Andreas Maier
Papers
12
Total Citations
192
H-Index
8
About
Andreas Maier is a prominent researcher specializing in medical imaging, computed tomography (CT) trajectory optimization, and computer vision, with a particular focus on advancing cone-beam computed tomography (CBCT) and robotic X-ray systems. His work sits at the intersection of imaging physics, machine learning, and clinical application, making meaningful contributions to both industrial and medical imaging communities. Maier is perhaps best known for his extensive contributions to scanning trajectory optimization for robotic and C-arm CT systems. His comprehensive 2022 review on source-detector trajectory optimization in CBCT (46 citations) has become a key reference in the field, while his earlier work introducing quantitative Tuy-based local quality estimation (35 citations) provided a practical framework for automatically computing optimal scanning trajectories. His 2021 study on task-specific trajectory optimization for twin-robotic systems (28 citations) further demonstrated how non-circular trajectories can dramatically expand CT capabilities. Beyond trajectory optimization, Maier has explored flat detector CT perfusion imaging for stroke intervention, multi-modal sensor calibration, self-supervised visual localization for robotics, and differentiable reconstruction methods for arbitrary CBCT orbits. His recent learning-based approaches signal a forward-looking integration of deep learning into CT system design. With a growing citation record spanning clinical, industrial, and algorithmic domains, Maier represents a versatile and impactful voice in modern medical imaging research.
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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- 6Learning-based Trajectory Optimization for a Twin Robotic CT System10 citations · 2023
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- 10DRACO: differentiable reconstruction for arbitrary CBCT orbits6 citations · 2025