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

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.

Intensity‐modulated proton therapy (IMPT) interplay effect evaluation of asymmetric breathing with simultaneous uncertainty considerations in patients with non‐small cell lung cancer

“…A large number of time-consuming computations were needed for this study. In order to speed up the calculation, we migrated our in-house developed treatment planning system (TPS) to a Graphic Processing Unit (GPU)-based computing platform, including the following three components: (a) a modified ray-casting-based dose and linear energy transfer (LET) calculation engine, 59,64 with the enhanced capability to account for inhomogeneity more accurately 65 ; (b) voxel- wise worst-case-based 3D, [10][11][12][13][14][15][16][17]20,[25][26][27][28]66,67,68 4D, 19 and LETguided 26,69 robust optimization; and (c) DVH-band method 20,61,63 to quantify plan robustness. The TPS was highly parallelized using Compute Unified Device Architecture (CUDA).…”

A Critical Review of LET-Based Intensity-Modulated Proton Therapy Plan Evaluation and Optimization for Head and Neck Cancer Management

“…Charnes and Cooper variable transformation was used to reformulate the original quasiconvex problem—which required sophisticated linear fractional programming—to a linear programming problem. The approach used by Cao and colleagues [ 98 ] did not use dose-volume/LET-volume constraints owing to the limitation of linear programming [ 104 , 105 ]], thus the determination of priority factors for dose and LET constraint terms was required. To customize the optimization in a user-friendly way, Inaniwa et al [ 101 ] used the summation of least-square terms in the dose- and dose-weighted-LET–based cost function.…”

High-dose-rate prostate brachytherapy inverse planning on dose-volume criteria by simulated annealing

“…Historically, these research fields have not been connected, perhaps due to the different scale of the problems. For optimization in IMRT, we refer to Ehrgott et al (2010) and the extensive literature review by Zaghian et al (2014). Simulated annealing is also applied to IMRT treatment planning, e.g., Cho et al (1998) optimize penalty functions to meet dose criteria.…”