Kevin Souris

Fast multipurpose Monte Carlo simulation for proton therapy using multi‐ and many‐core CPU architectures

PURPOSE Accuracy in proton therapy treatment planning can be improved using Monte Carlo (MC) simulations. However the long computation time of such methods hinders their use in clinical routine. This work aims to develop a fast multipurpose Monte Carlo simulation tool for proton therapy using massively parallel central processing unit (CPU) architectures. METHODS A new Monte Carlo, called MCsquare (many-core Monte Carlo), has been designed and optimized for the last generation of Intel Xeon processors and Intel Xeon Phi coprocessors. These massively parallel architectures offer the flexibility and the computational power suitable to MC methods. The class-II condensed history algorithm of MCsquare provides a fast and yet accurate method of simulating heavy charged particles such as protons, deuterons, and alphas inside voxelized geometries. Hard ionizations, with energy losses above a user-specified threshold, are simulated individually while soft events are regrouped in a multiple scattering theory. Elastic and inelastic nuclear interactions are sampled from ICRU 63 differential cross sections, thereby allowing for the computation of prompt gamma emission profiles. MCsquare has been benchmarked with the gate/geant4 Monte Carlo application for homogeneous and heterogeneous geometries. RESULTS Comparisons with gate/geant4 for various geometries show deviations within 2%-1 mm. In spite of the limited memory bandwidth of the coprocessor simulation time is below 25 s for 10(7) primary 200 MeV protons in average soft tissues using all Xeon Phi and CPU resources embedded in a single desktop unit. CONCLUSIONS MCsquare exploits the flexibility of CPU architectures to provide a multipurpose MC simulation tool. Optimized code enables the use of accurate MC calculation within a reasonable computation time, adequate for clinical practice. MCsquare also simulates prompt gamma emission and can thus be used also for in vivo range verification.

Experimental assessment of proton dose calculation accuracy in inhomogeneous media

PURPOSE Proton therapy with Pencil Beam Scanning (PBS) has the potential to improve radiotherapy treatments. Unfortunately, its promises are jeopardized by the sensitivity of the dose distributions to uncertainties, including dose calculation accuracy in inhomogeneous media. Monte Carlo dose engines (MC) are expected to handle heterogeneities better than analytical algorithms like the pencil-beam convolution algorithm (PBA). In this study, an experimental phantom has been devised to maximize the effect of heterogeneities and to quantify the capability of several dose engines (MC and PBA) to handle these. METHODS An inhomogeneous phantom made of water surrounding a long insert of bone tissue substitute (1×10×10 cm3) was irradiated with a mono-energetic PBS field (10×10 cm2). A 2D ion chamber array (MatriXX, IBA Dosimetry GmbH) lied right behind the bone. The beam energy was such that the expected range of the protons exceeded the detector position in water and did not attain it in bone. The measurement was compared to the following engines: Geant4.9.5, PENH, MCsquare, as well as the MC and PBA algorithms of RayStation (RaySearch Laboratories AB). RESULTS For a γ-index criteria of 2%/2mm, the passing rates are 93.8% for Geant4.9.5, 97.4% for PENH, 93.4% for MCsquare, 95.9% for RayStation MC, and 44.7% for PBA. The differences in γ-index passing rates between MC and RayStation PBA calculations can exceed 50%. CONCLUSION The performance of dose calculation algorithms in highly inhomogeneous media was evaluated in a dedicated experiment. MC dose engines performed overall satisfactorily while large deviations were observed with PBA as expected.

Evaluation of Motion Mitigation using Abdominal Compression in the Clinical Implementation of Pencil Beam Scanning Proton Therapy of Liver Tumors

