Limited angle x-ray tomography with machine learning

Chonghang Zhao; Mingyuan Ge; Xiaogang Yang; Yong S. Chu; Hanfei Yan

doi:10.1117/12.2672543

5 October 2023 Limited angle x-ray tomography with machine learning

Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan

Proceedings Volume 12698, X-Ray Nanoimaging: Instruments and Methods VI; 1269802 (2023) https://doi.org/10.1117/12.2672543
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

X-ray tomography is a nondestructive technique that visualizes interior features in solid objects. To achieve a given resolution, a sufficient number of projection images over a cycle is required for 3D reconstruction based on the Crowther criterion. However, practical limitations such as geometrical constraints, data acquisition time, and low dose requirements often prohibit the acquisition of the full dataset, only allowing a limited angular range. The unsampled angles lead to the 'missing edge' problem in tomography, which introduces strong artifacts in reconstruction. To tackle this challenge, we propose an approach that integrates a Convolutional Neural Network (CNN) as a regularizer into an iterative solving engine. It combines perceptual prior knowledge about the sample with the physical model to produce an artifact-free solution. Our approach demonstrates excellent results with an experimental dataset with a missing edge of over 90 degrees.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, and Hanfei Yan "Limited angle x-ray tomography with machine learning", Proc. SPIE 12698, X-Ray Nanoimaging: Instruments and Methods VI, 1269802 (5 October 2023); https://doi.org/10.1117/12.2672543

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