MRI Super Resolution With Ensemble Learning and Complementary Priors
Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, scan time, and throughput, it is often clinically challenging to obtain high quality MR images. The super resolution approach is potentially promising to improve MR image quality without any hardware upgrade. In this article, we propose an ensemble learning and deep learning framework for MR image super resolution. In our study, we first enlarged low resolution images using five commonly used super resolution algorithms and obtained differentially enlarged image datasets with complementary priors. Then, a generative adversarial network (GAN) is trained with each dataset to generate super resolution MR images. Finally, another GAN is used for ensemble learning that synergizes the outputs of GANs into the final MR super resolution images. According to our results, the ensemble learning results outperform any single GAN output component. Compared with some state of the art deep learning based super resolution methods, our approach is advantageous in suppressing artifacts and keeping more image details.
IEEE Transactions on Computational Imaging