title: Regularized field map estimation in MRI.
abstract: In fast magnetic resonance (MR) imaging with long readout times, such as echo-planar imaging (EPI) and spiral scans, it is important to correct for the effects of field inhomogeneity to reduce image distortion and blurring. Such corrections require an accurate field map, a map of the off-resonance frequency at each voxel. Standard field map estimation methods yield noisy field maps, particularly in image regions with low spin density. This paper describes regularized methods for field map estimation from two or more MR scans having different echo times. These methods exploit the fact that field maps are generally smooth functions. The methods use algorithms that decrease monotonically a regularized least-squares cost function, even though the problem is highly nonlinear. Results show that the proposed regularized methods significantly improve the quality of field map estimates relative to conventional unregularized methods.
authors.affiliation: Department of Electrical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Amanda K
authors.initials: AK
authors.lastname: Funai
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Fessler
authors.forename: Desmond T B
authors.initials: DT
authors.lastname: Yeo
authors.forename: Valur T
authors.initials: VT
authors.lastname: Olafsson
authors.forename: Douglas C
authors.initials: DC
authors.lastname: Noll
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity