Publication | Open Access
An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging
3.7K
Citations
56
References
2015
Year
Image ReconstructionEngineeringSubject MovementPet-mriAdvanced ImagingMagnetic Resonance ImagingIntegrated ApproachCalibrationBiostatisticsRadiologyHealth SciencesDiffusion ImagingReconstruction TechniqueMedical ImagingNeuroimagingInverse ProblemsMri-guided Radiation TherapyMedical Image ComputingBrain ImagingDiffusion Mr ImagingRetrospective EstimationElectronic ImagingBiomedical ImagingNeuroscience
The correction method is already widely adopted by major projects such as the WU‑UMinn HCP, MGH HCP, UK Biobank, and Whitehall studies. This paper presents a retrospective method for estimating and correcting eddy‑current–induced distortions and subject motion in diffusion MRI. The approach registers each diffusion volume to a model‑free prediction of its expected appearance, incorporates a supplied susceptibility field, and treats susceptibility and eddy‑current effects separately during motion, enabling accurate correction even for high‑b‑value data with varying contrast. We demonstrate that a higher‑order eddy‑current model outperforms the commonly used linear model for high‑resolution data from 3T and 7T scanners, and that the method is already in extensive practical use by four major projects.
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.
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