# Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence

arXiv: https://arxiv.org/abs/2402.08749 · HTML: https://mlsanity.com/paper/2402.08749/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 13 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledge](https://mlsanity.com/paper/2103.06168/) (2021)
- [Artifact- and content-specific quality assessment for MRI with image rulers](https://mlsanity.com/paper/2111.03780/) (2021)

## Built on by (most cited first, 2 of 2)

- [Automated Motion Artifact Check for MRI (AutoMAC-MRI): An Interpretable Framework for Motion Artifact Detection and Severity Assessment](https://mlsanity.com/paper/2512.15315/) (2025)
- [Attention-Gated Convolutional Networks for Scanner-Agnostic Quality Assessment](https://mlsanity.com/paper/2604.15059/) (2026)

## Abstract (arXiv)

Quality assessment, including inspecting the images for artifacts, is a critical step during MRI data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning model to detect rigid motion in T1-weighted brain images. We leveraged a 2D CNN for three-class classification and tested it on publicly available retrospective and prospective datasets. Grad-CAM heatmaps enabled the identification of failure modes and provided an interpretation of the model's results. The model achieved average precision and recall metrics of 85% and 80% on six motion-simulated retrospective datasets. Additionally, the model's classifications on the prospective dataset showed a strong inverse correlation (-0.84) compared to average edge strength, an image quality metric indicative of motion. This model is part of the ArtifactID tool, aimed at inline automatic detection of Gibbs ringing, wrap-around, and motion artifacts. This tool automates part of the time-consuming QA process and augments expertise on-site, particularly relevant in low-resource settings where local MR knowledge is scarce.