Recently, deep learning (DL)-based methods have been proposed for the
computational reduction of gadolinium-based contrast agents (GBCAs) to mitigate
adverse side effects while preserving diagnostic value. Currently, the two main
challenges for these approaches are the accurate prediction of contrast
enhancement and the synthesis of realistic images. In this work, we address
both challenges by utilizing the contrast signal encoded in the subtraction
images of pre-contrast and post-contrast image pairs. To avoid the synthesis of
any noise or artifacts and solely focus on contrast signal extraction and
enhancement from low-dose subtraction images, we train our DL model using
noise-free standard-dose subtraction images as targets. As a result, our model
predicts the contrast enhancement signal only; thereby enabling synthesization
of images beyond the standard dose. Furthermore, we adapt the embedding idea of
recent diffusion-based models to condition our model on physical parameters
affecting the contrast enhancement behavior. We demonstrate the effectiveness
of our approach on synthetic and real datasets using various scanners, field
strengths, and contrast agents.

利用深度学习方法，本文针对对比增强和图像合成的挑战，通过利用预处理对比度图像对的差异图中的对比信号提取和增强，在低剂量减影图像中回避噪声和伪影用以专注于对比信号，并可实现标准剂量之外的图像合成，同时基于最近扩散模型的嵌入思想来根据影响对比增强行为的物理参数对模型进行条件化，并在多个扫描仪、场强和对比剂的合成和真实数据集上验证了我们的方法的有效性。

使用有条件深度学习降低脑部 MRI 的钆剂量

Gadolinium dose reduction for brain MRI using conditional deep learning

Today Gadolinium-based contrast agents (GBCA) are indispensable in Magnetic
Resonance Imaging (MRI) for diagnosing various diseases. However, GBCAs are
expensive and may accumulate in patients with potential side effects, thus
dose-reduction is recommended. Still, it is unclear to which extent the GBCA
dose can be reduced while preserving the diagnostic value -- especially in
pathological regions. To address this issue, we collected brain MRI scans at
numerous non-standard GBCA dosages and developed a conditional GAN model for
synthesizing corresponding images at fractional dose levels. Along with the
adversarial loss, we advocate a novel content loss function based on the
Wasserstein distance of locally paired patch statistics for the faithful
preservation of noise. Our numerical experiments show that conditional GANs are
suitable for generating images at different GBCA dose levels and can be used to
augment datasets for virtual contrast models. Moreover, our model can be
transferred to openly available datasets such as BraTS, where non-standard GBCA
dosage images do not exist.

通过使用条件 GAN 模型合成图像，研究探讨了非标准 GBCA 剂量下诊断性能的损失，WS 距离被用于提高图像细节保真度，可得出适用于不同剂量下的生成图像，为虚拟对比模型提供数据.