Author profile
Duke University Medical Center
Effective radiotherapy for upper abdominal tumors requires dose escalation but is limited by respiratory motion and the low gastrointestinal radiation tolerance. Current clinical motion management on conventional Linacs relies on simple external surrogates th...
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...
To advance the clinical application of a fully automatic planning system for functional lung avoidance radiotherapy (AP-FLART) through clinical implementation and comprehensive evaluation.
Radiotherapy for upper abdominal cancers is limited by respiratory motion and the low radiation tolerance, restricting safe dose escalation. Conventional linear accelerators rely on kV X-ray and CBCT imaging but lack real-time internal motion tracking capabil...
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...