Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Transfer learning, Generalization
Motivation: Long MR procedure times often result in a shortage of patient data for specific cases, affecting the performance of data-dependent deep networks. Transfer learning offers a remedy, enabling pretrained models to adapt to new domains with limited data availability.
Goal(s): Our goal is to create a network capable of producing clinically acceptable reconstructions with limited data.
Approach: We leverage representation learning to refine low-resolution data and enhance final reconstructions in data-limited scenarios.
Results: Successful transfers with 100 and 40 training sample sets were achieved. Both networks achieve comparable results to the large dataset (240 samples) trained network.
Impact: Our approach has broader clinical uses beyond acquisition protocols, extending to vendor differences and scenarios with limited access to disease scans due to privacy concerns. It presents an opportunity to tackle limited data generalization challenges without adding architectural complexity.
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