  Deep learning based generative adversarial networks (GAN) can effectively
perform image reconstruction with under-sampled MR data. In general, a large
number of training samples are required to improve the reconstruction
performance of a certain model. However, in real clinical applications, it is
difficult to obtain tens of thousands of raw patient data to train the model
since saving k-space data is not in the routine clinical flow. Therefore,
enhancing the generalizability of a network based on small samples is urgently
needed. In this study, three novel applications were explored based on parallel
imaging combined with the GAN model (PI-GAN) and transfer learning. The model
was pre-trained with public Calgary brain images and then fine-tuned for use in
(1) patients with tumors in our center; (2) different anatomies, including knee
and liver; (3) different k-space sampling masks with acceleration factors (AFs)
of 2 and 6. As for the brain tumor dataset, the transfer learning results could
remove the artifacts found in PI-GAN and yield smoother brain edges. The
transfer learning results for the knee and liver were superior to those of the
PI-GAN model trained with its own dataset using a smaller number of training
cases. However, the learning procedure converged more slowly in the knee
datasets compared to the learning in the brain tumor datasets. The
reconstruction performance was improved by transfer learning both in the models
with AFs of 2 and 6. Of these two models, the one with AF=2 showed better
results. The results also showed that transfer learning with the pre-trained
model could solve the problem of inconsistency between the training and test
datasets and facilitate generalization to unseen data.
