Monte Carlo Modeling of Dynamic Tumor Tracking on a Gimbaled Linear Accelerator.

Purpose and Aim: The Vero4DRT (Brainlab AG) linear accelerator is capable of dynamic tumor tracking (DTT) by panning/tilting the radiation beam to follow respiratory-induced tumor motion in real time. In this study, the panning/tilting motion is modeled in Monte Carlo (MC) for quality assurance (QA) of four-dimensional (4D) dose distributions created within the treatment planning system (TPS). Materials and Methods: Step-and-shoot intensity-modulated radiation therapy plans were optimized for 10 previously treated liver patients. These plans were recalculated on multiple phases of a 4D computed tomography (4DCT) scan using MC while modeling panning/tilting. The dose distributions on each phase were accumulated to create a respiratory-weighted 4D dose distribution. Differences between the TPS and MC modeled doses were examined. Results: On average, 4D dose calculations in MC showed the maximum dose of an organ at risk (OAR) to be 10% greater than the TPS' three-dimensional dose calculation (collapsed cone [CC] convolution algorithm) predicted. MC's 4D dose calculations showed that 6 out of 24 OARs could exceed their specified dose limits, and calculated their maximum dose to be 4% higher on average (up to 13%) than the TPS' 4D dose calculations. Dose differences between MC and the TPS were greatest in the beam penumbra region. Conclusion: Modeling panning/tilting for DTT has been successfully modeled with MC and is a useful tool to QA respiratory-correlated 4D dose distributions. The dose differences between the TPS and MC calculations highlight the importance of using 4D MC to confirm the safety of OAR doses before DTT treatments.

Monte Carlo based continuous aperture optimization for VMAT on conventional and MR-linacs: A proof of concept.

BACKGROUND: Currently, the commercial treatment planning systems for magnetic-resonance guided linear accelerators (MR-linacs) only support step-and-shoot intensity-modulated radiation therapy (IMRT). However, recent studies have shown the feasibility of delivering arc therapy on MR-linacs, which is expected to improve dose distributions and delivery speed. By accurately accounting for the electron return effect in the presence of a magnetic field, a Monte Carlo (MC) algorithm is ideally suited for the inverse treatment planning of this technique. PURPOSE: We propose a novel MC-based continuous aperture optimization (MCCAO) algorithm for volumetric modulated arc therapy (VMAT), including applications to VMAT on MR-linacs and trajectory-based VMAT. A unique feature of MCCAO is that the continuous character of gantry rotation and multileaf collimator (MLC) motion is accounted for at every stage of the optimization. METHODS: The optimization process uses a multistage simulation of 4D dose distribution. A phase space is scored at the top surface of the MLC and the energy deposition of each particle history is mapped to its position in this phase space. A progressive sampling method is used, where both MLC leaf positions and monitor unit (MU) weights are randomly changed, while respecting the linac mechanical limits. Due to the continuous nature of the leaf motion, such changes affect not only a single control point, but propagate to the adjacent ones as well, and the corresponding dose distribution changes are accounted for. A dose-volume cost function is used, which includes the MC statistical uncertainty. RESULTS: We applied our optimization technique to various treatment sites, using standard and flattening-filter-free (FFF) 6 MV beam models, with and without a 1.5 T magnetic field. MCCAO generates deliverable plans, whose dose distributions are in good agreement with measurements on ArcCHECK and stereotactic radiosurgery End-To-End Phantom. CONCLUSIONS: We show that the novel MCCAO method generates VMAT plans that meet clinical objectives for both conventional and MR-linacs.

Monte Carlo simulation of 6-MV dynamic wave VMAT deliveries by Vero4DRT linear accelerator using EGSnrc moving sources.

