X-ray CT Data Completeness Condition for Sets of Arbitrary Projections
Herl G, Maier A, Zabler S (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: SPIE
Book Volume: 12304
Conference Proceedings Title: Proceedings of SPIE - The International Society for Optical Engineering
Event location: Virtual, Online
ISBN: 9781510656697
DOI: 10.1117/12.2646435
Abstract
X-ray tomography reconstruction requires a set of projections that provides sufficient information for the examined region. Commonly, to ensure mathematically complete reconstruction, first, a continuous curve (trajectory) that fulfils the Tuy conditions is chosen. Second, this curve is sampled based on the Nyquist-Shannon sampling theorem. This two-step approach is efficient for most standard X-ray tomography scanning scenarios. For agile X-ray tomography systems, e.g. robot-supported computed tomography systems, choosing a set of projections based on a continuous curve is often not useful. Instead, sets of projections from arbitrary views might be necessary. This work combines the Tuy-Smith condition with conclusions from the Nyquist-Shannon sampling theorem. In particular, the maximal pixel size and a requirement for the arrangement of projections are formulated depending on the smallest relevant object feature and integrated into the Tuy-Smith condition. We derive a comprehensive condition for data completeness that can assess the completeness of any set of arbitrary projections, e.g. for complex scanning scenarios with robot-supported X-ray tomography systems.
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Germany (DE)How to cite
APA:
Herl, G., Maier, A., & Zabler, S. (2022). X-ray CT Data Completeness Condition for Sets of Arbitrary Projections. In Joseph Webster Stayman (Eds.), Proceedings of SPIE - The International Society for Optical Engineering. Virtual, Online: SPIE.
MLA:
Herl, Gabriel, Andreas Maier, and Simon Zabler. "X-ray CT Data Completeness Condition for Sets of Arbitrary Projections." Proceedings of the 7th International Conference on Image Formation in X-Ray Computed Tomography, Virtual, Online Ed. Joseph Webster Stayman, SPIE, 2022.
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