A technique for generating phase-space-based Monte Carlo beamlets in radiotherapy applications

Prediction of dose deposition matrix using voxel features driven machine learning approach

OBJECTIVES

A dose deposition matrix (DDM) prediction method using several voxel features and a machine learning (ML) approach is proposed for plan optimization in radiation therapy.

METHODS

Head and lung cases with the inhomogeneous medium are used as training and testing data. The prediction model is a cascade forward backprop neural network where the input is the features of the voxel, including 1) voxel to body surface distance along the beamlet axis, 2) voxel to beamlet axis distance, 3) voxel density, 4) heterogeneity corrected voxel to body surface distance, 5) heterogeneity corrected voxel to beamlet axis, and (6) the dose of voxel obtained from the pencil beam (PB) algorithm. The output is the predicted voxel dose corresponding to a beamlet. The predicted DDM was used for plan optimization (ML method) and compared with the dose of MC-based plan optimization (MC method) and the dose of pencil beam-based plan optimization (PB method). The mean absolute error (MAE) value was calculated for full volume relative to the dose of the MC method to evaluate the overall dose performance of the final plan.

RESULTS

For patient with head tumor, the ML method achieves MAE value 0.49 × 10-4 and PB has MAE 1.86 × 10-4. For patient with lung tumor, the ML method has MAE 1.42 × 10-4 and PB has MAE 3.72 × 10-4. The maximum percentage difference in PTV dose coverage (D98) between ML and MC methods is no more than 1.2% for patient with head tumor, while the difference is larger than 10% using the PB method. For patient with lung tumor, the maximum percentage difference in PTV dose coverage (D98) between ML and MC methods is no more than 2.1%, while the difference is larger than 16% using the PB method.

CONCLUSIONS

In this work, a reliable DDM prediction method is established for plan optimization by applying several voxel features and the ML approach. The results show that the ML method based on voxel features can obtain plans comparable to the MC method and is better than the PB method in achieving accurate dose to the patient, which is helpful for rapid plan optimization and accurate dose calculation.

ADVANCES IN KNOWLEDGE

Establishment of a new machine learning method based on the relationship between the voxel and beamlet features for dose deposition matrix prediction in radiation therapy.

Full Monte Carlo-Based Biologic Treatment Plan Optimization System for Intensity Modulated Carbon Ion Therapy on Graphics Processing Unit

PURPOSE

One of the major benefits of carbon ion therapy is enhanced biological effectiveness at the Bragg peak region. For intensity modulated carbon ion therapy (IMCT), it is desirable to use Monte Carlo (MC) methods to compute the properties of each pencil beam spot for treatment planning, because of their accuracy in modeling physics processes and estimating biological effects. We previously developed goCMC, a graphics processing unit (GPU)-oriented MC engine for carbon ion therapy. The purpose of the present study was to build a biological treatment plan optimization system using goCMC.

METHODS AND MATERIALS

The repair-misrepair-fixation model was implemented to compute the spatial distribution of linear-quadratic model parameters for each spot. A treatment plan optimization module was developed to minimize the difference between the prescribed and actual biological effect. We used a gradient-based algorithm to solve the optimization problem. The system was embedded in the Varian Eclipse treatment planning system under a client-server architecture to achieve a user-friendly planning environment. We tested the system with a 1-dimensional homogeneous water case and 3 3-dimensional patient cases.

RESULTS

Our system generated treatment plans with biological spread-out Bragg peaks covering the targeted regions and sparing critical structures. Using 4 NVidia GTX 1080 GPUs, the total computation time, including spot simulation, optimization, and final dose calculation, was 0.6 hour for the prostate case (8282 spots), 0.2 hour for the pancreas case (3795 spots), and 0.3 hour for the brain case (6724 spots). The computation time was dominated by MC spot simulation.

CONCLUSIONS

We built a biological treatment plan optimization system for IMCT that performs simulations using a fast MC engine, goCMC. To the best of our knowledge, this is the first time that full MC-based IMCT inverse planning has been achieved in a clinically viable time frame.

