ARCHERRT - a GPU-based and photon-electron coupled Monte Carlo dose computing engine for radiation therapy: software development and application to helical tomotherapy

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.

Dose, dose, dose, but where is the patient dose?

The article reviews the historical developments in radiation dose metrices in medical imaging. It identifies the good, the bad, and the ugly aspects of current-day metrices. The actions on shifting focus from International Commission on Radiological Protection (ICRP) Reference-Man-based population-average phantoms to patient-specific computational phantoms have been proposed and discussed. Technological developments in recent years involving AI-based automatic organ segmentation and 'near real-time' Monte Carlo dose calculations suggest the feasibility and advantage of obtaining patient-specific organ doses. It appears that the time for ICRP and other international organizations to embrace 'patient-specific' dose quantity representing risk may have finally come. While the existing dose metrices meet specific demands, emphasis needs to be also placed on making radiation units understandable to the medical community.

CPU-GPU-coupled acceleration method for point flux calculation in Monte Carlo particle transport

In Monte Carlo particle transport simulations, point flux tallying is a variance reduction technique that performs well with small detectors and finds broad application in source-detector problems and local point dose calculations. However, its use in large-scale point flux tallying computation adds substantial computational time. To address this issue, we propose a CPU-GPU-coupled acceleration method, which separates the complex logic and computationally intensive parts of particle transport calculation and assigns them to the CPU and GPU, respectively. This proposed method greatly enhances the efficiency of large-scale point flux tallies, providing significant convenience for subsequent dose calculations and other related steps. We validated our method by comparing the performance of a pure CPU program with our CPU-GPU accelerated program using the NUREG/CR-6115 PWR benchmark problem. The results indicate identical outcomes for photon point flux estimation, with the accelerated program being ~50 times faster.

A fully automated machine learning-based methodology for personalized radiation dose assessment in thoracic and abdomen CT

PURPOSE

To develop a machine learning-based methodology for patient-specific radiation dosimetry in thoracic and abdomen CT.

METHODS

Three hundred and thirty-one thoracoabdominal radiotherapy-planning CT examinations with the respective organ/patient contours were collected retrospectively for the development and validation of segmentation 3D-UNets. Moreover, 97 diagnostic thoracic and 89 diagnostic abdomen CT examinations were collected retrospectively. For each of the diagnostic CT examinations, personalized MC dosimetry was performed. The data derived from MC simulations along with the respective CT data were used for the training and validation of a dose prediction deep neural network (DNN). An algorithm was developed to utilize the trained models and perform patient-specific organ dose estimates for thoracic and abdomen CT examinations. The doses estimated with the DNN were compared with the respective doses derived from MC simulations. A paired t-test was conducted between the DNN and MC results. Furthermore, the time efficiency of the proposed methodology was assessed.

RESULTS

The mean percentage differences (range) between DNN and MC dose estimates for the lungs, liver, spleen, stomach, and kidneys were 7.2 % (0.2-24.1 %), 5.5 % (0.4-23.0 %), 7.9 % (0.6-22.3 %), 6.9 % (0.0-23.0 %) and 6.7 % (0.3-22.6 %) respectively. The differences between DNN and MC dose estimates were not significant (p-value = 0.12). Moreover, the mean processing time of the proposed workflow was 99 % lower than the respective time needed for MC-based dosimetry.

CONCLUSIONS

The proposed methodology can be used for rapid and accurate patient-specific dosimetry in chest and abdomen CT.

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

BACKGROUND

Adaptive radiotherapy (ART) has made significant advances owing to magnetic resonance linear accelerator (MR-LINAC), which provides superior soft-tissue contrast, fast speed and rich functional magnetic resonance imaging (MRI) to guide radiotherapy. Independent dose verification plays a critical role in discovering errors, while several challenges remain in MR-LINAC.

PURPOSE

A Monte Carlo-based GPU-accelerated dose verification module for Unity is proposed and integrated into the commercial software ArcherQA to achieve fast and accurate quality assurance (QA) for online ART.

METHODS

Electron or positron motion in a magnetic field was implemented, and a material-dependent step-length limit method was used to trade off speed and accuracy. Transport was verified by dose comparison with EGSnrc in three A-B-A phantoms. Then, an accurate Monte Carlo-based Unity machine model was built in ArcherQA, including an MR-LINAC head, cryostat, coils, and treatment couch. In particular, a mixed model combining measured attenuation and homogeneous geometry was adopted for the cryostat. Several parameters in the LINAC model were tuned to commission it in the water tank. An alternating open-closed MLC plan on solid water measured with EBT-XD film was used to verify the LINAC model. Finally, the ArcherQA dose was compared with ArcCHECK measurements and GPUMCD in 30 clinical cases through the gamma test.

