Keywords: Machine Learning/Artificial Intelligence, Artifacts, Image quality
Magnetic resonance imaging (MRI) is a powerful imaging modality, but susceptible to various image quality problems. Today, technicians conduct image quality assurance (IQA) during scan-time as a manual, time-consuming, and subjective process. We propose a method towards adaptable automated IQA of MR images without the need of a large, annotated image database for training. Our method implements a machine learning-based module that uses multiple predictions from an ensemble of deep learning models trained with image quality metrics. The sensitivity of this method to detect image quality problems is adaptable to clinical requirements of the end user.
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