@inproceedings{a37087b03ee0478daeca1f46450e66a6,
title = "Improving Quality Control of MRI Images Using Synthetic Motion Data",
abstract = "MRI quality control (QC) is challenging due to unbalanced and limited datasets, as well as subjective scoring, which hin-der the development of reliable automated QC systems. To address these issues, we introduce an approach that pretrains a model on synthetically generated motion artifacts before applying transfer learning for QC classification. This method not only improves the accuracy in identifying poor-quality scans but also reduces training time and resource requirements compared to training from scratch. By leveraging syn-thetic data, we provide a more robust and resource-efficient solution for QC automation in MRI, paving the way for broader adoption in diverse research settings.",
keywords = "Deep learning, MRI, Quality control",
author = "C. Bricout and K. Cho and M. Harms and O. Pasternak and C. Bearden and McGorry, \{P. D.\} and Kahn, \{R. S.\} and Kane, \{J. M.\} and B. Nelson and Woods, \{S. W.\} and Shenton, \{M. E.\} and S. Bouix and Kahou, \{S. Ebrahimi\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025 ; Conference date: 14-04-2025 Through 17-04-2025",
year = "2025",
doi = "10.1109/ISBI60581.2025.10981056",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "ISBI 2025 - 2025 IEEE 22nd International Symposium on Biomedical Imaging, Proceedings",
}