Automatic, machine‐agnostic, convolution‐based beam, and fluence modeling for Monte Carlo independent dose calculation

TL;DR

This paper introduces a fast, automated method for Monte Carlo dose calculations in radiotherapy, improving accuracy and efficiency for quality assurance.

Contribution

A machine-agnostic, convolution-based beam and fluence modeling method for efficient Monte Carlo dose calculation.

Findings

The method achieved over 97% gamma passing rates compared to reference beam data using 2%/2 mm criteria.

Dose calculations for clinical plans showed over 97% gamma passing rates with an average time of less than 1 minute.

The approach supports a wide range of clinical external photon beam machines and enables efficient QA for adaptive radiotherapy.

Abstract

Monte Carlo (MC)‐based independent dose calculation is increasingly sought after for plan‐ and delivery‐specific quality assurance (QA) in modern radiotherapy because of its high accuracy. It is particularly valuable for online adaptive radiotherapy, where measurement‐based QA solutions are impractical. However, challenges related to beam modeling, commissioning, and plan/delivery‐specific fluence calculation have hindered its widespread clinical adoption.

We propose a generic, automated, convolution‐based beam and fluence modeling method for MC dose calculation, assuming zero or very limited knowledge of the linear accelerator (LINAC) head, with all necessary information derived from water phantom measurements. Instead of conventional particle transport through beam modulation devices (the phase space‐based approach), we developed a direct convolution‐based method to model the effects of beam modulation devices on output factors and fluence for downstream particle transport in the patient's body.

The measurement data necessary for the beam model include the percent depth dose (PDD) profile of a reference field (typically 10 × 10 cm2), the diagonal profile of the largest field at the depth of maximum dose, and the output factors for representative field sizes formed by beam modulation devices (jaws/MLCs). The beam modeling process involves adjusting the energy spectrum to match the reference field PDD, optimizing the weighting factor for electron contamination, and encoding the output factors in a fluence convolution kernel. The fluence is calculated by convolving the intensity map defined by beam modulation devices and monitor units with the kernel, and the dose is calculated through a point source model with initial particles sampled from the fluence. This approach was demonstrated using an in‐house developed general‐purpose MC dose engine for various clinical LINACs, including those integrated with magnetic resonance imaging.

Compared to reference beam data, our calculations achieved average gamma passing rates of over 97% using the 2%/2 mm criteria. Compared to a sample of 20 clinical plans calculated by the treatment planning systems (TPS) across different beam modalities and treatment machines, our calculated dose achieved gamma passing rates of over 97% using the 3%/2 mm criteria with an average calculation time of less than 1 min.

The proposed machine‐agnostic, convolution‐based beam, and fluence modeling approach enabled efficient automatic commissioning for a wide range of clinical external photon beam machines. The fluence map‐based dose calculation approached sub‐minute dose calculation efficiency for arbitrary treatment plans. The proposed method has the potential to accelerate the adoption of MC calculation‐based QA for online adaptive radiotherapy.

Figures