Purpose: To determine whether individual liver tumor patients can be safely treated with pencil beam scanning proton therapy. This study reports a planning preparation workflow that can be used for beam angle selection and the evaluation of the efficacy of abdominal compression (AC) to mitigate motion. Methods: Four‐dimensional computed tomography scans (4DCT) with and without AC were available from 10 liver tumor patients with fluoroscopy‐proven motion reduction by AC, previously treated using photons. For each scan, the motion amplitudes and the motion‐induced variation of water‐equivalent thickness (ΔWET) in each voxel of the target volume were evaluated during treatment plan preparation. Optimal proton beam angles were selected after volume analysis of the respective beam‐specific planning target volume (BSPTV). M⊥80 and ΔWET80 derived from the 80th percentiles of perpendicular motion amplitude (M⊥) and ΔWET were compared with and without AC. Proton plans were created on the average CT to achieve target coverage similar to that of the conventional photon treatments. 4D dynamic dose calculation was performed postplan by synchronizing proton beam delivery timing patterns to the 4DCT phases to assess interplay and fractionation effects, and to determine motion criteria for subsequent patient treatment. Results: Selected coplanar beam angles ranged between 180° and 39°, primarily from right lateral (oblique) and posterior (oblique) directions. While AC produced a significant reduction in mean Liver‐GTV dose, any reduction in mean heart dose was patient dependent and not significant. Similarly, AC resulted in reductions in M⊥, ΔWET, and BSPTV volumes and improved dose degradation (ΔD95 and ΔD1) within the CTV. For small motion (M⊥80 < 7 mm and ΔWET80 < 5 mm), motion mitigation was not needed. For moderate motion (M⊥80 7–10 mm or ΔWET80 5–7 mm), AC produced a modest improvement. For large motion (M⊥80 > 10 mm or ΔWET80 > 7 mm), AC and/or some other form of mitigation strategies were required. Conclusion: A workflow for screening patients’ motion characteristics and optimizing beam angle selection was established for the pencil beam scanning proton therapy treatment of liver tumors. Abdominal compression was found to be useful at mitigation of moderate and large motion.

TH-A-19A-08: Intel Xeon Phi Implementation of a Fast Multi-Purpose Monte Carlo Simulation for Proton Therapy

PURPOSE Recent studies have demonstrated the capability of graphics processing units (GPUs) to compute dose distributions using Monte Carlo (MC) methods within clinical time constraints. However, GPUs have a rigid vectorial architecture that favors the implementation of simplified particle transport algorithms, adapted to specific tasks. Our new, fast, and multipurpose MC code, named MCsquare, runs on Intel Xeon Phi coprocessors. This technology offers 60 independent cores, and therefore more flexibility to implement fast and yet generic MC functionalities, such as prompt gamma simulations. METHODS MCsquare implements several models and hence allows users to make their own tradeoff between speed and accuracy. A 200 MeV proton beam is simulated in a heterogeneous phantom using Geant4 and two configurations of MCsquare. The first one is the most conservative and accurate. The method of fictitious interactions handles the interfaces and secondary charged particles emitted in nuclear interactions are fully simulated. The second, faster configuration simplifies interface crossings and simulates only secondary protons after nuclear interaction events. Integral depth-dose and transversal profiles are compared to those of Geant4. Moreover, the production profile of prompt gammas is compared to PENH results. RESULTS Integral depth dose and transversal profiles computed by MCsquare and Geant4 are within 3%. The production of secondaries from nuclear interactions is slightly inaccurate at interfaces for the fastest configuration of MCsquare but this is unlikely to have any clinical impact. The computation time varies between 90 seconds for the most conservative settings to merely 59 seconds in the fastest configuration. Finally prompt gamma profiles are also in very good agreement with PENH results. CONCLUSION Our new, fast, and multi-purpose Monte Carlo code simulates prompt gammas and calculates dose distributions in less than a minute, which complies with clinical time constraints. It has been successfully validated with Geant4. This work has been financialy supported by InVivoIGT, a public/private partnership between UCL and IBA.

LET dependence of the response of a PTW-60019 microDiamond detector in a 62 MeV proton beam

This study was initiated following conclusions from earlier experimental work, performed in a low-energy carbon ion beam, indicating a significant LET dependence of the response of a PTW-60019 microDiamond detector. The purpose of this paper is to present a comparison between the response of the same PTW-60019 microDiamond detector and an IBA Roos-type ionization chamber as a function of depth in a 62MeV proton beam. Even though proton beams are considered as low linear energy transfer (LET) beams, the LET value increases slightly in the Bragg peak region. Contrary to the observations made in the carbon ion beam, in the 62MeV proton beam good agreement is found between both detectors in both the plateau and the distal edge region. No significant LET dependent response of the PTW-60019 microDiamond detector is observed consistent with other findings for proton beams in the literature, despite this particular detector exhibiting a substantial LET dependence in a carbon ion beam.

