An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging

• Published in: NeuroImage (Orlando, Fla.) Vol. 125; pp. 1063 - 1078
• Main Authors: Andersson, Jesper L.R., Sotiropoulos, Stamatios N.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.01.2016; Elsevier Limited; Academic Press
• Subjects: Algorithms; Diffusion; Diffusion Magnetic Resonance Imaging - methods; Eddy current; Estimates; Humans; Image Interpretation, Computer-Assisted - methods; Movement; Registration; Scanners; Similarity measures; Susceptibility; Movement; Diffusion; Eddy current; Susceptibility; Registration
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2015.10.019

In this paper we describe a method for retrospective estimation and correction of eddy current (EC)-induced distortions and subject movement in diffusion imaging. In addition a susceptibility-induced field can be supplied and will be incorporated into the calculations in a way that accurately reflects that the two fields (susceptibility- and EC-induced) behave differently in the presence of subject movement. The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b-value data where the contrast is vastly different in different volumes. In addition we show that the linear EC-model commonly used is insufficient for the data used in the present paper (high spatial and angular resolution data acquired with Stejskal–Tanner gradients on a 3T Siemens Verio, a 3T Siemens Connectome Skyra or a 7T Siemens Magnetome scanner) and that a higher order model performs significantly better. The method is already in extensive practical use and is used by four major projects (the WU-UMinn HCP, the MGH HCP, the UK Biobank and the Whitehall studies) to correct for distortions and subject movement. •We present a new method for correction of eddy current-induced distortions and subject movement in diffusion data.•It is based on alignment to predictions based on a Gaussian process•The results indicate that one can achieve reliable corrections even for high b-value data (b=7000).•A comparison to correlation ratio based registration (eddy_correct) indicates that the new method is vastly superior.