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.

翻译：近来，基于深度学习的方法被提出用于计算性地减少钆基对比剂用量，以在保持诊断价值的同时减轻其不良副作用。目前，这类方法面临两大主要挑战：对比增强的精确预测以及逼真图像的合成。本研究通过利用增强前后图像对剪影图中编码的对比信号，同时解决了上述两个挑战。为避免合成任何噪声或伪影，并专注于从低剂量剪影图中提取和增强对比信号，我们以无噪声标准剂量剪影图作为训练目标来训练深度学习模型。由此，模型仅预测对比增强信号，从而能够合成超越标准剂量的图像。此外，我们借鉴近期基于扩散模型的嵌入思想，使模型能够根据影响对比增强行为的物理参数进行条件化处理。通过采用不同扫描仪、场强及对比剂对合成数据集和真实数据集进行实验，我们验证了该方法的有效性。