Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results

Abstract

In this work we investigate the effects of echo planar imaging (EPI) distortions on diffusion tensor imaging (DTI) based fiber tractography results. We propose a simple experimental framework that would enable assessing the effects of EPI distortions on the accuracy and reproducibility of fiber tractography from a pilot study on a few subjects. We compare trajectories computed from two diffusion datasets collected on each subject that are identical except for the orientation of phase encode direction, either right–left (RL) or anterior–posterior (AP). We define metrics to assess potential discrepancies between RL and AP trajectories in association, commissural, and projection pathways. Results from measurements on a 3 Tesla clinical scanner indicated that the effects of EPI distortions on computed fiber trajectories are statistically significant and large in magnitude, potentially leading to erroneous inferences about brain connectivity. The correction of EPI distortion using an image-based registration approach showed a significant improvement in tract consistency and accuracy. Although obtained in the context of a DTI experiment, our findings are generally applicable to all EPI-based diffusion MRI tractography investigations, including high angular resolution (HARDI) methods. On the basis of our findings, we recommend adding an EPI distortion correction step to the diffusion MRI processing pipeline if the output is to be used for fiber tractography.

Choice of Diffusion Tensor Estimation Approach Affects Fiber Tractography of the Fornix in Preterm Brain

“…The diffusion-weighted images were first corrected for eddy currents, EPI distortion, and patient movement. 11,12 The diffusion tensor was then estimated according to 4 different methods: 1) LLS; 2) weighted linear least squares (WLLS); 3) nonlinear least squares (NLLS), and 4) robust estimation of tensors by outlier rejection (RESTORE). [13][14][15] Next, wholebrain tractography was performed for all datasets with the following parameters: fractional anisotropy (FA) threshold: 0.08; fiber length range: 15-500 mm; angle threshold 30°; and step size: 1 mm.…”

Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis

“…Although we found connectivity matrices rather invariant with respect to distortion, they are suspected to be significantly impacted by the susceptibility artifact on real data [14]. This hypothesis points to the need of more appropriate phantoms with denser connectivity matrices.…”

“…Recently, Irfanoglu et al [14] raised the question of distortion-derived impact on tractography. In this work, we propose an evaluation framework using a digital phantom designed for connectivity assessment.…”

Diffusion MRI: mitigation of magnetic field inhomogeneities

“…This characteristic may cause problems during the tractographic reconstruction of fibers (muscular or neural) [35], [36]. Through the use of the three measurement method or by measuring for several b-factors, this error can be very well corrected.…”