Development and clinical application of a GPU-based Monte Carlo dose verification module and software for 1.5 T MR-LINAC

A new GPU-based Monte Carlo code for helium ion therapy

PURPOSE

This work presents an effort to extend the capabilities of the previously introduced GPU-based Monte Carlo code ARCHER for helium ion therapy.

METHODS

ARCHER performs helium ion transport simulations in voxelized geometry, covering kinetic energy levels up to 220 MeV/u. The physical processes are modeled using a class II condensed-history algorithm, considering ionization, energy straggling, multiple scattering, and elastic and inelastic nuclear interactions. A new nuclear-event-repeat algorithm is proposed to generate inelastic nuclear reaction products. Secondary protons, deuterons, tritons, and 3He particles are tracked, while other particles either deposit their energy locally or are ignored. The code is developed under the compute unified device architecture (CUDA) platform to improve computational efficiency. Validations are conducted by benchmarking our code against TOPAS in different phantoms.

RESULTS

Dose distribution comparisons demonstrate strong agreement between our code and TOPAS. The mean point-by-point local relative errors in the region where the dose exceeds 10% of the maximum dose range from 0.25% to 1.31% for all phantoms. In the strict 1%/1 mm criterion, gamma passing rates for a head-neck case, chest case, and prostate case are 99.8%, 96.9%, and 99.6%, respectively. Except for the lung phantom, ARCHER takes less than 10 s to simulate 10 million primary helium ions using a single NVIDIA GeForce RTX 3080 card (NVIDIA Corporation, Santa Clara, USA), while TOPAS requires several minutes on a computational platform with two Intel Xeon Gold 6348 CPUs (Intel Corporation, Santa Clara, USA) with 56 cores.

CONCLUSION

This work presents the development and benchmarking of the first GPU-based dose engine for helium ion therapy. The code has been proven to achieve high levels of accuracy and efficiency.

Validation of a Monte Carlo-based dose calculation engine including the 1.5 T magnetic field for independent dose-check in MRgRT

PURPOSE

Adaptive MRgRT by 1.5 T MR-linac requires independent verification of the plan-of-the-day by the primary TPS (MonacoTM) (M). Here we validated a Monte Carlo-based dose-check including the magnetostatic field, SciMoCaTM (S).

METHODS

M and S were validated first in water, by comparison with commissioning-dosimetry. PDD(2x2cm2) through a lung(air)-equivalent virtual-slab was then calculated. Clinical validation retrospectively included 161 SBRT plans, from five patients per-site: Pelvic-Nodes, Prostate, Liver, Pancreas, and Lungs. S-minus-M percentage differences (Δ%) were computed for target- and OARs-related dose-volume metrics. In-phantom dose verification per-patient was performed.

RESULTS

γ(2 %,1mm)-passing-rates (PR%) of in-water-computed PDD and transverse-dose-profiles vs. commissioning-dosimetry were (99.1 ± 2.0)% for M, and (99.3 ± 1.5)% for S. Calculated output-factors (OF) were typically within 1 % from measurements, except for OF(1x1cm2) which was misestimated by -4.4 % and + 2.2 %, by M and S respectively. Dose spikes (valleys) on the PDD(2x2cm2) by S across the lung-equivalent virtual-slab were slightly reduced with respect to M. In clinical plans, S computed higher V95% (p <0.05*, for pancreas and lung) and D2% (p <0.05*, for all sites) for the target, while D%>2% resulted for duodenal D(1cm3), in Pancreas-SBRT, and for mean-lung-dose, in Lung-SBRT. All mostly due to the underestimated OF(1x1cm2) by M. In-phantom dose verifications showed an average 1% increase in PR% by S vs. M.

CONCLUSIONS

Beam-model quality in S resulted equivalent to M, thus making S useful both for an independent validation of the same beam-model in M, and for a daily validation of the M-based online approval decisions, without significantly delaying the clinical workflow (2-3 min).

An automated commissioning method based on virtual source models: Customizing Monte Carlo dose verification models for individual accelerators

BACKGROUND

In pursuit of precise dose calculation and verification, the importance of beam modelling cannot be overstated, as it ensures an accurate distribution of particles incident upon the human body. The virtual source model, as one of the beam modelling methods, offers the advantage of not requiring detailed accelerator information. Although various virtual source models exist, manual adjustment to these models demands a substantial investment of time and computational resources. There has long been a desire to develop an efficient and automated approach for model commissioning.