The commissioning and benchmark of a Monte Carlo (MC) model of the 6-MV Brainlab-Mitsubishi Vero4DRT linear accelerator for the purpose of quality assurance of clinical dynamic wave arc (DWA) treatment plans is reported. Open-source MC applications based on EGSnrc particle transport codes are used to simulate the medical linear accelerator head components. Complex radiotherapy irradiations can be simulated in a single MC run using a shared library format combined with BEAMnrc "source20." Electron energy tuning is achieved by comparing measured vs simulated percentage depth doses (PDDs) for MLC-defined field sizes in a water phantom. Electron spot size tuning is achieved by comparing measured and simulated inplane and crossplane beam profiles. DWA treatment plans generated from RayStation (RaySearch) treatment planning system (TPS) are simulated on voxelized (2.5 mm3 ) patient CT datasets. Planning target volume (PTV) and organs at risk (OAR) dose-volume histograms (DVHs) are compared to TPS-calculated doses for clinically deliverable dynamic volumetric modulated arc therapy (VMAT) trajectories. MC simulations with an electron beam energy of 5.9 MeV and spot size FWHM of 1.9 mm had the closest agreement with measurement. DWA beam deliveries simulated on patient CT datasets results in DVH agreement with TPS-calculated doses. PTV coverage agreed within 0.1% and OAR max doses (to 0.035 cc volume) agreed within 1 Gy. This MC model can be used as an independent dose calculation from the TPS and as a quality assurance tool for complex, dynamic radiotherapy treatment deliveries. Full patient CT treatment simulations are performed in a single Monte Carlo run in 23 min. Simulations are run in parallel using the Condor High-Throughput Computing software1 on a cluster of eight servers. Each server has two physical processors (Intel Xeon CPU E5-2650 0 @2.00 GHz), with 8 cores per CPU and two threads per core for 256 calculation nodes.

Dose rate mapping of VMAT treatments.

Human tissues exhibit a varying response to radiation dose depending on the dose rate and fractionation scheme used. Dose rate effects have been reported for different radiations, and tissue types. The literature indicates that there is not a significant difference in response for low-LET radiation when using dose rates between 1 Gy min(-1) and 12 Gy min(-1) but lower dose rates have an observable sparing effect on tissues and a differential effect between tissues. In intensity-modulated radiotherapy such as volumetric modulated arc therapy (VMAT) the dose can be delivered with a wide range of dose rates. In this work we developed a method based on time-resolved Monte Carlo simulations to quantify the dose rate frequency distribution for clinical VMAT treatments for three cancer sites, head and neck, lung, and pelvis within both planning target volumes (PTV) and normal tissues. The results show a wide range of dose rates are used to deliver dose in VMAT and up to 75% of the PTV can have its dose delivered with dose rates <1 Gy min(-1). Pelvic plans on average have a lower mean dose rate within the PTV than lung or head and neck plans but a comparable mean dose rate within the organs at risk. Two VMAT plans that fulfil the same dose objectives and constraints may be delivered with different dose rate distributions, particularly when comparing single arcs to multiple arc plans. It is concluded that for dynamic plans, the dose rate range used varies to a larger degree than previously assumed. The effect of the dose rate range in VMAT on clinical outcome is unknown.

Techniques for adaptive prostate radiotherapy.

Variations in the position and shape of the prostate make accurate setup and treatment challenging. Adaptive radiation therapy (ART) techniques seek to alter the treatment plan, at one or more points throughout the treatment course, in response to changes in patient anatomy observed between planning and pre-treatment images. This article reviews existing and developing ART techniques for prostate cancer along with an overview of supporting in-room imaging technologies. Challenges to the clinical implementation of adaptive radiotherapy are also discussed.

A method to perform multi-scale Monte Carlo simulations in the clinical setting.

In order to model the track structure of clinical mega-voltage photon beams in a reasonable time, it is necessary to use a multi-scale approach incorporating a track-structure algorithm for the regions of interest and a condensed history algorithm for the rest of the geometry. This paper introduces a multi-scale Monte Carlo system, which is used to hand off particle trajectory information between the two algorithms. Since condensed history algorithms ignore electrons with energy below a fixed threshold and those electrons are important to the track structure on the micrometre scale, it is necessary to hand over all charged particles to the track-structure algorithm only in a volume that extends beyond the scoring volume. Additionally, the system is validated against experimental results for radio-isotope gamma spectra.

Two new DOSXYZnrc sources for 4D Monte Carlo simulations of continuously variable beam configurations, with applications to RapidArc, VMAT, TomoTherapy and CyberKnife.

