Remote Global Radiation Oncology Education and Training: A Pathway to Increase Access to High-Quality Radiation Therapy Services in Low- and Middle-Income Countries.

Purpose: There is a vital need to train radiation therapy professionals in low- and middle-income countries (LMICs) to develop sustainable cancer treatment capacity and infrastructure. LMICs have started to introduce intensity modulated radiation therapy (IMRT), which is the standard of care in high-income countries, because of improved outcomes and reduced toxicities. This work reports the efficacy of a complementary asynchronous plus synchronous virtual-training approach on improving radiation therapy professions' self-confidence levels and evaluating participants' attitudes toward asynchronous and synchronous didactic hands-on learning in 3 LMICs. Methods and Materials: Training was provided to 37 participants from Uganda, Guatemala, and Mongolia, which included 4 theoretical lectures, 4 hands-on sessions, and 8 self-guided online videos. The 36-day training focused on IMRT contouring, site-specific target/organ definition, planning/optimization, and quality assurance. Participants completed pre- and postsession confidence surveys on a 0 to 10 scale, which was converted to a 5-point Likert rating scale to evaluate the training outcomes. The pros and cons of the 3 different training formats were compared. Results: The participants included 15 (40.5%) radiation oncologists, 11 (29.7%) medical physicists, 6 (16.2%) radiation therapists, and 5 (13.5%) dosimetrists. Approximately 50% had more than 10 years of radiation therapy experience, 70.8% had no formal IMRT training, and only 25% had IMRT at their institutions. The average experience and confidence levels in using IMRT at baseline were 3.2 and 2.9, which increased to 5.2 and 4.9 (P < .001) after the theoretical training. After the hands-on training, the experience and confidence levels further improved to 5.4 and 5.5 (P < .001). After the self-guided training, the confidence levels increased further to 6.9 (P < .01). Among the 3 different training sessions, hands-on trainings (58.3%) were most helpful for the development of participants' IMRT skills, followed by theoretical sessions with 25%. Conclusions: After completing the training sessions, Uganda and Mongolia started IMRT treatments. Remote training provides an excellent and feasible e-learning platform to train radiation therapy professionals in LMICs. The training program improved the IMRT confidence levels and treatment delivery. The hands-on trainings were most preferred.

Clinical application of a template-guided automated planning routine.

PURPOSE: Determine the dosimetric quality and the planning time reduction when utilizing a template-based automated planning application. METHODS: A software application integrated through the treatment planning system application programing interface, QuickPlan, was developed to facilitate automated planning using configurable templates for contouring, knowledge-based planning structure matching, field design, and algorithm settings. Validations are performed at various levels of the planning procedure and assist in the evaluation of readiness of the CT image, structure set, and plan layout for automated planning. QuickPlan is evaluated dosimetrically against 22 hippocampal-avoidance whole brain radiotherapy patients. The required times to treatment plan generation are compared for the validations set as well as 10 prospective patients whose plans have been automated by QuickPlan. RESULTS: The generations of 22 automated treatment plans are compared against a manual replanning using an identical process, resulting in dosimetric differences of minor clinical significance. The target dose to 2% volume and homogeneity index result in significantly decreased values for automated plans, whereas other dose metric evaluations are nonsignificant. The time to generate the treatment plans is reduced for all automated plans with a median difference of 9' 50″ ± 4' 33″. CONCLUSIONS: Template-based automated planning allows for reduced treatment planning time with consistent optimization structure creation, treatment field creation, plan optimization, and dose calculation with similar dosimetric quality. This process has potential expansion to numerous disease sites.

Simulation-Free Radiation Therapy: An Emerging Form of Treatment Planning to Expedite Plan Generation for Patients Receiving Palliative Radiation Therapy.

Purpose: Herein we report the clinical and dosimetric experience for patients with metastases treated with palliative simulation-free radiation therapy (SFRT) at a single institution. Methods and Materials: SFRT was performed at a single institution. Multiple fractionation regimens were used. Diagnostic imaging was used for treatment planning. Patient characteristics as well as planning and treatment time points were collected. A matched cohort of patients with conventional computed tomography simulation radiation therapy (CTRT) was acquired to evaluate for differences in planning and treatment time. SFRT dosimetry was evaluated to determine the fidelity of SFRT. Descriptive statistics were calculated on all variables and statistical significance was evaluated using the Wilcoxon signed rank test and t test methods. Results: Thirty sessions of SFRT were performed and matched with 30 sessions of CTRT. Seventy percent of SFRT and 63% of CTRT treatments were single fraction. The median time to plan generation was 0.88 days (0.19-1.47) for SFRT and 1.90 days (0.39-5.23) for CTRT (P = .02). The total treatment time was 41 minutes (28-64) for SFRT and 30 minutes (21-45) for CTRT (P = .02). In the SFRT courses, the maximum and mean deviations in the actual delivered dose from the approved plans for the maximum dose were 4.1% and 0.07%, respectively. All deliveries were within a 5% threshold and deemed clinically acceptable. Conclusions: Palliative SFRT is an emerging technique that allowed for a statistically significant lower time to plan generation and was dosimetrically acceptable. This benefit must be weighed against increased total treatment time for patients receiving SFRT compared with CTRT, and appropriate patient selection is critical.

