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。

运用时空方法改进动态增强 MRI 的风格转移

Improving style transfer in dynamic contrast enhanced MRI using a  spatio-temporal approach

Image segmentation and registration are said to be challenging when applied
to dynamic contrast enhanced MRI sequences (DCE-MRI). The contrast agent causes
rapid changes in intensity in the region of interest and elsewhere, which can
lead to false positive predictions for segmentation tasks and confound the
image registration similarity metric. While it is widely assumed that contrast
changes increase the difficulty of these tasks, to our knowledge no work has
quantified these effects. In this paper we examine the effect of training with
different ratios of contrast enhanced (CE) data on two popular tasks:
segmentation with nnU-Net and Mask R-CNN and registration using VoxelMorph and
VTN. We experimented further by strategically using the available datasets
through pretraining and fine tuning with different splits of data. We found
that to create a generalisable model, pretraining with CE data and fine tuning
with non-CE data gave the best result. This interesting find could be expanded
to other deep learning based image processing tasks with DCE-MRI and provide
significant improvements to the models performance.

本文旨在探讨采用不同对比度增强数据比例对 MRI 动态增强序列的图像分割和配准任务所产生的影响，发现通过预训练对比度增强数据和微调非对比度增强数据可得到更具有通用性的模型。

单一图像处理算法能否在动态增强磁共振成像的所有相位中同样有效？

Can a single image processing algorithm work equally well across all  phases of DCE-MRI?

DCE-MRI provides information about vascular permeability and tissue perfusion
through the acquisition of pharmacokinetic parameters. However, traditional
methods for estimating these pharmacokinetic parameters involve fitting tracer
kinetic models, which often suffer from computational complexity and low
accuracy due to noisy arterial input function (AIF) measurements. Although some
deep learning approaches have been proposed to tackle these challenges, most
existing methods rely on supervised learning that requires paired input DCE-MRI
and labeled pharmacokinetic parameter maps. This dependency on labeled data
introduces significant time and resource constraints, as well as potential
noise in the labels, making supervised learning methods often impractical. To
address these limitations, here we present a novel unpaired deep learning
method for estimating both pharmacokinetic parameters and the AIF using a
physics-driven CycleGAN approach. Our proposed CycleGAN framework is designed
based on the underlying physics model, resulting in a simpler architecture with
a single generator and discriminator pair. Crucially, our experimental results
indicate that our method, which does not necessitate separate AIF measurements,
produces more reliable pharmacokinetic parameters than other techniques.

本文提出了一种基于物理驱动 CycleGAN 方法的非配对深度学习方法，用于在不需要单独进行动脉输入函数（AIF）测量的情况下，估计药代动力学参数和 AIF，实验结果表明该方法可以生成比其他技术更可靠的药代动力学参数。