The adoption of contrast agents in medical imaging protocols is crucial for accurate and timely diagnosis. While highly effective and characterized by an excellent safety profile, the use of contrast agents has its limitation, including rare risk of allergic reactions, potential environmental impact and economic burdens on patients and healthcare systems. In this work, we address the contrast agent reduction (CAR) problem, which involves reducing the administered dosage of contrast agent while preserving the visual enhancement. The current literature on the CAR task is based on deep learning techniques within a fully image processing framework. These techniques digitally simulate high-dose images from images acquired with a low dose of contrast agent. We investigate the feasibility of a ``learned inverse problem'' (LIP) approach, as opposed to the end-to-end paradigm in the state-of-the-art literature. Specifically, we learn the image-to-image operator that maps high-dose images to their corresponding low-dose counterparts, and we frame the CAR task as an inverse problem. We then solve this problem through a regularized optimization reformulation. Regularization methods are well-established mathematical techniques that offer robustness and explainability. Our approach combines these rigorous techniques with cutting-edge deep learning tools. Numerical experiments performed on pre-clinical medical images confirm the effectiveness of this strategy, showing improved stability and accuracy in the simulated high-dose images.

翻译：在医学成像方案中采用造影剂对于准确及时的诊断至关重要。尽管造影剂效果显著且安全性优异，但其使用仍存在局限性，包括罕见的过敏反应风险、潜在的环境影响以及对患者和医疗系统的经济负担。本研究针对造影剂减量问题展开，旨在降低造影剂使用剂量的同时保持视觉增强效果。当前关于CAR任务的文献主要基于全图像处理框架下的深度学习技术，这些技术通过数字模拟从低剂量造影剂图像生成高剂量图像。我们探索了一种与现有文献中端到端范式不同的“学习型逆问题”方法的可行性。具体而言，我们学习了将高剂量图像映射至对应低剂量图像的图像间算子，并将CAR任务构建为逆问题，进而通过正则化优化重构求解。正则化方法作为成熟的数学技术，具有鲁棒性和可解释性优势。我们的方法将这些严谨的数学工具与前沿深度学习技术相结合。在临床前医学图像上进行的数值实验证实了该策略的有效性，显示出模拟高剂量图像在稳定性和准确性方面的提升。