Validation and clinical implementation of an accurate Monte Carlo code for pencil beam scanning proton therapy

Abstract Monte Carlo (MC)‐based dose calculations are generally superior to analytical dose calculations (ADC) in modeling the dose distribution for proton pencil beam scanning (PBS) treatments. The purpose of this paper is to present a methodology for commissioning and validating an accurate MC code for PBS utilizing a parameterized source model, including an implementation of a range shifter, that can independently check the ADC in commercial treatment planning system (TPS) and fast Monte Carlo dose calculation in opensource platform (MCsquare). The source model parameters (including beam size, angular divergence and energy spread) and protons per MU were extracted and tuned at the nozzle exit by comparing Tool for Particle Simulation (TOPAS) simulations with a series of commissioning measurements using scintillation screen/CCD camera detector and ionization chambers. The range shifter was simulated as an independent object with geometric and material information. The MC calculation platform was validated through comprehensive measurements of single spots, field size factors (FSF) and three‐dimensional dose distributions of spread‐out Bragg peaks (SOBPs), both without and with the range shifter. Differences in field size factors and absolute output at various depths of SOBPs between measurement and simulation were within 2.2%, with and without a range shifter, indicating an accurate source model. TOPAS was also validated against anthropomorphic lung phantom measurements. Comparison of dose distributions and DVHs for representative liver and lung cases between independent MC and analytical dose calculations from a commercial TPS further highlights the limitations of the ADC in situations of highly heterogeneous geometries. The fast MC platform has been implemented within our clinical practice to provide additional independent dose validation/QA of the commercial ADC for patient plans. Using the independent MC, we can more efficiently commission ADC by reducing the amount of measured data required for low dose “halo” modeling, especially when a range shifter is employed.

Patient-specific bolus for range shifter air gap reduction in intensity-modulated proton therapy of head-and-neck cancer studied with Monte Carlo based plan optimization

BACKGROUND & PURPOSE Intensity-modulated proton therapy (IMPT) of superficial lesions requires pre-absorbing range shifter (RS) to deliver the more shallow spots. RS air gap minimization is important to avoid spot size degradation, but remains challenging in complex geometries such as in head-and-neck cancer (HNC). In this study, clinical endpoints were investigated for patient-specific bolus and for conventional RS solutions, making use of a Monte Carlo (MC) dose engine for IMPT optimization. METHODS AND MATERIALS For 5 oropharyngeal cancer patients, IMPT spot maps were generated using beamlets calculated with MC. The plans were optimized for three different RS configurations: 3D printed on-skin bolus, snout- and nozzle-mounted RS. Organ-at-risk (OAR) doses and late toxicity probabilities were compared between all configuration-specific optimized plans. RESULTS The use of bolus reduced the mean dose to all OARs compared to snout and nozzle-mounted RS. The contralateral parotid gland and supraglottic larynx received on average 2.9Gy and 4.2Gy less dose compared to the snout RS. Bolus reduced the average probability for xerostomia by 3.0%. For dysphagia, bolus reduced the probability by 2.7%. CONCLUSIONS Quantification of the dosimetric advantage of patient-specific bolus shows significant reductions compared to conventional RS solutions for xerostomia and dysphagia probability. These results motivate the development of a patient-specific bolus solution in IMPT for HNC.

TH‐CD‐BRA‐04: Assessing How Stochastic CT Noise Can Lead to Systematic Proton Range Errors

