Adaptive step size algorithm to increase efficiency of proton macro Monte Carlo dose calculation

The effect of voxelization in Monte Carlo simulation to validate Bragg peak characteristics for a pencil proton beam

Background

The purpose of this research was to show how the Bragg peak (BP) characteristics were affected by changing the voxel size in longitudinal and transverse directions in Monte Carlo (MC) simulations by using Geant4 and to calculate BP characteristics accurately by considering the voxel size effect for 68 MeV and 235.81 MeV.

Materials and methods

Different interpolation techniques were applied to simulation data to find the closest results to the experimental data.

Results

When the x-size of the voxel was increased 2 times at low energy, the maximum dose increase in the entrance and plateau regions were 17.8% and 17%, respectively, while BP curve shifted to the shallower region, resulting in a 0.5 mm reduction in the curable tumor width (W_80pd). At high energy, the maximum dose increase at the entrance and plateau regions were 0.4% and 0.6%, respectively, while it was observed that W_80pd did not change. When the y-z sizes of the voxel were increased 2 times at low energy, the maximum dose reduction at the entrance and plateau regions was 3.4%, but no change was observed in W_80pd. At high energy, when the y-z sizes of the voxel were increased 2.2 times, the maximum dose reduction at the entrance and plateau regions were 8.9% and 9.1%, respectively, while W_80pd increased by 0.5 mm. When linear, cubic spline, and Akima interpolations were applied to the simulation data, it was found that the results closest to the experimental data were obtained for Akima interpolations for both energies.

Conclusion

it has been shown that the voxel size effect for the longitudinal direction was more effective at low energy than at high energy. However, the voxel size effect for the transverse direction was more effective for high energy.

TriB-RT: Simultaneous optimization of photon, electron and proton beams

PURPOSE

To develop a novel treatment planning process (TPP) with simultaneous optimization of modulated photon, electron and proton beams for improved treatment plan quality in radiotherapy.

METHODS

A framework for fluence map optimization of Monte Carlo (MC) calculated beamlet dose distributions is developed to generate treatment plans consisting of photon, electron and spot scanning proton fields. Initially, in-house intensity modulated proton therapy (IMPT) plans are compared to proton plans created by a commercial treatment planning system (TPS). A triple beam radiotherapy (TriB-RT) plan is generated for an exemplary academic case and the dose contributions of the three particle types are investigated. To investigate the dosimetric potential, a TriB-RT plan is compared to an in-house IMPT plan for two clinically motivated cases. Benefits of TriB-RT for a fixed proton beam line with a single proton field are investigated.

RESULTS

In-house optimized IMPT are of at least equal or better quality than TPS-generated proton plans, and MC-based optimization shows dosimetric advantages for inhomogeneous situations. Concerning TriB-RT, for the academic case, the resulting plan shows substantial contribution of all particle types. For the clinically motivated case, improved sparing of organs at risk close to the target volume is achieved compared to IMPT (e.g. myelon and brainstem [Formula: see text] -37%) at cost of an increased low dose bath (healthy tissue V _10% +22%). In the scenario of a fixed proton beam line, TriB-RT plans are able to compensate the loss in degrees of freedom to substantially improve plan quality compared to a single field proton plan.

CONCLUSION

A novel TPP which simultaneously optimizes photon, electron and proton beams was successfully developed. TriB-RT shows the potential for improved treatment plan quality and is especially promising for cost-effective single-room proton solutions with a fixed beamline in combination with a conventional linac delivering photon and electron fields.

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy

Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model's ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.

Development of an extended Macro Monte Carlo method for efficient and accurate dose calculation in magnetic fields

MOTIVATION

Progress in the field of magnetic resonance (MR)-guided radiotherapy has triggered the need for fast and accurate dose calculation in presence of magnetic fields. The aim of this work is to satisfy this need by extending the macro Monte Carlo (MMC) method to enable dose calculation for photon, electron, and proton beams in a magnetic field.

METHODS

The MMC method is based on the transport of particles in macroscopic steps through an absorber by sampling the relevant physical quantities from a precalculated database containing probability distribution functions. To enable MMC particle transport in a magnetic field, a transformation accounting for the Lorentz force is applied for each macro step by rotating the sampled position and direction around the magnetic field vector. The transformed position and direction distributions on local geometries are validated against full MC for electron and proton pencil beams. To enable photon dose calculation, an in-house MC algorithm is used for photon transport and interaction. Emerging secondary charged particles are passed to MMC for transport and energy deposition. The extended MMC dose calculation accuracy and efficiency is assessed by comparison with EGSnrc (photon and electron beams) and Geant4 (proton beam) calculated dose distributions of different energies and homogeneous magnetic fields for broad beams impinging on water phantoms with bone and lung inhomogeneities.

RESULTS

The geometric transformation on the local geometries is able to reproduce the results of full MC for all investigated settings (difference in mean value and standard deviation <1%). Macro Monte Carlo calculated dose distributions in a homogeneous magnetic field are in agreement with EGSnrc and Geant4, respectively, with gamma passing rates >99.6% (global 2%, 2 mm and 10% threshold criteria) for all situations. MMC achieves a substantial efficiency gain of up to a factor of 21 (photon beam), 66 (electron beam), and 356 (proton beam) compared to EGSnrc or Geant4.

CONCLUSION

Efficient and accurate dose calculation in magnetic fields was successfully enabled by utilizing the developed extended MMC transport method for photon, electron, and proton beams.