Richard Schielein

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About

Richard Schielein is a leading researcher in the field of robotic computed tomography (CT), specializing in the intersection of trajectory optimization and non-circular twin robot CT systems. His work addresses a critical challenge in advanced imaging: how to optimally position X-ray sources and detectors in flexible, robot-based CT setups to maximize reconstruction quality while minimizing the number of projections. Schielein’s major contributions include developing learning-based and geometry-driven algorithms that autonomously design optimal CT trajectories, significantly reducing image artifacts compared to conventional scanning paths. His 2023 paper on "Learning-based Trajectory Optimization for a Twin Robotic CT System" (10 citations) demonstrates how machine learning can enhance flexibility and efficiency in industrial and medical CT. His 2022 work on "Trajectory Optimization in Computed Tomography Based on Object Geometry" (7 citations) provides a foundational framework for artifact reduction through object-aware path planning. These contributions are pivotal for advancing RoboCT applications, where traditional circular trajectories are impractical. Schielein’s research is essential reading for engineers and scientists working on next-generation CT systems, offering practical solutions for high-quality, low-dose imaging in complex geometries.