Papers
11
Total Citations
145
H-Index
5
About
Gabriel Herl is an emerging researcher specializing in robotic X-ray computed tomography (CT), with a particular focus on trajectory optimization, system calibration, and image quality enhancement for both industrial and medical imaging applications. His work sits at the intersection of robotics, imaging physics, and computational optimization, addressing a fundamental challenge in modern CT: how to move beyond the limitations of conventional circular scanning trajectories. Herl's most influential contribution, a comprehensive review of source-detector trajectory optimization in cone-beam CT (2022, 46 citations), has become a key reference for researchers navigating this rapidly evolving field. His earlier work on quantitative Tuy-based local quality estimation (2020, 35 citations) introduced practical tools for automatically computing optimized trajectories in robotic CT systems, while his research on twin-robotic CT (2021, 28 citations) demonstrated how dual-arm robotic systems could unlock entirely new scanning possibilities across medicine and industry. More recently, Herl has tackled critical engineering challenges surrounding accuracy and calibration in high-degree-of-freedom CT systems, ensuring that geometric flexibility does not compromise measurement precision. His ongoing contributions to the "RoboCT" research agenda underscore his role as a driving voice in defining the current state and future direction of industrial robotic CT — a field with profound implications for non-destructive testing and advanced diagnostics.
Research Focus
Key Achievements
Top Papers
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- 3Task-Specific Trajectory Optimisation for Twin-Robotic X-Ray Tomography28 citations · 2021
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