A multi-layer strip ionization chamber (MLSIC) device for proton pencil beam scan quality assurance.

Objective. Proton pencil beam scanning (PBS) treatment fields needs to be verified before treatment deliveries to ensure patient safety. In current practice, treatment beam quality assurance (QA) is measured at a few selected depths using film or a 2D detector array, which is insensitive and time-consuming. A QA device that can measure all key dosimetric characteristics of treatment beams spot-by-spot within a single beam delivery is highly desired.Approach. We developed a multi-layer strip ionization chamber (MLSIC) prototype device that comprises of two layers of strip ionization chambers (IC) plates for spot position measurement and 64 layers of plate IC for beam energy measurement. The 768-channel strip ion chamber signals are integrated and sampled at a speed of 3.125 kHz. It has a 25.6 cm × 25.6 cm maximum measurement field size and 2 mm spatial resolution for spot position measurement. The depth resolution and maximum depth were 2.91 mm and 18.6 cm for 1.6 mm thick IC plate, respectively. The relative weight of each spot was determined from total charge by all IC detector channels.Main results. The MLSIC is able to measure ionization currents spot-by-spot. The depth dose measurement has a good agreement with the ground truth measured using a water tank and commercial one-dimensional (1D) multi-layer plate chamber. It can verify the spot position, energy, and relative weight of clinical PBS beams and compared with the treatment plans.Significance. The MLSIC is a highly efficient QA device that can measure the key dosimetric characteristics of proton treatment beams spot-by-spot with a single beam delivery. It may improve the quality and efficiency of clinical proton treatments.

Synchronized high-speed scintillation imaging of proton beams, generated by a gantry-mounted synchrocyclotron, on a pulse-by-pulse basis.

BACKGROUND: With the emergence of more complex and novel proton delivery techniques, there is a need for quality assurance tools with high spatiotemporal resolution to conveniently measure the spatial and temporal properties of the beam. In this context, scintillation-based dosimeters, if synchronized with the radiation beam and corrected for ionization quenching, are appealing. PURPOSE: To develop a synchronized high-speed scintillation imaging system for characterization and verification of the proton therapy beams on a pulse-by-pulse basis. MATERIALS AND METHODS: A 30 cm × 30 cm × 5 cm block of BC-408 plastic scintillator placed in a light-tight housing was irradiated by proton beams generated by a Mevion S250 proton therapy synchrocyclotron. A high-speed camera system, placed perpendicular to the beam direction and facing the scintillator, was synchronized to the accelerator's pulses to capture images. Opening and closing of the camera's shutter was controlled by setting a proper time delay and exposure time, respectively. The scintillation signal was recorded as a set of two-dimensional (2D) images. Empirical correction factors were applied to the images to correct for the nonuniformity of the pixel sensitivity and quenching of the scintillator. Proton range and modulation were obtained from the corrected images. RESULTS: The camera system was able to capture all data on a pulse-by-pulse basis at a rate of ∼504 frames per second. The applied empirical correction method for ionization quenching was effective and the corrected composite image provided a 2D map of dose distribution. The measured range (depth of distal 90%) through scintillation imaging agreed within 1.2 mm with that obtained from ionization chamber measurement. CONCLUSION: A high-speed camera system capable of capturing scintillation signals from individual proton pulses was developed. The scintillation imaging system is promising for rapid proton beam characterization and verification.

Improvement of IMRT QA prediction using imaging-based neural architecture search.