RESULTS

ArcherQA and EGSnrc were well matched in three A-B-A phantom tests, and the relative dose difference (RDD) was less than 1.6% in the homogenous region. A Unity model was commissioned in the water tank, and the RDD in the homogenous region was less than 2%. In the alternating open-closed MLC plan, the gamma result (3%/3 mm) between ArcherQA and Film was 96.55%, better than the gamma result between GPUMCD and Film (92.13%). In 30 clinical cases, the mean three-dimensional (3D) gamma result (3%/2 mm) was 99.36% ± 1.28% between ArcherQA and ArcCHECK for the QA plans and 99.27% ± 1.04% between ArcherQA and GPUMCD for the clinical patient plans. The average dose calculation time was 106 s in all clinical patient plans.

CONCLUSIONS

A GPU-accelerated Monte Carlo-based dose verification module was developed and built for the Unity MR-LINAC. The fast speed and high accuracy were proven by comparison with EGSnrc, commission data, the ArcCHECK measurement dose, and the GPUMCD dose. This module can achieve fast and accurate independent dose verification for Unity.

AAPM Task Group Report 306: Quality control and assurance for tomotherapy: An update to Task Group Report 148

Since the publication of AAPM Task Group (TG) 148 on quality assurance (QA) for helical tomotherapy, there have been many new developments on the tomotherapy platform involving treatment delivery, on-board imaging options, motion management, and treatment planning systems (TPSs). In response to a need for guidance on quality control (QC) and QA for these technologies, the AAPM Therapy Physics Committee commissioned TG 306 to review these changes and make recommendations related to these technology updates. The specific objectives of this TG were (1) to update, as needed, recommendations on tolerance limits, frequencies and QC/QA testing methodology in TG 148, (2) address the commissioning and necessary QA checks, as a supplement to Medical Physics Practice Guidelines (MPPG) with respect to tomotherapy TPS and (3) to provide risk-based recommendations on the new technology implemented clinically and treatment delivery workflow. Detailed recommendations on QA tests and their tolerance levels are provided for dynamic jaws, binary multileaf collimators, and Synchrony motion management. A subset of TPS commissioning and QA checks in MPPG 5.a. applicable to tomotherapy are recommended. In addition, failure mode and effects analysis has been conducted among TG members to obtain multi-institutional analysis on tomotherapy-related failure modes and their effect ranking.

Dosimetric comparison of deformable image registration and synthetic CT generation based on CBCT images for organs at risk in cervical cancer radiotherapy

OBJECTIVE

Anatomical variations existing in cervical cancer radiotherapy treatment can be monitored by cone-beam computed tomography (CBCT) images. Deformable image registration (DIR) from planning CT (pCT) to CBCT images and synthetic CT (sCT) image generation based on CBCT are two methods for improving the quality of CBCT images. This study aims to compare the accuracy of these two approaches geometrically and dosimetrically in cervical cancer radiotherapy.

METHODS

In this study, 40 paired pCT-CBCT images were collected to evaluate the accuracy of DIR and sCT generation. The DIR method was based on a 3D multistage registration network that was trained with 150 paired pCT-CBCT images, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN) with 6000 paired pCT-CBCT slices for training. Then, the doses were recalculated with the CBCT, pCT, deformed pCT (dpCT) and sCT images by a GPU-based Monte Carlo dose code, ArcherQA, to obtain Dose_CBCT, Dose_pCT, Dose_dpCT and Dose_sCT. Organs at risk (OARs) included small intestine, rectum, bladder, spinal cord, femoral heads and bone marrow, CBCT and pCT contours were delineated manually, dpCT contours were propagated through deformation vector fields, sCT contours were auto-segmented and corrected manually.

RESULTS

The global gamma pass rate of Dose_sCT and Dose_dpCT was 99.66% ± 0.34%, while that of Dose_CBCT and Dose_dpCT was 85.92% ± 7.56% at the 1%/1 mm criterion and a low-dose threshold of 10%. Based on Dose_dpCT as uniform dose distribution, there were comparable errors in femoral heads and bone marrow for the dpCT and sCT contours compared with CBCT contours, while sCT contours had lower errors in small intestine, rectum, bladder and spinal cord, especially for those with large volume difference of pCT and CBCT.

CONCLUSIONS

For cervical cancer radiotherapy, the DIR method and sCT generation could produce similar precise dose distributions, but sCT contours had higher accuracy when the difference in planning CT and CBCT was large.

