# Estimating Head Motion from MR-Images

arXiv: https://arxiv.org/abs/2302.14490 · HTML: https://mlsanity.com/paper/2302.14490/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Artifact- and content-specific quality assessment for MRI with image rulers](https://mlsanity.com/paper/2111.03780/) (2021)
- [Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges](https://mlsanity.com/paper/1912.02907/) (2019)

## Built on by (most cited first, 1 of 1)

- [Improving Quality Control Of MRI Images Using Synthetic Motion Data](https://mlsanity.com/paper/2502.00160/) (2025)

## Abstract (arXiv)

Head motion is an omnipresent confounder of magnetic resonance image (MRI) analyses as it systematically affects morphometric measurements, even when visual quality control is performed. In order to estimate subtle head motion, that remains undetected by experts, we introduce a deep learning method to predict in-scanner head motion directly from T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) images using motion estimates from an in-scanner depth camera as ground truth. Since we work with data from compliant healthy participants of the Rhineland Study, head motion and resulting imaging artifacts are less prevalent than in most clinical cohorts and more difficult to detect. Our method demonstrates improved performance compared to state-of-the-art motion estimation methods and can quantify drift and respiration movement independently. Finally, on unseen data, our predictions preserve the known, significant correlation with age.