X-ray reconstruction using synthetic prior image restoration, with application to noise and artefact removal

M. Andrew; A. Andreyev; F. Yang; M. Xu; S. Xu

doi:10.1117/12.3027813

31 October 2024 X-ray reconstruction using synthetic prior image restoration, with application to noise and artefact removal

M. Andrew, A. Andreyev, F. Yang, M. Xu, S. Xu

Author Affiliations +

Proceedings Volume 13152, Developments in X-Ray Tomography XV; 131520E (2024) https://doi.org/10.1117/12.3027813
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

A novel method for image restoration is introduced that uses a synthetic prior intermediate (SPI) which is passed through a forward imaging operator, creating a data pair well-structured for inverse operator optimization, of which network training is of particular interest. This technique is applied to a critical problem in x-ray reconstruction: noise and artefact removal. We discuss the creation of the SPI through state-of-the-art Deep Learning Reconstruction (DLR), a spatially variant heuristic data-driven forward model for spectrally accurate noise and artefact modelling, and final image reconstruction via a convolutional neural network. Qualitative and quantitative performance is then benchmarked on a range of samples, comparing legacy reconstruction (FDK), state-of-the-art DLR, and SPI based reconstruction. SPI based reconstruction better recovers small features while also reducing residual sampling artefacts in large features. Quantitative analysis of SPI reconstruction showed a 40% throughput improvement relative to the state-of-the-art at a comparable image quality.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

M. Andrew, A. Andreyev, F. Yang, M. Xu, and S. Xu "X-ray reconstruction using synthetic prior image restoration, with application to noise and artefact removal", Proc. SPIE 13152, Developments in X-Ray Tomography XV, 131520E (31 October 2024); https://doi.org/10.1117/12.3027813

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