Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, Magnetic Resonance CholangiopancreatographyMR cholangiopancreatography (MRCP) is a special MRI technique to visualize the biliary systems. Deep Learning-based (DL) reconstruction models have shown to reduce scan time from many anatomical regions. However, they generally require large training datasets. This is challenging for applications like MRCP, where public datasets are not available. This work analyzes two approaches to training a DL model for highly accelerated MRCP reconstruction: training from scratch using a small MRCP dataset and fine-tuning a model pretrained on a public knee dataset. Results show that despite of the substantial data domain shift between training and testing, fine-tuning outperformed training from scratch.
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