核心概念
Applying online model checking to dynamically select feasible beams and reduce treatment time in robotic radiation therapy with ultrasound guidance.
摘要
The content discusses an approach for optimizing beam scheduling in robotic radiation therapy using online model checking.
Key highlights:
Robotic radiation therapy with ultrasound guidance allows for real-time motion compensation, but beams must avoid colliding with the ultrasound transducer and robot.
Traditionally, beams are delivered in a fixed order to minimize robot motion, but this can lead to pauses when beams become infeasible due to patient motion.
The authors propose using online model checking to dynamically select feasible beams, avoiding pauses and reducing overall treatment time.
They model the patient's 3D respiratory motion as a network of 1D models, allowing for fast verification of beam feasibility.
Experiments show a 16.02% to 37.21% reduction in idle time compared to a static beam schedule, depending on the safety margin used.
The authors also discuss attempts to use machine learning to further improve beam selection, but found the results did not outperform the online model checking approach.
統計資料
Pr[<= {scope}] ([] result <= {upper} && result >= {lower})
Pr[<= {to-tm+t+to-t-≤tp≤to+t+∧x0-x-≤xp≤xo+x+}]
引述
"While human breathing patterns are complex and may change rapidly, we need a model which can be verified quickly and use approximation by a superposition of sine curves."
"Our preliminary results show a 16.02 % to 37.21 % mean improvement on the idle time compared to a static beam schedule, depending on an additional safety margin."