Yingpeng Song
Papers
1
Total Citations
5
H-Index
1
About
Yingpeng Song is a rising researcher at the intersection of biomedical engineering and radiation oncology, with a primary focus on respiratory motion management for thoracoabdominal radiotherapy and robotic surgery. His most notable contribution is the development of neural signals-based respiratory motion tracking, a proof-of-concept approach that addresses the critical limitation of system latency in conventional imaging-based techniques. This work, published in 2023, has already garnered 5 citations, signaling its early impact in a field where real-time accuracy is paramount for treatment precision. By exploring direct neural interfaces as an alternative to lag-prone imaging, Song’s research opens new pathways for reducing motion-induced errors in cancer therapy and surgical guidance. His work is particularly relevant for students and researchers interested in the convergence of neurotechnology, medical physics, and clinical robotics. As an emerging voice in this specialized domain, Song’s contributions promise to advance the safety and efficacy of motion-compensated procedures, making him a researcher to watch in the evolving landscape of precision medicine.
Research Focus
Key Achievements
Top Papers
- 1