Simon Bellens from the Dept. of Mechanical Engineering, KU Leuven, together with colleagues from the Dept. of Electrical Engineering, KU Leuven, and Materialise NV, has published a new article on the deep-learning reconstruction of few-view X-ray CT measurements of mono-material objects with a validation in additive manufacturing. The article is published in the May issue of CIRP Annals.
The large acquisition times needed for high-quality XCT measurements remain a stumbling block for high-throughput inspection tasks. This paper therefore presents a deep learning reconstruction algorithm to improve the quality of fast, few-view XCT measurements. The proposed method is validated on both simulated and experimental XCT measurements of additively manufactured cranio-maxillofacial implants. The validation demonstrates a drastic reduction in noise and streaking artifacts associated with few-view acquisitions. Therefore, the potential to maintain high reconstruction quality while reducing acquisition times by more than one order of magnitude is confirmed.
Reference
Simon Bellens, Patricio Guerrero, Michel Janssens, Patrick Vandewalle, Wim Dewulf, Deep learning reconstruction of few-view X-ray CT measurements of mono-material objects with validation in additive manufacturing, CIRP Annals, 2024, ISSN 0007-8506, https://doi.org/10.1016/j.cirp.2024.04.079.
Acknowledgements
This work was funded by Flanders Innovation & Entrepreneurship (VLAIO) as a Baekeland mandate grant (HBC.2020.2280) awarded to Simon Bellens in collaboration with Materialise.
