  Increasing demand for high field magnetic resonance (MR) scanner indicates
the need for high-quality MR images for accurate medical diagnosis. However,
cost constraints, instead, motivate a need for algorithms to enhance images
from low field scanners. We propose an approach to process the given low field
(3T) MR image slices to reconstruct the corresponding high field (7T-like)
slices. Our framework involves a novel architecture of a merged convolutional
autoencoder with a single encoder and multiple decoders. Specifically, we
employ three decoders with random initializations, and the proposed training
approach involves selection of a particular decoder in each weight-update
iteration for back propagation. We demonstrate that the proposed algorithm
outperforms some related contemporary methods in terms of performance and
reconstruction time.
