Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian Estimation
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 s...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2009-12-01
|
Series: | Sensors |
Subjects: | |
Online Access: | http://www.mdpi.com/1424-8220/10/1/266/ |
id |
doaj-3cf0ca95032c4a4daf4cdc56bb455406 |
---|---|
record_format |
Article |
spelling |
doaj-3cf0ca95032c4a4daf4cdc56bb4554062020-11-25T00:35:00ZengMDPI AGSensors1424-82202009-12-0110126627910.3390/s100100266Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian EstimationFabio BaseliceGiampaolo FerraioliAymen ShabouField 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. http://www.mdpi.com/1424-8220/10/1/266/Magnetic Resonance Imagingfield map estimationphase unwrappingbayesian estimationgraph-cutsMarkov Random Field |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fabio Baselice Giampaolo Ferraioli Aymen Shabou |
spellingShingle |
Fabio Baselice Giampaolo Ferraioli Aymen Shabou Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian Estimation Sensors Magnetic Resonance Imaging field map estimation phase unwrapping bayesian estimation graph-cuts Markov Random Field |
author_facet |
Fabio Baselice Giampaolo Ferraioli Aymen Shabou |
author_sort |
Fabio Baselice |
title |
Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian Estimation |
title_short |
Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian Estimation |
title_full |
Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian Estimation |
title_fullStr |
Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian Estimation |
title_full_unstemmed |
Field Map Reconstruction in Magnetic Resonance Imaging Using Bayesian Estimation |
title_sort |
field map reconstruction in magnetic resonance imaging using bayesian estimation |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2009-12-01 |
description |
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. |
topic |
Magnetic Resonance Imaging field map estimation phase unwrapping bayesian estimation graph-cuts Markov Random Field |
url |
http://www.mdpi.com/1424-8220/10/1/266/ |
work_keys_str_mv |
AT fabiobaselice fieldmapreconstructioninmagneticresonanceimagingusingbayesianestimation AT giampaoloferraioli fieldmapreconstructioninmagneticresonanceimagingusingbayesianestimation AT aymenshabou fieldmapreconstructioninmagneticresonanceimagingusingbayesianestimation |
_version_ |
1725310965377925120 |