[论文解读] End-to-End Differentiable Photon Counting CT

Sen Wang, Yirong Yang+3 more|arXiv (Cornell University)|Feb 12, 2026

Advanced X-ray and CT Imaging被引用 0

一句话总结

简要结论：通过将材料分解通过隐函数定理实现可微，建立一个端到端可微的光子计数CT框架，从而实现跨领域学习和对PCCT成像任务的自动优化。

ABSTRACT

Quantitative imaging is an important feature of spectral X-ray and CT systems, especially photon-counting CT (PCCT) imaging systems, which is achieved through material decomposition (MD) using spectral measurements. In this work, we present a novel framework that makes the PCCT imaging chain end-to-end differentiable (differentiable PCCT), with which we can leverage quantitative information in the image domain to enable cross-domain learning and optimization for upstream models. Specifically, the material decomposition from maximum-likelihood estimation (MLE) was made differentiable based on the Implicit Function Theorem and inserted as a layer into the imaging chain for end-to-end optimization. This framework allows for an automatic and adaptive solution of a wide range of imaging tasks, ultimately achieving quantitative imaging through computation rather than manual intervention. The end-to-end training mechanism effectively avoids the need for direct-domain training or supervision from intermediate references as models are trained using quantitative images. We demonstrate its applicability in two representative tasks: correcting detector energy bin drift and training an object scatter correction network using cross-domain reference from quantitative material images.

研究动机与目标

• 使PCCT成像链实现端到端可微，以便实现定量、跨域学习。
• 在成像管线中加入通过隐函数定理得到可微的材料分解步骤。
• 展示无需直接域监督的成像任务的自动、自适应解决方案。

提出的方法

• 用通过隐函数定理推导的可微分层替代最大似然材料分解。
• 将可微分分解嵌入CT成像链以实现端到端优化。
• 在不需要中间参考监督的情况下使用定量成像数据训练模型。
• 将该框架应用于诸如探测器能量 bin 漂移校正等任务。
• 将该框架应用于使用跨域定量材料图像训练对象散射校正网络。

实验结果

研究问题

• RQ1 端到端可微的PCCT是否能提高定量成像任务的质量或鲁棒性？
• RQ2 可微材料分解是否能为下游任务实现有效的跨域学习？
• RQ3 该框架是否能在没有中间监督的情况下自适应解决探测器漂移校正和散射校正等成像任务？

主要发现

• 通过可微材料分解层展示了PCCT成像链的端到端可微性。
• 显示可用于校正探测器能量 bin 漏动漂移的适用性。
• 演示使用跨域定量材料图像训练对象散射校正网络。