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Abstract #0569

Transfer learning for reconstruction of accelerated MRI acquisitions via neural networks

Salman Ul Hassan Dar1,2 and Tolga Çukur1,2,3

1Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, 3Neuroscience Graduate Program, Bilkent University, Ankara, Turkey

Neural network architectures have recently been proposed for reconstruction of undersampled MR acquisitions. These networks contain a large number of free parameters that typically have to be trained on orders-of-magnitude larger sets of fully-sampled MRI data. In practice, however, large datasets comprising thousands of images are rare. Here, we propose a transfer-learning approach to address the problem of data scarcity in training deep networks for accelerated MRI. Results show that networks obtained via transfer-learning using only tens of images in the testing domain achieve nearly identical performance to networks trained directly in the testing domain using thousands of MR images.

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