Exploratory research into reduction of scatter and beam hardening in industrial computed tomography using convolutional neural networks

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Abstract

Owing to recent advancements in the field of machine learning, such as deep convolutional neural networks (CNN), new applications in image processing have become feasible. The aim of this study was to explore the use of CNNs for the correction of X-ray scatter and beam hardening in industrial computed tomography. Through simulation, a large heterogeneous set of radiographs was produced, comprising monochromatic and polychromatic X-ray spectra with or without simulated scatter. This data was used to train three CNNs: a single network in which the overall effect of scatter and beam hardening is estimated, as well as a dual network in which both effects are estimated separately (i.e. scatter correction first, followed by beam hardening correction). Application of the trained CNNs on testing data showed superior performance for the dual network, at the cost of a increased training time.

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