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.

翻译：动态对比增强磁共振成像（DCE-MRI）通过获取药代动力学参数提供血管通透性和组织灌注信息。然而，传统药代动力学参数估计方法依赖示踪动力学模型拟合，常因动脉输入函数（AIF）测量噪声而面临计算复杂度高、精度低的问题。尽管已有深度学习方法被提出以应对这些挑战，但现有方法大多依赖监督学习，需要成对的输入DCE-MRI图像与标注的药代动力学参数图。这种对标注数据的依赖性带来了显著的时间和资源约束，同时标注中可能存在的噪声使监督学习方法常常难以实用。为突破这些局限，本文提出一种基于物理驱动的CycleGAN方法的非配对深度学习方法，用于同时估计药代动力学参数和AIF。我们提出的CycleGAN框架基于底层物理模型设计，通过单一生成器-判别器对实现了更简洁的网络架构。重要的是，实验结果表明，本方法无需独立测量AIF，能够比其他技术生成更可靠的药代动力学参数。