Effective clinical applications of Monte Carlo-based independent secondary dose verification software for helical tomotherapy

PURPOSE

To investigate the scope of the effective clinical application of Monte Carlo (MC)-based independent dose verification software for helical tomotherapy.

METHODS

DoseCHECK was selected as the MC-based dose calculation software. First, the dose calculation accuracy of DoseCHECK was evaluated with film and chamber measurements in a water-equivalent phantom. Second, the dose calculation accuracy was examined in several heterogeneous materials. Finally, dosimetric comparisons between DoseCHECK and the treatment planning system (TPS) were performed for clinical patient plans. Prostate IMRT, head and neck IMRT (HN), total body irradiation (TBI), and brain stereotactic radiotherapy (SRT) were evaluated.

RESULT

The DoseCHECK calculations agreed with the chamber and film measurements in the homogenous phantom. For heterogeneous phantom cases, the dose differences between DoseCHECK and TPS were within 3 %, except in air, in which large dose differences of 20 % were observed. In clinical patient plans, the median dose differences between the lung D_mean in TBI cases and the normal brain D_mean in brain SRT cases were significantly >3 %. For HN and brain SRT cases, the median target dose differences were >3 %.

CONCLUSION

Our results show that independent dose verification with the MC algorithm can detect systematic errors caused by the lack of heterogeneity correction in the TPS. In particular, MC-based independent dose verification is required for HN, TBI, and brain SRT cases in helical tomotherapy.

Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations

PURPOSE

This study proposes a method to identify plans that failed patient-specific quality assurance (QA) and attempts to establish a criterion to prescreen treatment plans for patient-specific QA measurements with independent Monte Carlo dose calculations.

MATERIALS AND METHODS

Patient-specific QA results measured with an ArcCHECK diode array of 207 patients (head and neck: 25; thorax: 61; abdomen: 121) were retrospectively analyzed. All patients were treated with the volumetric modulated arc therapy (VMAT) technique and plans were optimized with a Pinnacle v16.2 treatment planning system using an analytical algorithm-based dose engine. Afterwards, phantom verification plans were designed and recalculated by an independent GPU-accelerated Monte Carlo (MC) dose engine, ArcherQA. Moreover, sensitivity and specificity analyzes of gamma passing rates between measurements and MC calculations were carried out to show the ability of MC to monitor failing plans (ArcCHECK 3%/3 mm,<90%), and attempt to determine the appropriate threshold and gamma passing rate criterion utilized by ArcherQA to prescreen treatment plans for ArcCHECK measurements. The receiver operator characteristic (ROC) curve was also utilized to characterize the performance of different gamma passing rate criterion used by ArcherQA.

RESULTS

The thresholds for 100% sensitivity to detect plans that failed patient-specific QA by independent calculation were 97.0%, 95.4%, and 91.0% for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively, which corresponded to specificities of 0.720, 0.528, and 0.585, respectively. It was shown that the 3%/3 mm criterion with 97% threshold for ArcherQA demonstrated perfect sensitivity and the highest specificity compared with other criteria, which may be suitable for prescreening treatment plans treated with the investigated machine to implement measurement-based patient-specific QA of patient plans. In addition, the area under the curve (AUC) calculated from ROC analysis for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm used by ArcherQA were 0.948, 0.924, and 0.929, respectively.

CONCLUSIONS

Independent dose calculation with the MC-based program ArcherQA has potential as a prescreen treatment for measurement-based patient-specific QA. AUC values (>0.9) showed excellent classification accuracy for monitoring failing plans with independent MC calculations.

Development of a GPU-superposition Monte Carlo code for fast dose calculation in magnetic fields

Objective. To develop and validate a graphics processing unit (GPU) based superposition Monte Carlo (SMC) code for efficient and accurate dose calculation in magnetic fields. Approach. A series of mono-energy photons ranging from 25 keV to 7.7 MeV were simulated with EGSnrc in a water phantom to generate particle tracks database. SMC physics was extended with charged particle transport in magnetic fields and subsequently programmed on GPU as gSMC. Optimized simulation scheme was designed by combining variance reduction techniques to relieve the thread divergence issue in general GPU-MC codes and improve the calculation efficiency. The gSMC code’s dose calculation accuracy and efficiency were assessed through both phantoms and patient cases. Main results. gSMC accurately calculated the dose in various phantoms for both B = 0 T and B = 1.5 T, and it matched EGSnrc well with a root mean square error of less than 1.0% for the entire depth dose region. Patient cases validation also showed a high dose agreement with EGSnrc with 3D gamma passing rate (2%/2 mm) large than 97% for all tested tumor sites. Combined with photon splitting and particle track repeating techniques, gSMC resolved the thread divergence issue and showed an efficiency gain of 186–304 relative to EGSnrc with 10 CPU threads. Significance. A GPU-superposition Monte Carlo code called gSMC was developed and validated for dose calculation in magnetic fields. The developed code’s high calculation accuracy and efficiency make it suitable for dose calculation tasks in online adaptive radiotherapy with MR-LINAC.

