Towards Real-Time RF Coil Failure Recognition Using Deep Transfer Learning

Motivation: RF coil failures are often not visually recognizable. Quality control is only done weekly or monthly, leading to days to weeks where diagnostic images may be negatively impacted. Goal(s): Identify RF coil failures on patient images using deep transfer learning. Approach: >10,000 passed and failed images from 50 patients were used to train 4 pre-trained deep learning models using 2 different pipelines: (1) shuffled all images into train and test, and (2) shuffled by each patients’ images. Results: EfficientNet V2 (L) was the highest performing model, achieving 99% accuracy for pipeline 1, and 55% for pipeline 2. Other models showed similar results. Impact: Introducing a deep learning model that can identify radiofrequency coil failures on patient images would avoid costly rescans of patients whose images were only determined to be poor after a failure was detected on a later quality control scan.