Tobias Wissel
Papers
8
Total Citations
98
H-Index
5
About
Tobias Wissel’s research sits at the intersection of medical physics, robotics, and machine learning, with a sharp focus on solving one of radiotherapy’s toughest challenges: compensating for respiratory motion during tumor treatment. His major contributions center on developing predictive models and correlation techniques that allow robotic radiosurgery systems—like the CyberKnife—to track moving tumors in real time. Wissel pioneered the use of relevance vector machines and multi-task Gaussian processes to simultaneously predict external surrogate motion and correlate it with internal tumor position, significantly improving accuracy over traditional methods. His work on time-multiplexed structured light for optical head tracking in intracranial radiosurgery demonstrates his versatility, achieving high-precision triangulation for soft-tissue features. With his most-cited paper garnering 27 citations, Wissel’s research has directly informed the design of safer, more effective motion compensation strategies. His 2013 study on multi-modal sensor evaluation remains a foundational reference for researchers tackling time-delay compensation in robotic radiotherapy. By unifying prediction and correlation into a single framework, Wissel has helped push the field toward more robust, real-time adaptive treatments—a critical step for delivering precise radiation to moving tumors without compromising healthy tissue.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Respiratory Motion Compensation with Relevance Vector Machines20 citations · 2013
- 4Multivariate respiratory motion prediction16 citations · 2014
- 5
- 6
- 7
- 8