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.

在 DCE-MRI 中进行风格转换是一项具有挑战性的任务，由于不同组织和时间中对比度增强的大幅变化。本文提出了一种新方法，该方法结合了自编码器来解开内容和风格，并使用卷积 LSTMs 对时间中的预测潜空间进行建模，并使用自适应卷积来应对对比度增强的局部性质。通过提出一个考虑对比度增强的新度量方法来评估我们的方法，定性和定量分析表明所提出的方法在两个不同数据集上优于现有技术水平。