End-to-End Differentiable Photon Counting CT

TL;DR

This paper introduces a differentiable framework for photon-counting CT that enables end-to-end optimization of the entire imaging process, improving quantitative imaging and task-specific corrections without manual intervention.

Contribution

The work presents a novel end-to-end differentiable photon-counting CT framework that integrates material decomposition as a differentiable layer for improved optimization.

Findings

Successfully integrated differentiable material decomposition into the imaging chain.

Enabled end-to-end training for detector drift correction.

Demonstrated improved performance in scatter correction tasks.

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.