Author profile
Duke University Medical Center
To quantitatively benchmark dosimetric variation associated with tumor regression during head and neck (HN) radiotherapy and to evaluate the benefit of adaptive replanning as a basis for adapt-on-demand decision support.
Reinforcement learning (RL) constitutes a strong candidate for AI-guided treatment planning for two distinguishing reasons: it differs from greedy algorithms by optimizing strategy over the complete history of a Markovian process; and it contrasts with superv...
The novel treatment delivery technique RapidArc Dynamic (RAD), which combines dynamic gantry motion with static angle–modulated ports (STAMPs), offers strong potential for advanced planning and improved plan quality. However, a major challenge in clinical imp...
VMAT optimization is a non-convex problem with tightly coupled parameters and machine constraints, which limits the development of transparent and extensible frameworks outside commercial treatment planning systems. This work introduces a new perspective on V...
RapidArc Dynamic (RAD) is a novel treatment delivery technique that combines rotational arc therapy with static angle modulated ports (STAMPs). This study evaluates the feasibility, dosimetric performance, and delivery efficiency of RAD for single-isocenter m...