  Diffusion MRI (dMRI) generally suffers from eddy currents induced by strong
diffusion gradients, which introduce artefacts that can impair subsequent
diffusion metric analysis. Existing correction techniques assume that eddy
currents do not decay during acquisition, which is generally effective for
traditional Pulsed Gradient Spin Echo (PGSE) diffusion encoding. However, these
methods do not necessarily apply to advanced forms of dMRI that require
substantial gradient slewing, such as Oscillating Gradient Spin Echo (OGSE). In
this work, dynamic field monitoring was used to characterize diffusion gradient
induced eddy currents for both PGSE and OGSE to assess the applicability of
these typical assumptions. The performance of an in-house algorithm (TVEDDY)
that models eddy current decay was evaluated by correcting in-vivo PGSE and
OGSE brain images and comparing correction quality with conventional methods
using mean-squared error (MSE) between diffusion weighted images acquired with
opposite polarity diffusion gradients. As a ground truth comparison, spatially
varying field dynamics up to third order in space were used in a model-based
iterative reconstruction to eliminate distortions. Time-varying eddy currents
were observed for OGSE, which introduced blurring along the phase-encode
direction that was not reduced using the traditional approach, but was
diminished considerably with TVEDDY and model-based reconstruction. No MSE
difference was observed between the conventional approach and TVEDDY for PGSE,
but for OGSE TVEDDY resulted in significantly lower MSE. The
field-monitoring-informed model-based reconstruction had the lowest MSE for
both PGSE and OGSE. Accordingly, this work establishes that it is possible to
estimate time-varying eddy currents from the diffusion data itself, which
provides substantial image quality improvements for gradient-intensive dMRI
acquisitions like OGSE.
