A unified deep-learning framework for enhanced patient-specific quality assurance of intensity-modulated radiation therapy plans.

ABSTRACT:

Background

Modern radiation therapy techniques, such as intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), use complex fluence modulation strategies to achieve optimal patient dose distribution. Ensuring their accuracy necessitates rigorous patient-specific quality assurance (PSQA), traditionally done through pretreatment measurements with detector arrays. While effective, these methods are labor-intensive and time-consuming. Independent calculation-based methods leveraging advanced dose algorithms provide a reduced workload but cannot account for machine performance during delivery.

Purpose

This study introduces a novel unified deep-learning (DL) framework to enhance PSQA. The framework can combine the strengths of measurement- and cal

SUBMITTER: Looe HK 

PROVIDER: S-EPMC11880640 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature