  Quality assessment of diffusion MRI (dMRI) data is essential prior to any
analysis, so that appropriate pre-processing can be used to improve data
quality and ensure that the presence of MRI artifacts do not affect the results
of subsequent image analysis. Manual quality assessment of the data is
subjective, possibly error-prone, and infeasible, especially considering the
growing number of consortium-like studies, underlining the need for automation
of the process. In this paper, we have developed a deep-learning-based
automated quality control (QC) tool, QC-Automator, for dMRI data, that can
handle a variety of artifacts such as motion, multiband interleaving, ghosting,
susceptibility, herringbone and chemical shifts. QC-Automator uses
convolutional neural networks along with transfer learning to train the
automated artifact detection on a labeled dataset of ~332000 slices of dMRI
data, from 155 unique subjects and 5 scanners with different dMRI acquisitions,
achieving a 98% accuracy in detecting artifacts. The method is fast and paves
the way for efficient and effective artifact detection in large datasets. It is
also demonstrated to be replicable on other datasets with different acquisition
parameters.