PURPOSE

To develop an automatic commissioning method for the virtual source model to customize the accelerator model for independent Monte Carlo dose verification.

METHODS

Initially, the accelerator model is established using the virtual source model and self-developed Jaw and MLC models. Then, a fully automated iteration process is employed to adjust the parameters of the virtual source model. Three types of objective functions are designed to represent differences from water tank measurements. Each objective function is paired with a specific parameter for adjustment, and their effectiveness is demonstrated through physical evidence. In each iteration, parameters with the highest objective function percentage are chosen for adjustment, and step length is determined based on current objective function values. Iteration is terminated when changes in any direction from the optimal solution no longer produce an improvement. Dose verification model for nine accelerators has been accomplished using this method. Additionally, under the same initial conditions, verification models for Versa HD accelerator (FF and FFF modes) are established using this method, Nelder-Mead Simplex optimization method, and the Bayesian optimization method to compare the efficiency and quality of these three iterative approaches.

RESULTS

Iterations for all nine accelerators are completed within 30 iterations. The relative dose differences in dose fall-off region compared to water tank measurements are all less than 2%, and the average gamma passing rates (3%/2 mm) for ArcCHECK measurements in QA plans are all higher than 97%. For Versa HD accelerator in FFF and FF modes, the proposed method achieves an average relative dose difference below 1% within 11 and 13 iterations, respectively. In contrast, the Simplex optimization reached 1% within 78 iterations in FFF mode. Furthermore, the Simplex optimization in FF mode and Bayesian optimization in both modes failed to achieve a 1% difference within 100 iterations.

CONCLUSIONS

The proposed iterative method achieves fast and automated commissioning of dose verification models, contributing to accurate and reliable clinical dose verification.

Adaptive assessment based on fractional CBCT images for cervical cancer

PURPOSE

Anatomical and other changes during radiotherapy will cause inaccuracy of dose distributions, therefore the expectation for online adaptive radiation therapy (ART) is high in effectively reducing uncertainties due to intra-variation. However, ART requires extensive time and effort. This study investigated an adaptive assessment workflow based on fractional cone-beam computed tomography (CBCT) images.

METHODS

Image registration, synthetic CT (sCT) generation, auto-segmentation, and dose calculation were implemented and integrated into ArcherQA Adaptive Check. The rigid registration was based on ITK open source. The deformable image registration (DIR) method was based on a 3D multistage registration network, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN). The auto-segmentation of organs at risk (OARs) on sCT images was finished by a deep learning-based auto-segmentation software, DeepViewer. The contours of targets were obtained by the structure-guided registration. Finally, the dose calculation was based on a GPU-based Monte Carlo (MC) dose code, ArcherQA.

RESULTS

The dice similarity coefficient (DSCs) were over 0.86 for target volumes and over 0.79 for OARs. The gamma pass rate of ArcherQA versus Eclipse treatment planning system was more than 99% at the 2%/2 mm criterion with a low-dose threshold of 10%. The time for the whole process was less than 3 min. The dosimetric results of ArcherQA Adaptive Check were consistent with the Ethos scheduled plan, which can effectively identify the fractions that need the implementation of the Ethos adaptive plan.

CONCLUSION

This study integrated AI-based technologies and GPU-based MC technology to evaluate the dose distributions using fractional CBCT images, demonstrating remarkably high efficiency and precision to support future ART processes.

Clinical application of a GPU-accelerated monte carlo dose verification for cyberknife M6 with Iris collimator

PURPOSE

To apply an independent GPU-accelerated Monte Carlo (MC) dose verification for CyberKnife M6 with Iris collimator and evaluate the dose calculation accuracy of RayTracing (TPS-RT) algorithm and Monte Carlo (TPS-MC) algorithm in the Precision treatment planning system (TPS).

METHODS

GPU-accelerated MC algorithm (ArcherQA-CK) was integrated into a commercial dose verification system, ArcherQA, to implement the patient-specific quality assurance in the CyberKnife M6 system. 30 clinical cases (10 cases in head, and 10 cases in chest, and 10 cases in abdomen) were collected in this study. For each case, three different dose calculation methods (TPS-MC, TPS-RT and ArcherQA-CK) were implemented based on the same treatment plan and compared with each other. For evaluation, the 3D global gamma analysis and dose parameters of the target volume and organs at risk (OARs) were analyzed comparatively.

