Minsik Lee
Papers
1
Total Citations
10
H-Index
1
About
Minsik Lee is a researcher at the forefront of medical physics and radiation oncology, specializing in advanced motion management and treatment verification techniques. Their work addresses the critical challenge of respiratory-induced tumor motion during radiotherapy, a key obstacle in delivering precise, high-dose treatments. Lee’s most-cited paper, “Geometric and dosimetric verification of a recurrent neural network algorithm to compensate for respiratory motion using an articulated robotic couch” (2020, 10 citations), demonstrates a novel integration of machine learning and robotics. By employing a recurrent neural network to predict and correct for breathing patterns in real time, Lee’s algorithm enables an articulated robotic couch to dynamically reposition patients, ensuring accurate dose delivery to moving targets. This contribution not only enhances the geometric and dosimetric accuracy of stereotactic body radiotherapy but also reduces the need for invasive motion management techniques. With a growing citation impact, Lee’s work is a testament to the power of interdisciplinary innovation, bridging artificial intelligence, robotics, and clinical physics to improve cancer treatment outcomes. Their research continues to inspire new approaches in adaptive radiotherapy, making them a rising voice in the field.
Research Focus
Key Achievements
Top Papers
- 1