We present two new Monte Carlo sources for the DOSXYZnrc code, which can be used to compute dose distributions due to continuously variable beam configurations. These sources support a continuously rotating gantry and collimator, dynamic multileaf collimator (MLC) motion, variable monitor unit (MU) rate, couch rotation and translation in any direction, arbitrary isocentre motion with respect to the patient and variable source-to-axis distance (SAD). These features make them applicable to Monte Carlo simulations for RapidArc, Elekta VMAT, TomoTherapy and CyberKnife. Unique to these sources is the synchronization between the motion in the DOSXYZnrc geometry and the motion within the linac head, represented by a shared library (either a BEAMnrc accelerator with dynamic component modules, or an external library). The simulations are achieved in single runs, with no intermediate phase space files.

Monte Carlo based, patient-specific RapidArc QA using Linac log files.

PURPOSE: A Monte Carlo (MC) based QA process to validate the dynamic beam delivery accuracy for Varian RapidArc (Varian Medical Systems, Palo Alto, CA) using Linac delivery log files (DynaLog) is presented. Using DynaLog file analysis and MC simulations, the goal of this article is to (a) confirm that adequate sampling is used in the RapidArc optimization algorithm (177 static gantry angles) and (b) to assess the physical machine performance [gantry angle and monitor unit (MU) delivery accuracy]. METHODS: Ten clinically acceptable RapidArc treatment plans were generated for various tumor sites and delivered to a water-equivalent cylindrical phantom on the treatment unit. Three Monte Carlo simulations were performed to calculate dose to the CT phantom image set: (a) One using a series of static gantry angles defined by 177 control points with treatment planning system (TPS) MLC control files (planning files), (b) one using continuous gantry rotation with TPS generated MLC control files, and (c) one using continuous gantry rotation with actual Linac delivery log files. Monte Carlo simulated dose distributions are compared to both ionization chamber point measurements and with RapidArc TPS calculated doses. The 3D dose distributions were compared using a 3D gamma-factor analysis, employing a 3%/3 mm distance-to-agreement criterion. RESULTS: The dose difference between MC simulations, TPS, and ionization chamber point measurements was less than 2.1%. For all plans, the MC calculated 3D dose distributions agreed well with the TPS calculated doses (gamma-factor values were less than 1 for more than 95% of the points considered). Machine performance QA was supplemented with an extensive DynaLog file analysis. A DynaLog file analysis showed that leaf position errors were less than 1 mm for 94% of the time and there were no leaf errors greater than 2.5 mm. The mean standard deviation in MU and gantry angle were 0.052 MU and 0.355 degrees, respectively, for the ten cases analyzed. CONCLUSIONS: The accuracy and flexibility of the Monte Carlo based RapidArc QA system were demonstrated. Good machine performance and accurate dose distribution delivery of RapidArc plans were observed. The sampling used in the TPS optimization algorithm was found to be adequate.

Direct aperture optimization for IMRT using Monte Carlo generated beamlets.

This work introduces an EGSnrc-based Monte Carlo (MC) beamlet does distribution matrix into a direct aperture optimization (DAO) algorithm for IMRT inverse planning. The technique is referred to as Monte Carlo-direct aperture optimization (MC-DAO). The goal is to assess if the combination of accurate Monte Carlo tissue inhomogeneity modeling and DAO inverse planning will improve the dose accuracy and treatment efficiency for treatment planning. Several authors have shown that the presence of small fields and/or inhomogeneous materials in IMRT treatment fields can cause dose calculation errors for algorithms that are unable to accurately model electronic disequilibrium. This issue may also affect the IMRT optimization process because the dose calculation algorithm may not properly model difficult geometries such as targets close to low-density regions (lung, air etc.). A clinical linear accelerator head is simulated using BEAMnrc (NRC, Canada). A novel in-house algorithm subdivides the resulting phase space into 2.5 X 5.0 mm2 beamlets. Each beamlet is projected onto a patient-specific phantom. The beamlet dose contribution to each voxel in a structure-of-interest is calculated using DOSXYZnrc. The multileaf collimator (MLC) leaf positions are linked to the location of the beamlet does distributions. The MLC shapes are optimized using direct aperture optimization (DAO). A final Monte Carlo calculation with MLC modeling is used to compute the final dose distribution. Monte Carlo simulation can generate accurate beamlet dose distributions for traditionally difficult-to-calculate geometries, particularly for small fields crossing regions of tissue inhomogeneity. The introduction of DAO results in an additional improvement by increasing the treatment delivery efficiency. For the examples presented in this paper the reduction in the total number of monitor units to deliver is approximately 33% compared to fluence-based optimization methods.