Monte Carlo dose calculations for high-dose-rate brachytherapy using GPU-accelerated processing

The effect of vaginal cylinder inhomogeneity on the HDR brachytherapy dose calculations using Monte Carlo simulations

PURPOSE

To analytically assess the heterogeneity effect of vaginal cylinders (VC) made of high-density plastics on dose calculations, considering the prescription point (surface or 5 mm beyond the surface), and benchmark the accuracy of a commercial model-based dose calculation (MBDC) algorithm using Monte Carlo (MC) simulations.

METHODS AND MATERIALS

The GEANT4 MC code was used to simulate a commercial 192 Ir HDR source and VC, with diameters ranging from 20 to 35 mm, inside a virtual water phantom. Standard plans were generated from a commercial treatment planning system [TPS-BrachyVision ACUROS (BV)] optimized for a treatment length of 5 cm through two dose calculation approaches: (1) assuming all the environment as water (i.e., Dw,w-MC & Dw,w-TG43 ) and (2) accounting for the heterogeneity of VC applicators (i.e., Dw,w-App-MC & Dw,w-App-MBDC ). The compared isodose lines, and dose & energy difference maps were extracted for analysis. In addition, the dose difference on the peripheral surface, along the applicator and at middle of treatment length, as well as apical tip was evaluated.

RESULTS

The Dw,w-App-MC results indicated that the VC heterogeneity can cause a dose reduction of (up to) % 6.8 on average (for all sizes) on the peripheral surface, translating to 1 mm shrinkage of the isodose lines compared to Dw,w-MC . In addition, the results denoted that BV overestimates the dose on the peripheral surface and apical tip of about 3.7% and 17.9%, respectively, (i.e., Dw,w-App-MBDC vs Dw,w-App-MC ) when prescribing to the surface. However, the difference between the two were negligible at the prescription point when prescribing to 5 mm beyond the surface.

CONCLUSION

The VCs' heterogeneity could cause dose reduction when prescribing dose to the surface of the applicator, and hence increases the level of uncertainty. Thus, reviewing the TG43 results, in addition to ACUROS, becomes prudent, when evaluating the surface coverage at the apex.

Monte Carlo study of TG-43 dosimetry parameters of GammaMed Plus high dose rate 192 Ir brachytherapy source using TOPAS

PURPOSE

To develop a simulation model for GammaMed Plus high dose rate ¹⁹² Ir brachytherapy source in TOPAS Monte Carlo software and validate it by calculating the TG-43 dosimetry parameters and comparing them with published data.

METHODS

We built a model for GammaMed Plus high dose rate brachytherapy source in TOPAS. The TG-43 dosimetry parameters including air-kerma strength S_K , dose-rate constant Λ, radial dose function g_L (r), and 2D anisotropy function F(r,θ) were calculated using Monte Carlo simulation with Geant4 physics models and NNDC ¹⁹² Ir spectrum. Calculations using an old ¹⁹² Ir spectrum were also carried out to evaluate the impact of incident spectrum and cross sections. The results were compared with published data.

RESULTS

For calculations using the NNDC spectrum, the air-kerma strength per unit source activity S_K /A and Λ were 1.0139 × 10^-7 U/Bq and 1.1101 cGy.h^-1 .U^-1 , which were 3.56% higher and 0.62% lower than the reference values, respectively. The g_L (r) agreed with reference values within 1% for radial distances from 2 mm to 20 cm. For radial distances of 1, 3, 5, and 10 cm, the agreements between F(r,θ) from this work and the reference data were within 1.5% for 15° < θ < 165°, and within 4% for all θ values. The discrepancies were attributed to the updated source spectrum and cross sections. They caused deviations of the S_K /A of 2.90% and 0.64%, respectively. As for g_L (r), they caused average deviations of -0.22% and 0.48%, respectively. Their impact on F(r,θ) was not quantified for the relatively high statistical uncertainties, but basically they did not result in significant discrepancies.

