Detail publikačního výsledku

Machine Learning Based Scatter Correction for Industrial Computed Tomography

SUPPES, A.; MAYER, T.; ROTHE, N.; MAŠINSKÝ, P.; ŘEHÁK, I.; PETŘÍK, M.; BLAŽEK, P.; ZIKMUND, T.; KAISER, J.

Originální název

Machine Learning Based Scatter Correction for Industrial Computed Tomography

Anglický název

Machine Learning Based Scatter Correction for Industrial Computed Tomography

Druh

Stať ve sborníku mimo WoS a Scopus

Originální abstrakt

Industrial X-ray computed tomography (CT) is a critical tool for non-destructive testing and quality control in various industries. However, scatter radiation remains a significant challenge, degrading image quality and limiting the evaluation accuracy of CT scans, especially for dense parts at higher X-ray energies, such as 450 kV. This study presents a comprehensive evaluation of traditional CT image improvement methods and introduces a novel machine learning-based approach for scatter correction to enhance image quality in industrial X-ray CT. To test the methods, we conduct an application study on a real-world part that compares the results from scans with a 300 kV micro-focus X-ray tube and a 450 kV meso-focus X-ray tube. The image quality, scanning speed, and total cost of ownership are compared.

Anglický abstrakt

Industrial X-ray computed tomography (CT) is a critical tool for non-destructive testing and quality control in various industries. However, scatter radiation remains a significant challenge, degrading image quality and limiting the evaluation accuracy of CT scans, especially for dense parts at higher X-ray energies, such as 450 kV. This study presents a comprehensive evaluation of traditional CT image improvement methods and introduces a novel machine learning-based approach for scatter correction to enhance image quality in industrial X-ray CT. To test the methods, we conduct an application study on a real-world part that compares the results from scans with a 300 kV micro-focus X-ray tube and a 450 kV meso-focus X-ray tube. The image quality, scanning speed, and total cost of ownership are compared.

Klíčová slova

Industrial computed tomography, X-ray, Scatter correction, Machine Learning, Digital Twin

Klíčová slova v angličtině

Industrial computed tomography, X-ray, Scatter correction, Machine Learning, Digital Twin

Autoři

SUPPES, A.; MAYER, T.; ROTHE, N.; MAŠINSKÝ, P.; ŘEHÁK, I.; PETŘÍK, M.; BLAŽEK, P.; ZIKMUND, T.; KAISER, J.

Rok RIV

2026

Vydáno

01.08.2025

Nakladatel

ndt.net

Místo

Paris, France

Strany od

1

Strany do

2

Strany počet

2

URL

https://www.ndt.net/search/docs.php3?id=31427

BibTex

@inproceedings{BUT201159,
  author="Alexander {Suppes} and  {} and  {} and Petr {Mašinský} and Ivo {Řehák} and Michal {Petřík} and Pavel {Blažek} and Tomáš {Zikmund} and Jozef {Kaiser}",
  title="Machine Learning Based Scatter Correction for Industrial Computed Tomography",
  year="2025",
  pages="2",
  publisher="ndt.net",
  address="Paris, France",
  url="https://www.ndt.net/search/docs.php3?id=31427"
}