Real-time estimation of patient-specific dose distributions for medical CT using the deep dose estimation

PURPOSE

With the rising number of computed tomography (CT) examinations and the trend toward personalized medicine, patient-specific dose estimates are becoming more and more important in CT imaging. However, current approaches are often too slow or too inaccurate to be applied routinely. Therefore, we propose the so-called deep dose estimation (DDE) to provide highly accurate patient dose distributions in real time METHODS: To combine accuracy and computational performance, the DDE algorithm uses a deep convolutional neural network to predict patient dose distributions. To do so, a U-net like architecture is trained to reproduce Monte Carlo simulations from a two-channel input consisting of a CT reconstruction and a first-order dose estimate. Here, the corresponding training data were generated using CT simulations based on 45 whole-body patient scans. For each patient, simulations were performed for different anatomies (pelvis, abdomen, thorax, head), different tube voltages (80 kV, 100 kV, 120 kV), different scan trajectories (circle, spiral), and with and without bowtie filtration and tube current modulation. Similar simulations were performed using a second set of eight whole-body CT scans from the Visual Concept Extraction Challenge in Radiology (Visceral) project to generate testing data. Finally, the DDE algorithm was evaluated with respect to the generalization to different scan parameters and the accuracy of organ dose and effective dose estimates based on an external organ segmentation.

RESULTS

DDE dose distributions were quantified in terms of the mean absolute percentage error (MAPE) and a gamma analysis with respect to the ground truth Monte Carlo simulation. Both measures indicate that DDE generalizes well to different scan parameters and different anatomical regions with a maximum MAPE of 6.3% and a minimum gamma passing rate of 91%. Evaluating the organ dose values for all organs listed in the International Commission on Radiological Protection (ICRP) recommendation, shows an average error of 3.1% and maximum error of 7.2% (bone surface).

CONCLUSIONS

The DDE algorithm provides an efficient approach to determine highly accurate dose distributions. Being able to process a whole-body CT scan in about 1.5 s, it provides a valuable alternative to Monte Carlo simulations on a graphics processing unit (GPU). Here, the main advantage of DDE is that it can be used on top of any existing Monte Carlo code such that real-time performance can be achieved without major adjustments. Thus, DDE opens up new options not only for dosimetry but also for scan and protocol optimization.

Development of a GPU-accelerated Monte Carlo dose calculation module for nuclear medicine, ARCHER-NM: demonstration for a PET/CT imaging procedure

Objective. This paper describes the development and validation of a GPU-accelerated Monte Carlo (MC) dose computing module dedicated to organ dose calculations of individual patients undergoing nuclear medicine (NM) internal radiation exposures involving PET/CT examination. Approach. This new module extends the more-than-10-years-long ARCHER project that developed a GPU-accelerated MC dose engine by adding dedicated NM source-definition features. To validate the code, we compared dose distributions from the point ion source, including 18F, 11C, 15O, and 68Ga, calculated for a water phantom against a well-tested MC code, GATE. To demonstrate the clinical utility and advantage of ARCHER-NM, one set of 18F-FDG PET/CT data for an adult male NM patient is calculated using the new code. Radiosensitive organs in the CT dataset are segmented using a CNN-based tool called DeepViewer. The PET image intensity maps are converted to radioactivity distributions to allow for MC radiation transport dose calculations at the voxel level. The dose rate maps and corresponding statistical uncertainties were calculated at the acquisition time of PET image. Main results. The water-phantom results show excellent agreement, suggesting that the radiation physics module in the new NM code is adequate. The dose rate results of the 18F-FDG PET imaging patient show that ARCHER-NM’s results agree very well with those of the GATE within −2.45% to 2.58% (for a total of 28 organs considered in this study). Most impressively, ARCHER-NM obtains such results in 22 s while it takes GATE about 180 min for the same number of 5 × 108 simulated decay events. Significance. This is the first study presenting GPU-accelerated patient-specific MC internal radiation dose rate calculations for clinically realistic 18F-FDG PET/CT imaging case involving autosegmentation of whole-body PET/CT images. This study suggests that the proposed computing tools—ARCHER-NM— are accurate and fast enough for routine internal dosimetry in NM clinics.