RESULTS

For gamma pass rates at the criterion of 2%/2 mm, the results were over 98.0% for TPS-MC vs.TPS-RT, TPS-MC vs. ArcherQA-CK and TPS-RT vs. ArcherQA-CK in head cases, 84.9% for TPS-MC vs.TPS-RT, 98.0% for TPS-MC vs. ArcherQA-CK and 83.3% for TPS-RT vs. ArcherQA-CK in chest cases, 98.2% for TPS-MC vs.TPS-RT, 99.4% for TPS-MC vs. ArcherQA-CK and 94.5% for TPS-RT vs. ArcherQA-CK in abdomen cases. For dose parameters of planning target volume (PTV) in chest cases, the deviations of TPS-RT vs. TPS-MC and ArcherQA-CK vs. TPS-MC had significant difference (P < 0.01), and the deviations of TPS-RT vs. TPS-MC and TPS-RT vs. ArcherQA-CK were similar (P > 0.05). ArcherQA-CK had less calculation time compared with TPS-MC (1.66 min vs. 65.11 min).

CONCLUSIONS

Our proposed MC dose engine (ArcherQA-CK) has a high degree of consistency with the Precision TPS-MC algorithm, which can quickly identify the calculation errors of TPS-RT algorithm for some chest cases. ArcherQA-CK can provide accurate patient-specific quality assurance in clinical practice.

Assessment of the deep learning-based gamma passing rate prediction system for 1.5 T magnetic resonance-guided linear accelerator

Measurement-based verification is impossible for the patient-specific quality assurance (QA) of online adaptive magnetic resonance imaging-guided radiotherapy (oMRgRT) because the patient remains on the couch throughout the session. We assessed a deep learning (DL) system for oMRgRT to predict the gamma passing rate (GPR). This study collected 125 verification plans [reference plan (RP), 100; adapted plan (AP), 25] from patients with prostate cancer treated using Elekta Unity. Based on our previous study, we employed a convolutional neural network that predicted the GPRs of nine pairs of gamma criteria from 1%/1 mm to 3%/3 mm. First, we trained and tested the DL model using RPs (n = 75 and n = 25 for training and testing, respectively) for its optimization. Second, we tested the GPR prediction accuracy using APs to determine whether the DL model could be applied to APs. The mean absolute error (MAE) and correlation coefficient (r) of the RPs were 1.22 ± 0.27% and 0.29 ± 0.10 in 3%/2 mm, 1.35 ± 0.16% and 0.37 ± 0.15 in 2%/2 mm, and 3.62 ± 0.55% and 0.32 ± 0.14 in 1%/1 mm, respectively. The MAE and r of the APs were 1.13 ± 0.33% and 0.35 ± 0.22 in 3%/2 mm, 1.68 ± 0.47% and 0.30 ± 0.11 in 2%/2 mm, and 5.08 ± 0.29% and 0.15 ± 0.10 in 1%/1 mm, respectively. The time cost was within 3 s for the prediction. The results suggest the DL-based model has the potential for rapid GPR prediction in Elekta Unity.

Is it necessary to perform measurement-based patient-specific quality assurance for online adaptive radiotherapy with Elekta Unity MR-Linac?

This study aimed to investigate the necessity of measurement-based patient-specific quality assurance (PSQA) for online adaptive radiotherapy by analyzing measurement-based PSQA results and calculation-based 3D independent dose verification results with Elekta Unity MR-Linac. There are two workflows for Elekta Unity enabled in the treatment planning system: adapt to position (ATP) and adapt to shape (ATS). ATP plans are those which have relatively slighter shifts from reference plans by adjusting beam shapes or weights, whereas ATS plans are the new plans optimized from the beginning with probable re-contouring targets and organs-at-risk. PSQA gamma passing rates were measured using an MR-compatible ArcCHECK diode array for 78 reference plans and corresponding 208 adaptive plans (129 ATP plans and 79 ATS plans) of Elekta Unity. Subsequently, the relationships between ATP, or ATS plans and reference plans were evaluated separately. The Pearson's r correlation coefficients between ATP or ATS adaptive plans and corresponding reference plans were also characterized using regression analysis. Moreover, the Bland-Altman plot method was used to describe the agreement of PSQA results between ATP or ATS adaptive plans and reference plans. Additionally, Monte Carlo-based independent dose verification software ArcherQA was used to perform secondary dose check for adaptive plans. For ArcCHECK measurements, the average gamma passing rates (ArcCHECK vs. TPS) of PSQA (3%/2 mm criterion) were 99.51% ± 0.88% and 99.43% ± 0.54% for ATP and ATS plans, respectively, which were higher than the corresponding reference plans 99.34% ± 1.04% (p < 0.05) and 99.20% ± 0.71% (p < 0.05), respectively. The Pearson's r correlation coefficients were 0.720 between ATP and reference plans and 0.300 between ATS and reference plans with ArcCHECK, respectively. Furthermore, >95% of data points of differences between both ATP and ATS plans and reference plans were within ±2σ (standard deviation) of the mean difference between adaptive and reference plans with ArcCHECK measurements. With ArcherQA calculation, the average gamma passing rates (ArcherQA vs. TPS) were 98.23% ± 1.64% and 98.15% ± 1.07% for ATP and ATS adaptive plans, separately. It might be unnecessary to perform measurement-based PSQA for both ATP and ATS adaptive plans for Unity if the gamma passing rates of both measurements of corresponding reference plans and independent dose verification of adaptive plans have high gamma passing rates. Periodic machine QA and verification of adaptive plans were recommended to ensure treatment safety.

