QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images

TL;DR

This paper introduces QC-Automator, a deep learning tool that automates quality control for diffusion MRI data, accurately detecting various artifacts to improve data reliability in large-scale studies.

Contribution

The paper presents a novel deep learning-based QC tool for dMRI that handles multiple artifact types with high accuracy and demonstrates its applicability across diverse datasets.

Findings

Achieved 98% accuracy in artifact detection.

Handled a variety of artifacts including motion and ghosting.

Demonstrated replicability on different datasets.

Abstract

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.