  Artifacts are a common occurrence in Diffusion MRI (dMRI) scans. Identifying
and removing them is essential to ensure the accuracy and viability of any post
processing carried out on these scans. This makes QC (quality control) a
crucial first step prior to any analysis of dMRI data. Several QC methods for
artifact detection exist, however they suffer from problems like requiring
manual intervention and the inability to generalize across different artifacts
and datasets. In this paper, we propose an automated deep learning (DL)
pipeline that utilizes a 3D-Densenet architecture to train a model on diffusion
volumes for automatic artifact detection. Our method is applied on a vast
dataset consisting of 9000 volumes sourced from 7 large clinical datasets.
These datasets comprise scans from multiple scanners with different gradient
directions, high and low b values, single shell and multi shell acquisitions.
Additionally, they represent diverse subject demographics like the presence or
absence of pathologies. Our QC method is found to accurately generalize across
this heterogenous data by correctly detecting 92% artifacts on average across
our test set. This consistent performance over diverse datasets underlines the
generalizability of our method, which currently is a significant barrier
hindering the widespread adoption of automated QC techniques. For these
reasons, we believe that 3D-QCNet can be integrated in diffusion pipelines to
effectively automate the arduous and time-intensive process of artifact
detection.