ART2Dose: A comprehensive dose verification platform for online adaptive radiotherapy

BACKGROUND

Online adaptive radiotherapy (ART) involves the development of adaptable treatment plans that consider patient anatomical data obtained right prior to treatment administration, facilitated by cone-beam computed tomography guided adaptive radiotherapy (CTgART) and magnetic resonance image-guided adaptive radiotherapy (MRgART). To ensure accuracy of these adaptive plans, it is crucial to conduct calculation-based checks and independent verification of volumetric dose distribution, as measurement-based checks are not practical within online workflows. However, the absence of comprehensive, efficient, and highly integrated commercial software for secondary dose verification can impede the time-sensitive nature of online ART procedures.

PURPOSE

The main aim of this study is to introduce an efficient online quality assurance (QA) platform for online ART, and subsequently evaluate it on Ethos and Unity treatment delivery systems in our clinic.

METHODS

To enhance efficiency and ensure compliance with safety standards in online ART, ART2Dose, a secondary dose verification software, has been developed and integrated into our online QA workflow. This implementation spans all online ART treatments at our institution. The ART2Dose infrastructure comprises four key components: an SQLite database, a dose calculation server, a report generator, and a web portal. Through this infrastructure, file transfer, dose calculation, report generation, and report approval/archival are seamlessly managed, minimizing the need for user input when exporting RT DICOM files and approving the generated QA report. ART2Dose was compared with Mobius3D in pre-clinical evaluations on secondary dose verification for 40 adaptive plans. Additionally, a retrospective investigation was conducted utilizing 1302 CTgART fractions from ten treatment sites and 1278 MRgART fractions from seven treatment sites to evaluate the practical accuracy and efficiency of ART2Dose in routine clinical use.

RESULTS

With dedicated infrastructure and an integrated workflow, ART2Dose achieved gamma passing rates that were comparable to or higher than those of Mobius3D. Additionally, it significantly reduced the time required to complete pre-treatment checks by 3-4 min for each plan. In the retrospective analysis of clinical CTgART and MRgART fractions, ART2Dose demonstrated average gamma passing rates of 99.61 ± 0.83% and 97.75 ± 2.54%, respectively, using the 3%/2 mm criteria for region greater than 10% of prescription dose. The average calculation times for CTgART and MRgART were approximately 1 and 2 min, respectively.

CONCLUSION

Overall, the streamlined implementation of ART2Dose notably enhances the online ART workflow, offering reliable and efficient online QA while reducing time pressure in the clinic and minimizing labor-intensive work.

The Quality Assurance of a 1.5 T MR-Linac

The recent introduction of a commercial 1.5 T MR-linac system has considerably improved the image quality of the patient acquired in the treatment unit as well as enabling online adaptive radiation therapy (oART) treatment strategies. Quality Assurance (QA) of this new technology requires new methodology that allows for the high field MR in a linac environment. The presence of the magnetic field requires special attention to the phantoms, detectors, and tools to perform QA. Due to the design of the system, the integrated megavoltage imager (MVI) is essential for radiation beam calibrations and QA. Additionally, the alignment between the MR image system and the radiation isocenter must be checked. The MR-linac system has vendor-supplied phantoms for calibration and QA tests. However, users have developed their own routine QA systems to independently check that the machine is performing as required, as to ensure we are able to deliver the intended dose with sufficient certainty. The aim of this work is therefore to review the MR-linac specific QA procedures reported in the literature.