SU‐E‐T‐689: Monte Carlo‐Based Optimization for Lung SBRT Treatment Planning

Abstract

Purpose: Pencil beam algorithms used for clinical plan optimization have limited accuracy in lung dose calculation. The purpose of this study was to develop a framework for Monte Carlo‐based optimization in IMRT planning of lung tumors treated with SBRT. Methods: The in‐house‐developed planning framework consists of a graphical user interface, EGSnrc‐based dose engine, and an optimization platform. The interface was developed using Qt/C++ in Linux. Modeling of a Varian linear accelerator (21EX, 6MV) was performed using EGSnrc/BEAMnrc. Each particle has its path intersected with a virtual plane partitioned into a 20×20cm grid at 100cm SSD. The intersection index is recorded in the particleˈs LATCH variable. Deposited energy is accumulated based on the particleˈs index using a modified DOSXYZnrc code. 3D doses computed for each beamlet are saved into a binary file in sequential order. Beamlets have dimensions of either 0.5×0.5 or 1×1cm. Structure/organ contours are imported into the planning platform from Pinnacle. Target dose prescription and normal organ constraints are optimized using quadratic objective functions. A gradient‐based method is used for optimization. The dose calculation system was verified against measurements and applied to SBRT lung treatment plans. Results: Depth and profile doses computed in water for square‐shaped beams were, on average within 1%/1mm of measurements. SBRT treatment plans computed using MC for the final dose calculation and PB‐based optimization showed significant underdosage in the minimum PTV doses relative to the plan computed with PB‐based final calculation and optimization. In one example, when MC‐based optimization was employed, the PTV minimum dose coverage was increased from 75.3% to 90.2% relative to the plan with PB‐based optimization and MC‐based final calculation. Conclusions: Initial results suggest that Monte Carlo‐based optimization may help improve plan quality and target dose coverage in planning of small lung tumors.