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

arXiv: https://arxiv.org/abs/1911.06816 · HTML: https://mlsanity.com/paper/1911.06816/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2019.

## Built on by (most cited first, 8 of 8)

- [Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review](https://mlsanity.com/paper/2102.01530/) (2021)
- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)
- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [3D-QCNet -- A Pipeline for Automated Artifact Detection in Diffusion MRI images](https://mlsanity.com/paper/2103.05285/) (2021)
- [FetMRQC: Automated Quality Control for fetal brain MRI](https://mlsanity.com/paper/2304.05879/) (2023)
- [Artificial intelligence-based locoregional markers of brain peritumoral microenvironment](https://mlsanity.com/paper/2208.14445/) (2022)
- [Unsupervised dMRI Artifact Detection via Angular Resolution Enhancement and Cycle Consistency Learning](https://mlsanity.com/paper/2409.15883/) (2024)
- [Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis](https://mlsanity.com/paper/2603.25886/) (2026)

## Abstract (arXiv)

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.