Distortion correction of EPI data using multimodal nonrigid registration with an anisotropic regularization

• Verfasst von: Glodeck, Daniel [VerfasserIn]
• Verfasst von: Hesser, Jürgen [VerfasserIn]
• Verfasst von: Zheng, Lei [VerfasserIn]
• Titel: Distortion correction of EPI data using multimodal nonrigid registration with an anisotropic regularization
• Verf.angabe: Daniel Glodeck, Jürgen Hesser, Lei Zheng
• E-Jahr: 2016
• Jahr: February 2016
• Umfang: 10 S.
• Fussnoten: Gesehen am 28.01.2019
• Titel Quelle: Enthalten in: Magnetic resonance imaging
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1982
• Jahr Quelle: 2016
• Band/Heft Quelle: 34(2016), 2, Seite 127-136
• ISSN Quelle: 1873-5894
• DOI: doi:10.1016/j.mri.2015.10.032
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Diffusion magnetic resonance imaging
• Sach-SW: Distortion correction
• Sach-SW: Echo planar imaging
• Sach-SW: Image pyramid
• Sach-SW: Image registration
• Sach-SW: Normalized mutual information
• K10plus-PPN: 1586643053

Abstract

In this paper, a novel strategy for correcting both geometric and image intensity distortions of echo-planar imaging (EPI) MRI data is presented. To deal with small local distortions caused by rapid changes of the magnetic field, an improved multimodal registration framework using normalized mutual information (NMI) in combination with a multi-scale technique is presented to estimate a dense displacement field. To ensure the robustness of this high dimensional ill-posed inverse problem, a novel anisotropic regularization functional is used. In order to quantify geometric distortions, a new quality measure, called standardized contour distance (SCD), is introduced. It uses the outer structure shape (OSS) information as basis for the evaluation. The new registration method was evaluated with one monomodal phantom data set and two multimodal human brain data sets (BrainSuite trainings data, SPM Subject data). By comparing with recent and efficient techniques of the state of the art, in the monomodal case, the new approach achieves results comparable to the sum of squared differences as data term. In the multimodal cases, our new registration strategy improves the mean of the SCD from 0.96±0.11 to 0.60±0.13 in case of the SPM Subject data and from 0.92±0.07 to 0.78±0.11 in case of the BrainSuite trainings data.