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13 March 2024 Unsupervised data clustering for material separation in spectral x-ray radiography with energy-resolving photon counting detectors
Alexandra Jalali, Cale Lewis
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Proceedings Volume PC12903, AI and Optical Data Sciences V; PC129030G (2024) https://doi.org/10.1117/12.3006957
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
Energy-resolving photon counting detectors (PCDs) are being explored for non-destructive spectral x-ray imaging in medical and industrial applications, allowing quantitative material mapping not practical with conventional radiography. However, PCDs suffer inherent detector non-idealities that negatively impact image quality and quantitative accuracy. While analytical methods are being developed for material separation, we leverage machine learning techniques (e.g., principal component analysis and clustering) to increase flexibility by reducing the reliance on prior knowledge of the inspected object or detection properties. Through simulating various acquisition conditions, we evaluate the robustness of these machine learning techniques for material-specific mapping in spectral x-ray imaging.
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Alexandra Jalali and Cale Lewis "Unsupervised data clustering for material separation in spectral x-ray radiography with energy-resolving photon counting detectors", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC129030G (13 March 2024); https://doi.org/10.1117/12.3006957
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KEYWORDS
Radiography
X-rays
Photon counting
X-ray detectors
X-ray imaging
Image quality
Image segmentation
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