A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography

TL;DR

This paper introduces a deep learning-based iterative reconstruction framework for industrial cone-beam X-ray CT that delivers fast, high-quality 3D images of dense metal parts, handling noise and artifacts better than existing methods.

Contribution

The authors develop a novel deep neural network iterative algorithm with automated regularization, improving reconstruction quality and speed for large-scale industrial XCT data, especially for challenging dense metal objects.

Findings

Achieves high-quality 3D reconstructions in few iterations.

Effectively handles noise and streak artifacts.

Outperforms state-of-the-art supervised learning methods.

Abstract

Cone-beam X-ray Computed Tomography (XCT) with large detectors and corresponding large-scale 3D reconstruction plays a pivotal role in micron-scale characterization of materials and parts across various industries. In this work, we present a novel deep neural network-based iterative algorithm that integrates an artifact reduction-trained CNN as a prior model with automated regularization parameter selection, tailored for large-scale industrial cone-beam XCT data. Our method achieves high-quality 3D reconstructions even for extremely dense thick metal parts - which traditionally pose challenges to industrial CT images - in just a few iterations. Furthermore, we show the generalizability of our approach to out-of-distribution scans obtained under diverse scanning conditions. Our method effectively handles significant noise and streak artifacts, surpassing state-of-the-art supervised learning methods trained on the same data.