Linda-Sophie Schneider

Friedrich-Alexander-Universität Erlangen-Nürnberg

Papers

Total Citations

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About

Linda-Sophie Schneider is an emerging researcher at the intersection of medical imaging, robotics, and machine learning, with a focused specialization in computed tomography (CT) reconstruction and trajectory optimization. Her work addresses one of the most technically demanding challenges in modern radiology: designing optimal imaging pathways for flexible, non-standard CT systems that can reduce radiation exposure while maintaining diagnostic quality. Her most notable contribution, "Learning-based Trajectory Optimization for a Twin Robotic CT System" (2023, 10 citations), demonstrates how machine learning can be leveraged to optimize projection trajectories in highly flexible twin robotic CT configurations — a significant step toward more efficient and adaptive clinical imaging. Building on this foundation, her 2025 work introducing DRACO (6 citations) presents a differentiable reconstruction framework capable of handling arbitrary cone beam CT orbits, directly tackling the computational bottlenecks that have historically limited iterative reconstruction methods. Though early in her career, Schneider's research signals meaningful progress in making advanced robotic CT systems practically viable. Her dual focus on acquisition optimization and reconstruction methodology positions her as a promising contributor to next-generation medical imaging technology.