XIORT-MC: A real-time MC-based dose computation tool for low- energy X-rays intraoperative radiation therapy

PURPOSE

The INTRABEAM system is a miniature accelerator for low-energy X-ray Intra-Operative Radiation Therapy (IORT), and it could benefit from a fast and accurate dose computation tool. With regards to accuracy, dose computed with Monte Carlo (MC) simulations are the gold standard, however, they require a large computational effort and consequently they are not suitable for real-time dose planning. This work presents a comparison of the implementation on Graphics Processing Unit (GPU) of two different dose calculation algorithms based on MC phase-space (PHSP) information to compute dose distributions for the INTRABEAM device within seconds and with the accuracy of realistic MC simulations.

METHODS

The MC-based algorithms we present incorporate photoelectric, Compton and Rayleigh effects for the interaction of low-energy X-rays. XIORT-MC (X-ray Intra-Operative Radiation Therapy Monte Carlo) includes two dose calculation algorithms; a Woodcock-based MC algorithm (WC-MC) and a Hybrid MC algorithm (HMC), and it is implemented in CPU and in GPU. Detailed MC simulations have been generated to validate our tool in homogeneous and heterogeneous conditions with all INTRABEAM applicators, including three clinically realistic CT-based simulations. A performance study has been done to determine the acceleration reached with the code, in both CPU and GPU implementations.

RESULTS

Dose distributions were obtained with the HMC and the WC-MC and compared to standard reference MC simulations with more than 95% voxels fulfilling a 7%-0.5 mm gamma evaluation in all the cases considered. The CPU-HMC is 100 times more efficient than the reference MC, and the CPU-WC-MC is about 50 times more efficient. With the GPU implementation, the particle tracking of the WC-MC is faster than the HMC, with the extraction of the particle's information from the PHSP file taking a major part of the time. However, thanks to the variance reduction techniques implemented in the HMC, up to 400 times less particles are needed in the HMC to reach the same level of noise than the WC-MC. Therefore, in our implementation for INTRABEAM energies, the HMC is about 1.3 times more efficient than the WC-MC in an NVIDIA GeForce GTX 1080 Ti card and about 5.5 times more efficient in an NVIDIA GeForce RTX 3090. Dose with noise below 5% has been obtained in realistic situations in less than 5 s with the WC-MC and in less than 0.5 s with the HMC.

CONCLUSIONS

The XIORT-MC is a dose computation tool designed to take full advantage of modern GPUs, making possible to obtain MC-grade accurate dose distributions within seconds. Its high speed allows a real-time dose calculation that includes the realistic effects of the beam in voxelized geometries of patients. It can be used as a dose-planning tool in the operating room during a XIORT treatment with any INTRABEAM device.

A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing

PURPOSE

One technical barrier to patient-specific computed tomography (CT) dosimetry has been the lack of computational tools for the automatic patient-specific multi-organ segmentation of CT images and rapid organ dose quantification. When previous CT images are available for the same body region of the patient, the ability to obtain patient-specific organ doses for CT - in a similar manner as radiation therapy treatment planning - will open the door to personalized and prospective CT scan protocols. This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of multiple radiosensitive organs from CT images with the GPU-based Monte Carlo rapid organ dose calculation.

METHODS

A deep convolutional neural network (CNN) based on the U-Net for organ segmentation is developed and trained to automatically delineate multiple radiosensitive organs from CT images. Two databases are used: The lung CT segmentation challenge 2017 (LCTSC) dataset that contains 60 thoracic CT scan patients, each consisting of five segmented organs, and the Pancreas-CT (PCT) dataset, which contains 43 abdominal CT scan patients each consisting of eight segmented organs. A fivefold cross-validation method is performed on both sets of data. Dice similarity coefficients (DSCs) are used to evaluate the segmentation performance against the ground truth. A GPU-based Monte Carlo dose code, ARCHER, is used to calculate patient-specific CT organ doses. The proposed method is evaluated in terms of relative dose errors (RDEs). To demonstrate the potential improvement of the new method, organ dose results are compared against those obtained for population-average patient phantoms used in an off-line dose reporting software, VirtualDose, at Massachusetts General Hospital.

