Estimating Head Motion from Mr-Images

Pollak, Clemens; Reuter, Martin; Kügler, David

doi:10.1109/ISBI53787.2023.10230717

Contribution to a conference proceedings/Contribution to a book

DZNE-2024-01000

Estimating Head Motion from Mr-Images

Pollak, C. (First author)DZNE* ; Kügler, D.DZNE* ; Reuter, M. (Last author)DZNE*

2023
IEEE

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) : [Proceedings] - IEEE, 2023. - ISBN 978-1-6654-7358-3 - doi:10.1109/ISBI53787.2023.10230717
2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 18 Apr 2023 - 21 Apr 2023 IEEE pp. 1-5 (2023) [10.1109/ISBI53787.2023.10230717]

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Abstract: 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.

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