MIST GAN: Modality Imputation Using Style Transfer for MRI
This addresses the high cost and time of acquiring all MRI modalities for diagnosis, though it appears incremental as it builds on existing style transfer and generative model techniques.
The paper tackled the problem of generating missing MRI modalities from existing ones by formulating it as an imputation problem using style transfer, achieving results on par with state-of-the-art methods in terms of SSIM and PSNR metrics on the BraTS'18 dataset.
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.