PURPOSE: Machine learning (ML) has been used to predict the gamma passing rate (GPR) of intensity-modulated radiation therapy (IMRT) QA results. In this work, we applied a novel neural architecture search to automatically tune and search for the best deep neural networks instead of using hand-designed deep learning architectures. METHOD AND MATERIALS: One hundred and eighty-two IMRT plans were created and delivered with portal dosimetry. A total of 1497 fields for multiple treatment sites were delivered and measured by portal imagers. Gamma criteria of 2%/2 mm with a 5% threshold were used. Fluence maps calculated for each plan were used as inputs to a convolution neural network (CNN). Auto-Keras was implemented to search for the best CNN architecture for fluence image regression. The network morphism was adopted in the searching process, in which the base models were ResNet and DenseNet. The performance of this CNN approach was compared with tree-based ML models previously developed for this application, using the same dataset. RESULTS: The deep-learning-based approach had 98.3% of predictions within 3% of the measured 2%/2-mm GPRs with a maximum error of 3.1% and a mean absolute error of less than 1%. Our results show that this novel architecture search approach achieves comparable performance to the machine-learning-based approaches with handcrafted features. CONCLUSIONS: We implemented a novel CNN model using imaging-based neural architecture for IMRT QA prediction. The imaging-based deep-learning method does not require a manual extraction of relevant features and is able to automatically select the best network architecture.

Determination of the electronic portal imaging device pixel-sensitivity-map for quality assurance applications. Part 1: Comparison of methods.

PURPOSE: Calibration of a radiotherapy electronic portal imaging device (EPID) using the pixel-sensitivity-map (PSM) in place of the flood field correction improves the utility of the EPID for quality assurance applications. Multiple methods are available for determining the PSM and this study provides an evaluation to inform on which is superior. METHODS: Three different empirical methods ("Calvary Mater Newcastle" [CMN], "Varian," and "WashU") and a Monte Carlo-based method of PSM determination were investigated on a single Varian TrueBeam STx linear accelerator (linac) with an aS1200 EPID panel. PSM measurements were performed for each empirical method three successive times using the 6 MV beam. The resulting PSM from each method was compared to the Monte Carlo method as a reference using 2D percentage deviation maps and histograms plus crossplane profiles. The repeatability of generated PSMs was also assessed via 2D standard deviation (SD) maps and histograms. Additionally, the Beam-Response generated by removal of the PSM from a raw EPID image for each method was visually contrasted. Finally, the practicality of each method was assessed qualitatively and via the measured time required to acquire and export the required images. RESULTS: The median pixel-by-pixel percentage deviation between each of the empirical PSM methods and the Monte Carlo PSM was -0.36%, 0.24%, and 0.74% for the CMN, Varian, and WashU methods, respectively. Ninety-five percent of pixels were found to be repeatable to within -0.21%, 0.08%, 0.19%, and 0.35% (1 SD) for the CMN, Monte Carlo, Varian, and WashU methods, respectively. The WashU method was found to be quickest for data acquisition and export and the CMN the slowest. CONCLUSION: For the first time four methods of generating the EPID PSM have been compared in detail and strengths and weaknesses of each method have been identified. All methods are considered likely to be clinically acceptable and with similar practical requirements.

Determination of the electronic portal imaging device pixel-sensitivity-map for quality assurance applications. Part 2: Photon beam dependence.

PURPOSE: The EPID PSM is a useful EPID calibration method for QA applications. The dependence of the EPID PSM on the photon beam used to acquire it has been investigated in this study for the four available PSM methods. The aim is to inform upon the viability of applying a single PSM for all available photon beams to simplify PSM implementation and maintenance. METHODS: Four methods of PSM determination were each measured once in a single session on a single TrueBeam ® STx linac using 6 MV, 10 MV, 6 MV Flattening-Filter-Free (FFF), and 10 MV FFF photon beams. The resultant PSM was assessed for both intra- and inter-method beam dependence via comparison between PSM of the same method compared to the 6 MV PSM and via comparison between PSM of the same beam with the corresponding Monte Carlo PSM. Comparisons were performed via 2D percentage deviation plots with associated histograms, 1D crossplane profiles, and via mean, median, and standard deviation percentage deviation statistics. Generated beam-response was compared qualitatively via 1D crossplane profile comparison and quantitatively via symmetry assessment with comparison to the IC profiler device. RESULTS: The Varian method provided the most consistent PSM with varying photon beam, with median percent deviation from the 6 MV PSM within 0.14% for all other beams. Qualitatively, each method provided similar beam-response profiles. The measured beam-response symmetry agreed to within 0.2% between the Calvary Mater Newcastle (CMN) method and IC profiler, but agreement reduced to within 0.9% and 2.2% for the Varian and WashU methods. PSM percent deviation with Monte Carlo PSM was within 0.75% for all methods and beams. CONCLUSION: Results suggest that the PSM may be independent of photon beam to clinically relevant levels. The Varian method of PSM determination introduces the least beam dependence into the measured PSM.

