Literature DB >> 25827811

Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization.

Chitresh Bhushan1, Justin P Haldar1, Soyoung Choi2, Anand A Joshi1, David W Shattuck3, Richard M Leahy4.   

Abstract

Diffusion MRI provides quantitative information about microstructural properties which can be useful in neuroimaging studies of the human brain. Echo planar imaging (EPI) sequences, which are frequently used for acquisition of diffusion images, are sensitive to inhomogeneities in the primary magnetic (B0) field that cause localized distortions in the reconstructed images. We describe and evaluate a new method for correction of susceptibility-induced distortion in diffusion images in the absence of an accurate B0 fieldmap. In our method, the distortion field is estimated using a constrained non-rigid registration between an undistorted T1-weighted anatomical image and one of the distorted EPI images from diffusion acquisition. Our registration framework is based on a new approach, INVERSION (Inverse contrast Normalization for VERy Simple registratION), which exploits the inverted contrast relationship between T1- and T2-weighted brain images to define a simple and robust similarity measure. We also describe how INVERSION can be used for rigid alignment of diffusion images and T1-weighted anatomical images. Our approach is evaluated with multiple in vivo datasets acquired with different acquisition parameters. Compared to other methods, INVERSION shows robust and consistent performance in rigid registration and shows improved alignment of diffusion and anatomical images relative to normalized mutual information for non-rigid distortion correction.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  B(0)-field inhomogeneity; Co-registration; Diffusion MRI; Distortion correction; Echo-planar imaging

Mesh:

Year:  2015        PMID: 25827811      PMCID: PMC4461504          DOI: 10.1016/j.neuroimage.2015.03.050

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


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