  Long acquisition time (AQT) due to series acquisition of multi-modality MR
images (especially T2 weighted images (T2WI) with longer AQT), though
beneficial for disease diagnosis, is practically undesirable. We propose a
novel deep network based solution to reconstruct T2W images from T1W images
(T1WI) using an encoder-decoder architecture. The proposed learning is aided
with semantic features by using multi-channel input with intensity values and
gradient of image in two orthogonal directions. A reconstruction module (RM)
augmenting the network along with a domain adaptation module (DAM) which is an
encoder-decoder model built-in with sharp bottleneck module (SBM) is trained
via modular training. The proposed network significantly reduces the total AQT
with negligible qualitative artifacts and quantitative loss (reconstructs one
volume in approximately 1 second). The testing is done on publicly available
dataset with real MR images, and the proposed network shows (approximately 1dB)
increase in PSNR over SOTA.
