimation; Electrons; field map estimation; graph-cuts; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic fields; Magnetic Resonance Imaging; Magnetic Resonance Imaging - methods; Markov Random Field; Pattern Recognition, Automated - methods; phase unwrapping; Reproducibility of Results; Sensitivity and Specificity; Sensors; Magnetic Resonance Imaging; graph-cuts; Markov Random Field; bayesian estimation; field map estimation; phase unwrapping
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s100100266

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.