  MRI entails a great amount of cost, time and effort for the generation of all
the modalities that are recommended for efficient diagnosis and treatment
planning. Recent advancements in deep learning research show that generative
models have achieved substantial improvement in the aspects of style transfer
and image synthesis. In this work, we formulate generating the missing MR
modality from existing MR modalities as an imputation problem using style
transfer. With a multiple-to-one mapping, we model a network that accommodates
domain specific styles in generating the target image. We analyse the style
diversity both within and across MR modalities. Our model is tested on the
BraTS'18 dataset and the results obtained are observed to be on par with the
state-of-the-art in terms of visual metrics, SSIM and PSNR. After being
evaluated by two expert radiologists, we show that our model is efficient,
extendable, and suitable for clinical applications.