RESULTS

The median DSCs are found to be 0.97 (right lung), 0.96 (left lung), 0.92 (heart), 0.86 (spinal cord), 0.76 (esophagus) for the LCTSC dataset, along with 0.96 (spleen), 0.96 (liver), 0.95 (left kidney), 0.90 (stomach), 0.87 (gall bladder), 0.80 (pancreas), 0.75 (esophagus), and 0.61 (duodenum) for the PCT dataset. Comparing with organ dose results from population-averaged phantoms, the new patient-specific method achieved smaller absolute RDEs (mean ± standard deviation) for all organs: 1.8% ± 1.4% (vs 16.0% ± 11.8%) for the lung, 0.8% ± 0.7% (vs 34.0% ± 31.1%) for the heart, 1.6% ± 1.7% (vs 45.7% ± 29.3%) for the esophagus, 0.6% ± 1.2% (vs 15.8% ± 12.7%) for the spleen, 1.2% ± 1.0% (vs 18.1% ± 15.7%) for the pancreas, 0.9% ± 0.6% (vs 20.0% ± 15.2%) for the left kidney, 1.7% ± 3.1% (vs 19.1% ± 9.8%) for the gallbladder, 0.3% ± 0.3% (vs 24.2% ± 18.7%) for the liver, and 1.6% ± 1.7% (vs 19.3% ± 13.6%) for the stomach. The trained automatic segmentation tool takes <5 s per patient for all 103 patients in the dataset. The Monte Carlo radiation dose calculations performed in parallel to the segmentation process using the GPU-accelerated ARCHER code take <4 s per patient to achieve <0.5% statistical uncertainty in all organ doses for all 103 patients in the database.

CONCLUSION

This work shows the feasibility to perform combined automatic patient-specific multi-organ segmentation of CT images and rapid GPU-based Monte Carlo dose quantification with clinically acceptable accuracy and efficiency.

Development and validation of an open source Monte Carlo dosimetry model for wide-beam CT scanners using Fluka

PURPOSE

Development and validation of an open source Fluka-based Monte Carlo source model for diagnostic patient dose calculations.

METHODS

A framework to simulate a computed tomography (CT) scanner using Fluka Monte Carlo particle transport code was developed. The General Electric (GE) Revolution scanner with the large body filter and 120 kV tube potential was characterized using measurements. The model was validated on benchmark CT test problems and on dose measurements in computed tomography dose index (CTDI) and anthropomorphic phantoms. Axial and helical operation modes with provision for tube current modulation (TCM) were implemented. The particle simulations in Fluka were accelerated by executing them on a high-performance computing cluster.

RESULTS

The simulation results agreed to better than an average of 4% of the reference simulation results from the AAPM Report 195 test scenarios, namely: better than 2% for both test problems in case 4 using the PMMA phantom, and better than 5% of the reference result for 14 of 17 organs in case 5, and within 10% for the three remaining organs. The Fluka simulation results agreed to better than 2% of the air kerma measured in-air at isocenter of the GE Revolution scanner. The simulated air kerma in the center of the CTDI phantom overestimated the measurement by 7.5% and a correction factor was introduced to account for this. The simulated mean absorbed doses for a chest scan of the pediatric anthropomorphic phantom was completed in ~9 min and agreed to within the 95% CI for bone, soft tissue, and lung measurements made using MOSFET detectors for fixed current axial and helical scans as well as helical scan with TCM.

CONCLUSION

A Fluka-based Monte Carlo simulation model of axial and helical acquisition techniques using a wide-beam collimation CT scanner demonstrated good agreement between measured and simulated results for both fixed current and TCM in complex and simple geometries. Code and dataset will be made available at https://github.com/chezhia/FLUKA_CT.

Innovations in Computer Technologies Have Impacted Radiation Dosimetry Through Anatomically Realistic Phantoms and Fast Monte Carlo Simulations

Radiological physics principles have not changed in the past 60 y when computer technologies advanced exponentially. The research field of anatomical modeling for the purpose of radiation dose calculations has experienced an explosion in activity in the past two decades. Such an exciting advancement is due to the feasibility of creating three-dimensional geometric details of the human anatomy from tomographic imaging and of performing Monte Carlo radiation transport simulations on increasingly fast and cheap personal computers. The advent of a new type of high-performance computing hardware in recent years-graphics processing units-has made it feasible to carry out time-consuming Monte Carlo calculations at near real-time speeds. This paper introduces the history of three generations of computational human phantoms (the stylized medical internal radiation dosimetry-type phantoms, the voxelized tomographic phantoms, and the boundary representation deformable phantoms) and new development of the graphics processing unit-based Monte Carlo radiation dose calculations. Examples are given for research projects performed by my students in applying computational phantoms and a new Monte Carlo code, ARCHER, to problems in radiation protection, imaging, and radiotherapy. Finally, the paper discusses challenges and future opportunities for research.

