An Automatic Approach for Satisfying Dose-Volume Constraints in Linear Fluence Map Optimization for IMPT

Area of Science:

• Radiation Oncology
• Medical Physics
• Computational Biology

Background:

• Radiation therapy planning relies on dose-volume constraints (DVCs) for treatment efficacy and safety.
• Optimizing radiation therapy plans to meet DVCs often involves complex, iterative trial-and-error adjustments.

Purpose of the Study:

• To develop and evaluate an iterative, multi-objective linear programming (LP) approach for solving the fluence map optimization (FMO) problem.
• To improve the satisfaction of DVCs in radiation therapy planning by balancing target coverage and organ-at-risk (OAR) sparing.

Main Methods:

• An iterative LP model was developed to solve for beamlet intensities, gradually updating parameters to satisfy DVCs.
• The proposed LP-based heuristic algorithm was compared against a nonlinear FMO model (L-BFGS) and a commercial treatment planning system (Eclipse 8.9).
• The study retrospectively analyzed treatment plans for six cancer patients (five lung, one prostate).

Main Results:

• The LP-based heuristic approach successfully improved target coverage and met DVCs, while also satisfying OAR DVCs.
• The proposed algorithm demonstrated superior DVC satisfaction compared to the commercial treatment planning system.
• The L-BFGS nonlinear model satisfied DVCs in only three of five test cases, with limited recourse for unsatisfied constraints.

Conclusions:

• The iterative LP-based heuristic algorithm offers an effective and robust method for satisfying DVCs in radiation therapy planning.
• This approach is less sensitive to initial parameter values, reducing the trial-and-error burden.
• The method shows promise for improving treatment plan quality and efficiency in clinical practice.