In MRI, many deep learning-based solutions often degrade when deployed in different clinical scenarios due to lack of large training datasets. Moreover, the knowledge about the reconstruction problem is constrained to the data seen during training. This paper presents a transfer learning approach to deal with the problems of data scarcity and differences in the source and target domain while reconstructing the MR images using deep learning. Experimental results show successful domain transfer between the source and target datasets in terms of change in magnetic field strength, anatomy and Acceleration Factor (AF).
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