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.

翻译：动态对比增强MRI（DCE-MRI）中的风格迁移是一项具有挑战性的任务，原因在于不同组织及不同时间点的对比增强存在显著差异。现有无监督方法因序列图像间对比增强的广泛变化以及运动伪影而失效。我们提出了一种新方法，该方法结合自编码器以解耦内容与风格，利用卷积长短期记忆网络（convlutional LSTM）对沿时间维度的潜在空间进行建模，并通过自适应卷积处理对比增强的局部化特性。为评估所提方法，我们提出了一种纳入对比增强因素的新指标。定性与定量分析表明，所提方法在两个不同数据集上均超越了现有最优技术。