TITLE:
An Automatic Approach for Satisfying Dose-Volume Constraints in Linear Fluence Map Optimization for IMPT
AUTHORS:
Maryam Zaghian, Gino Lim, Wei Liu, Radhe Mohan
KEYWORDS:
Fluence Map Optimization (FMO); Linear Programming (LP); Nonlinear Programming (NLP); Dose-Volume Constraint (DVC); Intensity-Modulated Proton Therapy (IMPT)
JOURNAL NAME:
Journal of Cancer Therapy,
Vol.5 No.2,
February
25,
2014
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.