Improving Quality Control Of MRI Images Using Synthetic Motion Data

2025/01/31 by Charles Bricout, Bricout, Charles, Kang Ik K. Cho +23
Engineering · Medicine · #Advanced MRI Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Medical Imaging Techniques and Applications #Medical Imaging and Analysis #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2502.00160

openalex publication_date 2025/01/31 · openalex created_date 2025/02/05 · openalex updated_date 2026/08/01

Abstract

MRI quality control (QC) is challenging due to unbalanced and limited datasets, as well as subjective scoring, which hinder 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 synthetic 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.

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