DEVELOPMENT OF A SET OF MESH-BASED AND AGE-DEPENDENT CHINESE PHANTOMS AND APPLICATION FOR CT DOSE CALCULATIONS

Phantoms for organ dose calculations are essential in radiation protection dosimetry. This article describes the development of a set of mesh-based and age-dependent phantoms for Chinese populations using reference data recommended by the Chinese government and by the International Atomic Energy Agency (IAEA). Existing mesh-based RPI adult male (RPI-AM) and RPI adult female (RPI-AF) phantoms were deformed to form new phantoms according to anatomical data for the height and weight of Chinese individuals of 5 years old male, 5 years old female, 10 years old male, 10 years old female,15 years old male, 15 years old female, adult male and adult female-named USTC-5 M, USTC-5F, USTC-10M, USTC-10F, USTC-15M, USTC-15F, USTC-AM and USTC-AF, respectively. Following procedures to ensure the accuracy, more than 120 organs/tissues in each model were adjusted to match the Chinese reference parameters and the mass errors were within 0.5%. To demonstrate the usefulness, these new set of phantoms were combined with a fully validated model of the GE LightSpeed Pro 16 multi-detector computed tomography (MDCT) scanner and the GPU-based ARCHER Monte Carlo code to compute organ doses from CT examinations. Organ doses for adult models were then compared with the data of RPI-AM and RPI-AF under the same conditions. The absorbed doses and the effective doses of RPI phantoms are found to be lower than these of the USTC adult phantoms whose body sizes are smaller. Comparisons for the doses among different ages and genders were also made. It was found that teenagers receive more radiation doses than adults do. Such Chinese-specific phantoms are clearly better suited in organ dose studies for the Chinese individuals than phantoms designed for western populations. As already demonstrated, data derived from age-specific Chinese phantoms can help CT operators and designers to optimize image quality and doses.

Towards real-time photon Monte Carlo dose calculation in the cloud

Near real-time application of Monte Carlo (MC) dose calculation in clinic and research is hindered by the long computational runtimes of established software. Currently, fast MC software solutions are available utilising accelerators such as graphical processing units (GPUs) or clusters based on central processing units (CPUs). Both platforms are expensive in terms of purchase costs and maintenance and, in case of the GPU, provide only limited scalability. In this work we propose a cloud-based MC solution, which offers high scalability of accurate photon dose calculations. The MC simulations run on a private virtual supercomputer that is formed in the cloud. Computational resources can be provisioned dynamically at low cost without upfront investment in expensive hardware. A client-server software solution has been developed which controls the simulations and transports data to and from the cloud efficiently and securely. The client application integrates seamlessly into a treatment planning system. It runs the MC simulation workflow automatically and securely exchanges simulation data with the server side application that controls the virtual supercomputer. Advanced encryption standards were used to add an additional security layer, which encrypts and decrypts patient data on-the-fly at the processor register level. We could show that our cloud-based MC framework enables near real-time dose computation. It delivers excellent linear scaling for high-resolution datasets with absolute runtimes of 1.1 seconds to 10.9 seconds for simulating a clinical prostate and liver case up to 1% statistical uncertainty. The computation runtimes include the transportation of data to and from the cloud as well as process scheduling and synchronisation overhead. Cloud-based MC simulations offer a fast, affordable and easily accessible alternative for near real-time accurate dose calculations to currently used GPU or cluster solutions.

Concurrent Monte Carlo transport and fluence optimization with fluence adjusting scalable transport Monte Carlo

PURPOSE

The future of radiation therapy will require advanced inverse planning solutions to support single-arc, multiple-arc, and "4π" delivery modes, which present unique challenges in finding an optimal treatment plan over a vast search space, while still preserving dosimetric accuracy. The successful clinical implementation of such methods would benefit from Monte Carlo (MC) based dose calculation methods, which can offer improvements in dosimetric accuracy when compared to deterministic methods. The standard method for MC based treatment planning optimization leverages the accuracy of the MC dose calculation and efficiency of well-developed optimization methods, by precalculating the fluence to dose relationship within a patient with MC methods and subsequently optimizing the fluence weights. However, the sequential nature of this implementation is computationally time consuming and memory intensive. Methods to reduce the overhead of the MC precalculation have been explored in the past, demonstrating promising reductions of computational time overhead, but with limited impact on the memory overhead due to the sequential nature of the dose calculation and fluence optimization. The authors propose an entirely new form of "concurrent" Monte Carlo treat plan optimization: a platform which optimizes the fluence during the dose calculation, reduces wasted computation time being spent on beamlets that weakly contribute to the final dose distribution, and requires only a low memory footprint to function. In this initial investigation, the authors explore the key theoretical and practical considerations of optimizing fluence in such a manner.

