Abstract
Prescriptions for radiation therapy are given in terms of dose-volume constraints (DVCs). Solving the fluence map optimization (FMO) problem while satisfying DVCs often requires a tedious trial-and-error for selecting appropriate dose control parameters on various organs. In this paper, we propose an iterative approach to satisfy DVCs using a multi-objective linear programming (LP) model for solving beamlet intensities. This algorithm, starting from arbitrary initial parameter values, gradually updates the values through an iterative solution process toward optimal solution. This method finds appropriate parameter values through the trade-off between OAR sparing and target coverage to improve the solution. We compared the plan quality and the satisfaction of the DVCs by the proposed algorithm with two nonlinear approaches: a nonlinear FMO model solved by using the L-BFGS algorithm and another approach solved by a commercial treatment planning system (Eclipse 8.9). We retrospectively selected from our institutional database five patients with lung cancer and one patient with prostate cancer for this study. Numerical results show that our approach successfully improved target coverage to meet the DVCs, while trying to keep corresponding OAR DVCs satisfied. The LBFGS algorithm for solving the nonlinear FMO model successfully satisfied the DVCs in three out of five test cases. However, there is no recourse in the nonlinear FMO model for correcting unsatisfied DVCs other than manually changing some parameter values through trial and error to derive a solution that more closely meets the DVC requirements. The LP-based heuristic algorithm outperformed the current treatment planning system in terms of DVC satisfaction. A major strength of the LP-based heuristic approach is that it is not sensitive to the starting condition.
Highlights
The primary goal of radiation therapy is to deliver the prescribed dose to the target while sparing the adjacent organs at risk (OARs) as much as possible
Results for the linear programming (LP) Heuristic Approach we applied our developed method to five cases of lung cancer
At the final iteration of the LP heuristic for the same patient, 99.6% of the PTV received a dose greater than or equal to 95% of the prescribed dose, which satisfied the reference criteria for DVC1, and 29.7% of the normal lung received a dose greater than or equal to 20 Gy, which was still less than the reference point for DVC3 (37%)
Summary
The primary goal of radiation therapy is to deliver the prescribed dose to the target while sparing the adjacent organs at risk (OARs) as much as possible. Intensitymodulated proton therapy (IMPT) is a powerful tool for designing and efficiently delivering highly conformal dose distributions to the target while simultaneously sparing the neighboring OARs to a greater degree than intensity-modulated radiation therapy. Ment plans for IMPT, different inverse planning approaches to fluence map optimization (FMO) have been proposed [1]. Radiation oncologists use dose-volume constraints (DVCs) to prescribe and control the dose to the target and OARs. The DVC specifies what fraction of a structure is allowed to receive a radiation dose higher than the specified upper threshold value or lower than the specified lower threshold value. According to the lung protocol at The University of Texas MD Anderson Cancer Center, the treatment planner may specify that
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