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.
While reinforcement-learning (RL) based needle placement optimization has been explored in prostate brachytherapy, its application to gynecologic high dose rate (HDR) brachytherapy remains limited. This work presents a reinforcement learning based framework f...
To investigate the suitability of three different materials as background media for use with in-house phantoms for HDR interstitial brachytherapy. In-house phantoms are designed to supplement commercial phantom capabilities and enable interstitial needle plac...
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.
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...