Deep learning based workflow for accelerated industrial X-ray Computed Tomography

TL;DR

This paper presents a novel deep learning workflow that accelerates industrial X-ray CT scans by reducing the number of projections needed, while effectively correcting artifacts and improving reconstruction quality.

Contribution

It introduces a two-network approach that enhances sparse-view XCT reconstructions without calibration or retraining, enabling faster and more reliable industrial inspections.

Findings

Robust generalization across multiple alloys.

Effective artifact correction in sparse-view reconstructions.

Significant reduction in measurement time.

Abstract

X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analytic reconstruction algorithms require a large number of projections for accurate characterization - leading to longer measurement times and hindering the adoption of XCT for in-line inspections. In this paper, we introduce a new workflow based on the use of two neural networks to obtain high-quality accelerated reconstructions from sparse-view XCT scans of single material metal parts. The first network, implemented using fully-connected layers, helps reduce the impact of BH in the projection data without the need of any calibration or knowledge of the component material. The second network, a convolutional neural network, maps a low-quality analytic 3D reconstruction to a high-quality reconstruction. Using experimental data, we demonstrate that our method robustly generalizes across several alloys, and for a range of sparsity levels without any need for retraining the networks thereby enabling accurate and fast industrial XCT inspections.