Deep residual learning in CT physics: scatter correction for spectral CT

TL;DR

This paper introduces a deep residual learning approach for scatter correction in spectral CT, significantly reducing computational costs while maintaining high accuracy in scatter estimation for improved material quantification.

Contribution

The study develops a deep convolutional neural network that effectively models object-dependent scatter in spectral CT, overcoming limitations of empirical methods and Monte Carlo simulations.

Findings

Accurate scatter estimation with lower computational costs.

Effective in digital and real phantom tests.

Improves spectral CT quantitative accuracy.

Abstract

Recently, spectral CT has been drawing a lot of attention in a variety of clinical applications primarily due to its capability of providing quantitative information about material properties. The quantitative integrity of the reconstructed data depends on the accuracy of the data corrections applied to the measurements. Scatter correction is a particularly sensitive correction in spectral CT as it depends on system effects as well as the object being imaged and any residual scatter is amplified during the non-linear material decomposition. An accurate way of removing scatter is subtracting the scatter estimated by Monte Carlo simulation. However, to get sufficiently good scatter estimates, extremely large numbers of photons is required, which may lead to unexpectedly high computational costs. Other approaches model scatter as a convolution operation using kernels derived using empirical methods. These techniques have been found to be insufficient in spectral CT due to their inability to sufficiently capture object dependence. In this work, we develop a deep residual learning framework to address both issues of computation simplicity and object dependency. A deep convolution neural network is trained to determine the scatter distribution from the projection content in training sets. In test cases of a digital anthropomorphic phantom and real water phantom, we demonstrate that with much lower computing costs, the proposed network provides sufficiently accurate scatter estimation.