  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 paper, we propose an ensemble learning
and deep learning framework for MR image super-resolution. In our study, we
first enlarged low resolution images using 5 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, a
convolutional neural network 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 outcome any one of GAN outputs. 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.
