Improving style transfer in dynamic contrast enhanced MRI using a
  spatio-temporal approach
Adam G. Tattersall Keith A. Goatman Lucy E. Kershaw Scott I. K. Semple Sonia Dahdouh
Style transfer in DCE-MRI is a challenging task due to large variations in
contrast enhancements across different tissues and time. Current unsupervised
methods fail due to the wide variety of contrast enhancement and motion between
the images in the series. We propose a new method that combines autoencoders to
disentangle content and style with convolutional LSTMs to model predicted
latent spaces along time and adaptive convolutions to tackle the localised
nature of contrast enhancement. To evaluate our method, we propose a new metric
that takes into account the contrast enhancement. Qualitative and quantitative
analyses show that the proposed method outperforms the state of the art on two
different datasets.