TBS-BAO: fully automated beam angle optimization for IMRT guided by a total-beam-space reference plan.

Area of Science:

• Radiation Oncology
• Medical Physics
• Computational Biology

Background:

• Beam angle optimization (BAO) is crucial for radiotherapy quality but is challenging due to its complex nature.
• Non-coplanar robotic CyberKnife radiotherapy for prostate cancer presents unique BAO challenges.

Purpose of the Study:

• To introduce and evaluate TBS-BAO, a novel approach for solving the beam angle optimization problem.
• To assess the efficiency and quality of TBS-BAO for prostate cancer treatment planning.

Main Methods:

• Generated ideal Pareto-optimal dose distributions using multi-criterial fluence map optimization (FMO) in total-beam-space (TBS).
• Reproduced ideal dose distributions using segmentation/beam angle optimization (SEG/BAO) with a limited number of beams.
• Tested TBS-BAO on 33 prostate SBRT patients, generating plans with varying beam constraints.

Main Results:

• TBS-BAO automatically generated clinically feasible plans (approx. 25 beams) with quality comparable to 91-beam ideal reference plans.
• Compared to Erasmus-iCycle, TBS-BAO achieved similar plan quality with significantly reduced computation times (1.5-4.8 hours vs. 10.7 hours).
• TBS-BAO plans demonstrated comparable quality to manual plans but with fewer beams and reduced delivery times.

Conclusions:

• TBS-BAO effectively addresses the discrete, non-convex nature of the BAO problem.
• The proposed method offers an efficient and effective solution for automated radiotherapy treatment planning.
• TBS-BAO has the potential to improve treatment quality and reduce delivery times in clinical practice.