Abstract
Beam-hardening (BH) artifacts are ubiquitous in X-ray CT scans of dense metal additively manufactured (AM) parts.
While linearization approaches are useful for correcting beam-hardened data from single material objects, they either require a calibration scan or detailed system and material composition information.
In this paper, we introduce a neural network-based, material-agnostic method to correct beam-hardening artifacts.
We train a neural network to map the acquired beam-hardened projection values and the corresponding estimated thickness of the part based on an initial segmentation to beam-hardening related parameters, which can be used to compute the coefficients of a linearizing correction polynomial.
A key strength of our approach is that, once the network is trained, it can be used for correcting beam hardening from a variety of materials without any calibration scans or detailed system and material composition information.
Furthermore, our method is robust to errors in the estimated thickness due to the typical challenge of obtaining an accurate initial segmentation from reconstructions impacted by BH artifacts.
We demonstrate the utility of our method to obtain high-quality CT reconstructions from a collection of AM parts -- suppressing cupping and streaking artifacts
