Salman Ul Hassan Dar1,2, Mahmut Yurt1,2, and Tolga Çukur1,2,3
1Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, 3Neuroscience Program, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey
1Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, 3Neuroscience Program, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey
Here we proposed a few shot learning approach to address the issue of data scarcity in deep neural networks for accelerated MRI. The proposed approach enables data efficient training of deep neural networks by merging subject-driven priors with data-driven prior.
Figure. 1 - COMNET consists of an unrolled cascade of sub-networks where each sub-network consists of a calibration consistency (CC) block fused with a network consistency (NC) block, both followed by a data consistency (DC) block.
Figure. 2 - Average PSNR values of a) cT1, b) T2, and c) FLAIR images of test subjects as a function of number of training subjects (upper x-axis), and training samples (lower x-axis). COMNET requires just a few training samples from a single subject to outperform L1-SPIRiT. On the other hand, DNN on average requires around 90 samples from 9 different subjects to start performing better than L1-SPIRiT.