Simon Zabler
Papers
5
Total Citations
24
H-Index
4
About
Simon Zabler is a researcher specializing in advanced X-ray computed tomography (CT) systems, with a particular focus on the emerging field of twin robotic CT — a cutting-edge approach that replaces conventional, mechanically constrained CT systems with two industrial robotic arms independently controlling the X-ray source and detector. His work addresses fundamental challenges in flexible CT acquisition, including geometric calibration, trajectory optimization, and reconstruction accuracy for arbitrarily positioned robotic configurations. Among his most significant contributions is the development and rigorous evaluation of twin robotic CT systems, exploring how robots can unlock new scanning geometries previously impossible with traditional equipment. His research on AI-driven geometric calibration — applying attention-based machine learning to map markers in X-ray projections — represents an innovative bridge between deep learning and metrological precision in industrial imaging. He has also advanced efficient calibration methods that ensure measurement accuracy across arbitrary scanning trajectories, a critical requirement for industrial quality assurance. With publications accumulating citations in the field since 2023, Zabler's work is gaining recognition as robotic CT transitions from concept to industrial reality. His ongoing contributions, including the comprehensive state-of-the-field survey "RoboCT," position him as a key voice shaping the future of flexible, intelligent X-ray imaging systems.
Research Focus
Key Achievements
Top Papers
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