METHODS

The authors present a novel derivation and implementation of a gradient descent algorithm that allows for optimization during MC particle transport, based on highly stochastic information generated through particle transport of very few histories. A gradient rescaling and renormalization algorithm, and the concept of momentum from stochastic gradient descent were used to address obstacles unique to performing gradient descent fluence optimization during MC particle transport. The authors have applied their method to two simple geometrical phantoms, and one clinical patient geometry to examine the capability of this platform to generate conformal plans as well as assess its computational scaling and efficiency, respectively.

RESULTS

The authors obtain a reduction of at least 50% in total histories transported in their investigation compared to a theoretical unweighted beamlet calculation and subsequent fluence optimization method, and observe a roughly fixed optimization time overhead consisting of ∼10% of the total computation time in all cases. Finally, the authors demonstrate a negligible increase in memory overhead of ∼7-8 MB to allow for optimization of a clinical patient geometry surrounded by 36 beams using their platform.

CONCLUSIONS

This study demonstrates a fluence optimization approach, which could significantly improve the development of next generation radiation therapy solutions while incurring minimal additional computational overhead.

Fast CPU-based Monte Carlo simulation for radiotherapy dose calculation

Monte-Carlo (MC) simulations are considered to be the most accurate method for calculating dose distributions in radiotherapy. Its clinical application, however, still is limited by the long runtimes conventional implementations of MC algorithms require to deliver sufficiently accurate results on high resolution imaging data. In order to overcome this obstacle we developed the software-package PhiMC, which is capable of computing precise dose distributions in a sub-minute time-frame by leveraging the potential of modern many- and multi-core CPU-based computers. PhiMC is based on the well verified dose planning method (DPM). We could demonstrate that PhiMC delivers dose distributions which are in excellent agreement to DPM. The multi-core implementation of PhiMC scales well between different computer architectures and achieves a speed-up of up to 37[Formula: see text] compared to the original DPM code executed on a modern system. Furthermore, we could show that our CPU-based implementation on a modern workstation is between 1.25[Formula: see text] and 1.95[Formula: see text] faster than a well-known GPU implementation of the same simulation method on a NVIDIA Tesla C2050. Since CPUs work on several hundreds of GB RAM the typical GPU memory limitation does not apply for our implementation and high resolution clinical plans can be calculated.

An exponential growth of computational phantom research in radiation protection, imaging, and radiotherapy: a review of the fifty-year history

Radiation dose calculation using models of the human anatomy has been a subject of great interest to radiation protection, medical imaging, and radiotherapy. However, early pioneers of this field did not foresee the exponential growth of research activity as observed today. This review article walks the reader through the history of the research and development in this field of study which started some 50 years ago. This review identifies a clear progression of computational phantom complexity which can be denoted by three distinct generations. The first generation of stylized phantoms, representing a grouping of less than dozen models, was initially developed in the 1960s at Oak Ridge National Laboratory to calculate internal doses from nuclear medicine procedures. Despite their anatomical simplicity, these computational phantoms were the best tools available at the time for internal/external dosimetry, image evaluation, and treatment dose evaluations. A second generation of a large number of voxelized phantoms arose rapidly in the late 1980s as a result of the increased availability of tomographic medical imaging and computers. Surprisingly, the last decade saw the emergence of the third generation of phantoms which are based on advanced geometries called boundary representation (BREP) in the form of Non-Uniform Rational B-Splines (NURBS) or polygonal meshes. This new class of phantoms now consists of over 287 models including those used for non-ionizing radiation applications. This review article aims to provide the reader with a general understanding of how the field of computational phantoms came about and the technical challenges it faced at different times. This goal is achieved by defining basic geometry modeling techniques and by analyzing selected phantoms in terms of geometrical features and dosimetric problems to be solved. The rich historical information is summarized in four tables that are aided by highlights in the text on how some of the most well-known phantoms were developed and used in practice. Some of the information covered in this review has not been previously reported, for example, the CAM and CAF phantoms developed in 1970s for space radiation applications. The author also clarifies confusion about 'population-average' prospective dosimetry needed for radiological protection under the current ICRP radiation protection system and 'individualized' retrospective dosimetry often performed for medical physics studies. To illustrate the impact of computational phantoms, a section of this article is devoted to examples from the author's own research group. Finally the author explains an unexpected finding during the course of preparing for this article that the phantoms from the past 50 years followed a pattern of exponential growth. The review ends on a brief discussion of future research needs (a supplementary file '3DPhantoms.pdf' to figure 15 is available for download that will allow a reader to interactively visualize the phantoms in 3D).