Monte Carlo systems used for treatment planning and dose verification

General-purpose radiation transport Monte Carlo codes have been used for estimation of the absorbed dose distribution in external photon and electron beam radiotherapy patients since several decades. Results obtained with these codes are usually more accurate than those provided by treatment planning systems based on non-stochastic methods. Traditionally, absorbed dose computations based on general-purpose Monte Carlo codes have been used only for research, owing to the difficulties associated with setting up a simulation and the long computation time required. To take advantage of radiation transport Monte Carlo codes applied to routine clinical practice, researchers and private companies have developed treatment planning and dose verification systems that are partly or fully based on fast Monte Carlo algorithms. This review presents a comprehensive list of the currently existing Monte Carlo systems that can be used to calculate or verify an external photon and electron beam radiotherapy treatment plan. Particular attention is given to those systems that are distributed, either freely or commercially, and that do not require programming tasks from the end user. These systems are compared in terms of features and the simulation time required to compute a set of benchmark calculations.

Monte Carlo calculations of an Elekta Precise SL-25 photon beam model

Background

Monte Carlo (MC) simulations have been used extensively for benchmarking photon dose calculations in modern radiotherapy using linear accelerators (linacs). Moreover, a major barrier to widespread clinical implementation of MC dose calculation is the difficulty in characterising the radiation source using data reported from manufacturers.

Purpose

This work aims to develop a generalised full MC histogram source model of an Elekta Precise SL-25 linac (electron exit window, target, flattening filter, monitor chambers and collimators) for 6 MV photon beams used in standard therapies. The inclusion of many different probability processes such as scatter, nuclear reactions, decay, capture cross-sections and more led to more realistic dose calculations in treatment planning and quality assurance.

Materials and methods

Two different codes, MCNPX 2·6 and EGSr-BEAM, were used for the calculation of particle transport, first in the geometry of the internal/external accelerator source, and then followed by tracking the transport and energy deposition in phantom-equivalent tissues. A full phase space file was scored directly above the upper multilayer collimator’s jaws to derive the beam characteristics such as planar fluence, angular distribution and energy spectrum. To check the quality of the generated photon beam, its depth dose curves and cross-beam profiles were calculated and compared with measured data.

Results

In-field dose distributions calculated using the accelerator models were tuned to match measurement data with preliminary calculations performed using the accelerator information provided by the manufacturer. Field sizes of 3×3, 5×5, 10×10, 15×15 and 20×20 cm2were analysed. Local differences between calculated and measured curve doses beneath 2% were obtained for all the studied field sizes. Higher discrepancies were obtained in the air–water interface, where measurements of dose distributions with the ionisation chamber need to be shifted for the effective point of measurement.

Conclusion

The agreements between MC-calculated and measured dose distributions were excellent for both codes, showing the strength and stability of the proposed model. Beam reconstruction methods as direct input to dose-calculation codes using the recorded histograms can be implemented for more accurate patient dose estimation.

Phase space determination from measured dose data for intraoperative electron radiation therapy

A procedure to characterize beams of a medical linear accelerator for their use in Monte Carlo (MC) dose calculations for intraoperative electron radiation therapy (IOERT) is presented. The procedure relies on dose measurements in homogeneous media as input, avoiding the need for detailed simulations of the accelerator head. An iterative algorithm (EM-ML) has been employed to extract the relevant details of the phase space (PHSP) of the particles coming from the accelerator, such as energy spectra, spatial distribution and angle of emission of particles. The algorithm can use pre-computed dose volumes in water and/or air, so that the machine-specific tuning with actual data can be performed in a few minutes. To test the procedure, MC simulations of a linear accelerator with typical IOERT applicators and energies, have been performed and taken as reference. A solution PHSP derived from the dose produced by the simulated accelerator has been compared to the reference PHSP. Further, dose delivered by the simulated accelerator for setups not included in the fit of the PHSP were compared to the ones derived from the solution PHSP. The results show that it is possible to derive from dose measurements PHSP accurate for IOERT MC dose estimations.