CONCLUSION

A model for GammaMed Plus high dose rate ¹⁹² Ir brachytherapy source was developed in TOPAS and validated following TG-43 protocols, which can be used for future studies. The impact of updated incident spectrum and cross sections on the dosimetry parameters was quantified.

A Novel GPU-based Fast Monte Carlo Photon Dose Calculating Method for Accurate Radiotherapy Treatment Planning

BACKGROUND

An accurate and fast radiation dose calculations method is the main part of treatment planning for successful radiation therapy.

OBJECTIVE

This work aimed to create a novel GPU-based fast Monte Carlo Photon Dose Code (MCPDC) as a fast and accurate tool in dose calculation for radiotherapy treatment planning.

MATERIALS AND METHODS

In this analytical study, MCDPC was written to implement photon MC simulation for energies 0.01 to 20 MeV and run on an NVIDIA GTX970. The code was validated using DOSXYZnrc results and experimental measurements, performed by a Mapcheck dosimeter. Using the innovative definition of photon and electron interactions, mean calculation time for the MCPDC was 5.4 sec for 5e7 source particle history, significantly less than that of DOSXYZnrc which was 400 min.

RESULTS

Considering the simulations in the anthropomorphic phantom with bone and lung inhomogeneity, more than 96.1% of all significant voxels passed the gamma criteria of 3%-3 mm. Compared to the experimental dosimetry results, 97.6% or more of all significant voxels passed the acceptable clinical gamma index of 3%-3 mm.

CONCLUSION

Very fast calculation speed and high accuracy in dose calculation may allow the MCPDC to be used in radiotherapy as a central component of a treatment plan verification system and also as the dose calculation engine for MC-based planning. MCPDC is currently being developed for electron dose calculation module and graphic user interface. In addition, future work on the applicability of the improved version of the MCPDC in transit dosimetry of megavoltage CT is in process.

Efficiency improvement in proton dose calculations with an equivalent restricted stopping power formalism

The equivalent restricted stopping power formalism is introduced for proton mean energy loss calculations under the continuous slowing down approximation. The objective is the acceleration of Monte Carlo dose calculations by allowing larger steps while preserving accuracy. The fractional energy loss per step length ϵ was obtained with a secant method and a Gauss-Kronrod quadrature estimation of the integral equation relating the mean energy loss to the step length. The midpoint rule of the Newton-Cotes formulae was then used to solve this equation, allowing the creation of a lookup table linking ϵ to the equivalent restricted stopping power L _eq, used here as a key physical quantity. The mean energy loss for any step length was simply defined as the product of the step length with L _eq. Proton inelastic collisions with electrons were added to GPUMCD, a GPU-based Monte Carlo dose calculation code. The proton continuous slowing-down was modelled with the L _eq formalism. GPUMCD was compared to Geant4 in a validation study where ionization processes alone were activated and a voxelized geometry was used. The energy straggling was first switched off to validate the L _eq formalism alone. Dose differences between Geant4 and GPUMCD were smaller than 0.31% for the L _eq formalism. The mean error and the standard deviation were below 0.035% and 0.038% respectively. 99.4 to 100% of GPUMCD dose points were consistent with a 0.3% dose tolerance. GPUMCD 80% falloff positions ([Formula: see text]) matched Geant's [Formula: see text] within 1 μm. With the energy straggling, dose differences were below 2.7% in the Bragg peak falloff and smaller than 0.83% elsewhere. The [Formula: see text] positions matched within 100 μm. The overall computation times to transport one million protons with GPUMCD were 31-173 ms. Under similar conditions, Geant4 computation times were 1.4-20 h. The L _eq formalism led to an intrinsic efficiency gain factor ranging between 30-630, increasing with the prescribed accuracy of simulations. The L _eq formalism allows larger steps leading to a [Formula: see text] algorithmic time complexity. It significantly accelerates Monte Carlo proton transport while preserving accuracy. It therefore constitutes a promising variance reduction technique for computing proton dose distributions in a clinical context.