ALERT-RA: an aperture library-enabled real-time respiratory motion adaptive framework for 4D-VMAT

Area of Science:

• Medical Physics
• Radiation Oncology
• Image-Guided Therapy

Background:

• Real-time tumor motion management is crucial for VMAT.
• Adaptive radiotherapy aims to adjust treatment based on intra-fraction motion.
• Robustness to trajectory variations is a key challenge in adaptive VMAT.

Purpose of the Study:

• To develop a robust optimization framework for real-time adaptive VMAT.
• To evaluate the robustness of the proposed framework against tumor trajectory variations.

Main Methods:

• Developed the aperture library-enabled real-time robust adaptation (ALERT-RA) framework.
• Optimized MLC apertures considering probabilistic respiratory motion models.
• Simulated treatment delivery 50 times per fraction to assess cumulative dose robustness.
• Compared ALERT-RA with optimized tracking, conformal tracking, and motion-encompassing methods.

Main Results:

• ALERT-RA demonstrated superior target coverage robustness compared to optimized and conformal tracking methods.
• For conventional fractionation, ALERT-RA achieved 92-97% prescription dose volume, outperforming others.
• ALERT-RA showed comparable or better robustness than motion-encompassing methods with lower OAR dose.
• SBRT results also favored ALERT-RA for robustness and comparable OAR sparing.

Conclusions:

• The ALERT-RA algorithm provides robust and high-quality adaptive VMAT plans.
• It effectively mitigates uncertainties arising from tumor motion trajectory variations.
• ALERT-RA represents a significant advancement in motion-adaptive radiotherapy techniques.