Inverse planning for intensity-modulated arc therapy using direct aperture optimization

Area of Science:

• Radiation Oncology
• Medical Physics
• Computational Biology

Background:

• Intensity-modulated arc therapy (IMAT) offers advantages of rotational IMRT using conventional linear accelerators.
• The full potential of IMAT has been limited by the absence of automated, efficient treatment planning tools.

Purpose of the Study:

• To introduce and evaluate a novel inverse treatment planning algorithm, Direct Aperture Optimization (DAO), for Intensity-Modulated Arc Therapy (IMAT).
• To demonstrate the capability of DAO in generating efficient IMAT plans by optimizing aperture shapes and weights directly.

Main Methods:

• Developed DAO, an inverse planning algorithm that optimizes multi-leaf collimator (MLC) aperture positions and weights simultaneously.
• DAO enforces delivery constraints during optimization, eliminating the need for a separate leaf-sequencing step.
• Applied DAO to generate IMAT plans for a cylindrical phantom, a head and neck cancer patient, and a prostate cancer patient.

Main Results:

• DAO successfully generated inverse treatment plans for IMAT.
• The algorithm directly optimized aperture parameters, integrating planning and delivery constraint enforcement.
• Demonstrated feasibility across different clinical scenarios, including phantom and patient cases.

Conclusions:

• Direct Aperture Optimization (DAO) is a feasible and effective algorithm for automated IMAT treatment planning.
• DAO overcomes previous limitations by directly optimizing aperture shapes and weights, enabling efficient IMAT delivery.
• This approach facilitates the broader clinical adoption of IMAT by providing robust planning capabilities.