GPU-based Monte Carlo radiotherapy dose calculation using phase-space sources

A novel phase-space source implementation has been designed for graphics processing unit (GPU)-based Monte Carlo dose calculation engines. Short of full simulation of the linac head, using a phase-space source is the most accurate method to model a clinical radiation beam in dose calculations. However, in GPU-based Monte Carlo dose calculations where the computation efficiency is very high, the time required to read and process a large phase-space file becomes comparable to the particle transport time. Moreover, due to the parallelized nature of GPU hardware, it is essential to simultaneously transport particles of the same type and similar energies but separated spatially to yield a high efficiency. We present three methods for phase-space implementation that have been integrated into the most recent version of the GPU-based Monte Carlo radiotherapy dose calculation package gDPM v3.0. The first method is to sequentially read particles from a patient-dependent phase-space and sort them on-the-fly based on particle type and energy. The second method supplements this with a simple secondary collimator model and fluence map implementation so that patient-independent phase-space sources can be used. Finally, as the third method (called the phase-space-let, or PSL, method) we introduce a novel source implementation utilizing pre-processed patient-independent phase-spaces that are sorted by particle type, energy and position. Position bins located outside a rectangular region of interest enclosing the treatment field are ignored, substantially decreasing simulation time with little effect on the final dose distribution. The three methods were validated in absolute dose against BEAMnrc/DOSXYZnrc and compared using gamma-index tests (2%/2 mm above the 10% isodose). It was found that the PSL method has the optimal balance between accuracy and efficiency and thus is used as the default method in gDPM v3.0. Using the PSL method, open fields of 4 × 4, 10 × 10 and 30 × 30 cm(2) in water resulted in gamma passing rates of 99.96%, 99.92% and 98.66%, respectively. Relative output factors agreed within 1%. An intensity modulated radiation therapy patient plan using the PSL method resulted in a passing rate of 97%, and was calculated in 50 s (per GPU) compared to 8.4 h (per CPU) for BEAMnrc/DOSXYZnrc.

Generation of a novel phase-space-based cylindrical dose kernel for IMRT optimization

PURPOSE

Improving dose calculation accuracy is crucial in intensity-modulated radiation therapy (IMRT). We have developed a method for generating a phase-space-based dose kernel for IMRT planning of lung cancer patients.

METHODS

Particle transport in the linear accelerator treatment head of a 21EX, 6 MV photon beam (Varian Medical Systems, Palo Alto, CA) was simulated using the EGSnrc/BEAMnrc code system. The phase space information was recorded under the secondary jaws. Each particle in the phase space file was associated with a beamlet whose index was calculated and saved in the particle's LATCH variable. The DOSXYZnrc code was modified to accumulate the energy deposited by each particle based on its beamlet index. Furthermore, the central axis of each beamlet was calculated from the orientation of all the particles in this beamlet. A cylinder was then defined around the central axis so that only the energy deposited within the cylinder was counted. A look-up table was established for each cylinder during the tallying process. The efficiency and accuracy of the cylindrical beamlet energy deposition approach was evaluated using a treatment plan developed on a simulated lung phantom.

RESULTS

Profile and percentage depth doses computed in a water phantom for an open, square field size were within 1.5% of measurements. Dose optimized with the cylindrical dose kernel was found to be within 0.6% of that computed with the nontruncated 3D kernel. The cylindrical truncation reduced optimization time by approximately 80%.

CONCLUSIONS

A method for generating a phase-space-based dose kernel, using a truncated cylinder for scoring dose, in beamlet-based optimization of lung treatment planning was developed and found to be in good agreement with the standard, nontruncated scoring approach. Compared to previous techniques, our method significantly reduces computational time and memory requirements, which may be useful for Monte-Carlo-based 4D IMRT or IMAT treatment planning.

Design and development of a new micro-beam treatment planning system: effectiveness of algorithms of optimization and dose calculations and potential of micro-beam treatment

A new treatment planning system (TPS) was designed and developed for a new treatment system, which consisted of a micro-beam-enabled linac with robotics and a real-time tracking system. We also evaluated the effectiveness of the implemented algorithms of optimization and dose calculations in the TPS for the new treatment system. In the TPS, the optimization procedure consisted of the pseudo Beam's-Eye-View method for finding the optimized beam directions and the steepest-descent method for determination of beam intensities. We used the superposition-/convolution-based (SC-based) algorithm and Monte Carlo-based (MC-based) algorithm to calculate dose distributions using CT image data sets. In the SC-based algorithm, dose density scaling was applied for the calculation of inhomogeneous corrections. The MC-based algorithm was implemented with Geant4 toolkit and a phase-based approach using a network-parallel computing. From the evaluation of the TPS, the system can optimize the direction and intensity of individual beams. The accuracy of the dose calculated by the SC-based algorithm was less than 1% on average with the calculation time of 15 s for one beam. However, the MC-based algorithm needed 72 min for one beam using the phase-based approach, even though the MC-based algorithm with the parallel computing could decrease multiple beam calculations and had 18.4 times faster calculation speed using the parallel computing. The SC-based algorithm could be practically acceptable for the dose calculation in terms of the accuracy and computation time. Additionally, we have found a dosimetric advantage of proton Bragg peak-like dose distribution in micro-beam treatment.