Purpose: To demonstrate that the discontinuous nature of the CT number to stopping power ratio (SPR) calibration curve, combined with the presence of uncorrelated zero-mean Gaussian CT noise, leads to non-negligible and tissue-dependent systematic errors in SPRs and proton range, typically not taken into account in usual safety margins for proton therapy. Methods: Increased systematic errors with noise standard deviation have first been observed in proton range Monte-Carlo simulations with stoichiometric calibrations, whereas only zero-mean random errors were expected. Their existence has then been proved analytically for arbitrary calibration curves and material distributions along the proton path and validated through continuous slowing down approximation (CSDA) simulations. Their importance relative to the other sources of uncertainty has then been estimated in head-and-neck, lung, and pelvis patient data for multiple beam orientations. CT noise has first been reduced using a double-pass median filtering approach and a Gaussian noise has then been added to obtain total standard deviations between 10 to 40 HU. Results: This study provides close form equations for the systematic error and uncertainty on SPR and proton range due to uncorrelated noise. They have shown to accurately match CSDA simulation results with realistic calibration curves and material distributions. Depending on the tissue distribution and the position of the discontinuities along the curve the resulting effect on range varies but has shown never to cancel out completely as opposed to common beliefs. The analysis performed on patient data with clinical calibration curves has confirmed that fact with estimated systematic range errors of 0.2–0.5% and uncertainties (4 σ) between 0.5 and 1% with typical CT noise levels. Conclusion: A new source of SPR and range systematic errors has been highlighted and proved not to be negligible compared to the 3.5% uncertainty reference value used for safety margin design This study is linked to a public partnership between UCL and IBA funded by the Walloon region under convention number 1017266 and 1217662

Feasibility of online IMPT adaptation using fast, automatic and robust dose restoration

Intensity-modulated proton therapy (IMPT) offers excellent dose conformity and healthy tissue sparing, but it can be substantially compromised in the presence of anatomical changes. A major dosimetric effect is caused by density changes, which alter the planned proton range in the patient. Three different methods, which automatically restore an IMPT plan dose on a daily CT image were implemented and compared: (1) simple dose restoration (DR) using optimization objectives of the initial plan, (2) voxel-wise dose restoration (vDR), and (3) isodose volume dose restoration (iDR). Dose restorations were calculated for three different clinical cases, selected to test different capabilities of the restoration methods: large range adaptation, complex dose distributions and robust re-optimization. All dose restorations were obtained in less than 5 min, without manual adjustments of the optimization settings. The evaluation of initial plans on repeated CTs showed large dose distortions, which were substantially reduced after restoration. In general, all dose restoration methods improved DVH-based scores in propagated target volumes and OARs. Analysis of local dose differences showed that, although all dose restorations performed similarly in high dose regions, iDR restored the initial dose with higher precision and accuracy in the whole patient anatomy. Median dose errors decreased from 13.55 Gy in distorted plan to 9.75 Gy (vDR), 6.2 Gy (DR) and 4.3 Gy (iDR). High quality dose restoration is essential to minimize or eventually by-pass the physician approval of the restored plan, as long as dose stability can be assumed. Motion (as well as setup and range uncertainties) can be taken into account by including robust optimization in the dose restoration. Restoring clinically-approved dose distribution on repeated CTs does not require new ROI segmentation and is compatible with an online adaptive workflow.

WE-AB-209-01: A Monte Carlo-Based Method to Include Random Errors in Robust Optimization

PURPOSE To develop an efficient method to implement random set-up errors and organ motion in robust optimization for proton therapy treatment planning. METHODS The plans were created with an in-house robust optimizer, coupled with a super-fast Monte Carlo (MC) engine (less than 1 minute for final dose). MC simulates random errors by shifting randomly the starting point of each particle, according to their probability distribution. Such strategy assumes a sufficient number of treatment fractions. Two strategies are presented: 1) Full robust optimization with beamlets that already include the effect of random errors and 2) Mixed robust optimization, where the nominal beamlets are involved but a correction term C modifies the prescription. Starting from C=0, the method alternates optimization of the spot weights with the nominal beamlets and updates of C, with C=Drandom-Dnominal and where Drandom results from a regular MC computation (without pre-computed beamlets) that simulates random errors. Updates of C can be triggered as often as necessary by running the MC engine with the last corrected values for the spot weights as input. The method was applied to lung and prostate cases. For both patients the range error was set to 3%, systematic setup error to 5mm and standard deviation for random errors to 5 mm. Comparison between full robust optimization and the mixed strategy (with 3 updates of C) is presented. RESULTS Target coverage was far below the clinical constraints (D95 > 95% of the prescribed dose) for plans where random errors were not simulated, especially for lung case. However, by using the proposed strategies the plans achieved a target coverage above clinical constraints. CONCLUSION Full robust optimization gives better results than the mixed strategy, but the latter can be useful in cases where a MC engine is not available or too computationally intensive for beamlets calculation.