A reconstruction approach for proton computed tomography by modeling the integral depth dose of the scanning proton pencil beam.

PURPOSE: To present a proton computed tomography (pCT) reconstruction approach that models the integral depth dose (IDD) of the clinical scanning proton beam into beamlets. Using a multilayer ionization chamber (MLIC) as the imager, the proposed pCT system and the reconstruction approach can minimize extra ambient neutron dose and simplify the beamline design by eliminating an additional collimator to confine the proton beam. METHODS: Monte Carlo simulation was applied to digitally simulate the IDDs of the exiting proton beams detected by the MLIC. A forward model was developed to model each IDD into a weighted sum of percentage depth doses of the constituent beamlets separated laterally by 1 mm. The water equivalent path lengths (WEPLs) of the beamlets were determined by iteratively minimizing the squared L2-norm between the forward projected and simulated IDDs. The final WEPL values were reconstructed to pCT images, that is, proton stopping power ratio (SPR) maps, through simultaneous algebraic reconstruction technique with total variation regularization. The reconstruction process was tested with a digital cylindrical water-based phantom and an ICRP adult reference computational phantom. The mean of SPR within regions of interest (ROIs) and the WEPL along a 4 mm-wide beam ( WEP L 4 mm ${\rm{WEP}}{{\rm{L}}_{4{\rm{mm}}}}$ ) were compared with the reference values. The spatial resolution was analyzed at the edge of a cortical insert of the cylindrical phantom. RESULTS: The percentage deviations from reference SPR were within ±1% in all selected ROIs. The mean absolute error of the reconstructed SPR was 0.33%, 0.19%, and 0.27% for the cylindrical phantom, the adult phantom at the head and lung region, respectively. The corresponding percentage deviations from reference WEP L 4 mm ${\rm{WEP}}{{\rm{L}}_{4{\rm{mm}}}}$ were 0.48 ± 0.64%, 0.28 ± 0.48%, and 0.22 ± 0.49%. The full width at half maximum of the line spread function (LSF) derived from the radial edge spread function (ESF) of a cortical insert was 0.13 cm. The frequency at 10% of the modulation transfer function (MTF) was 6.38 cm-1 . The mean signal-to-noise ratio (SNR) of all the inserts was 2.45. The mean imaging dose was 0.29 and 0.25 cGy at the head and lung region of the adult phantom, respectively. CONCLUSION: A new pCT reconstruction approach was developed by modeling the IDDs of the uncollimated scanning proton beams in the pencil beam geometry. SPR accuracy within ±1%, spatial resolution of better than 2 mm at 10% MTF, and imaging dose at the magnitude of mGy were achieved. Potential side effects caused by neutron dose were eliminated by removing the extra beam collimator.

Weaving attention U-net: A novel hybrid CNN and attention-based method for organs-at-risk segmentation in head and neck CT images.

PURPOSE: In radiotherapy planning, manual contouring is labor-intensive and time-consuming. Accurate and robust automated segmentation models improve the efficiency and treatment outcome. We aim to develop a novel hybrid deep learning approach, combining convolutional neural networks (CNNs) and the self-attention mechanism, for rapid and accurate multi-organ segmentation on head and neck computed tomography (CT) images. METHODS: Head and neck CT images with manual contours of 115 patients were retrospectively collected and used. We set the training/validation/testing ratio to 81/9/25 and used the 10-fold cross-validation strategy to select the best model parameters. The proposed hybrid model segmented 10 organs-at-risk (OARs) altogether for each case. The performance of the model was evaluated by three metrics, that is, the Dice Similarity Coefficient (DSC), Hausdorff distance 95% (HD95), and mean surface distance (MSD). We also tested the performance of the model on the head and neck 2015 challenge dataset and compared it against several state-of-the-art automated segmentation algorithms. RESULTS: The proposed method generated contours that closely resemble the ground truth for 10 OARs. On the head and neck 2015 challenge dataset, the DSC scores of these OARs were 0.91 ± 0.02, 0.73 ± 0.10, 0.95 ± 0.03, 0.76 ± 0.08, 0.79 ± 0.05, 0.87 ± 0.05, 0.86 ± 0.08, 0.87 ± 0.03, and 0.87 ± 0.07 for brain stem, chiasm, mandible, left/right optic nerve, left/right submandibular, and left/right parotid, respectively. Our results of the new weaving attention U-net (WAU-net) demonstrate superior or similar performance on the segmentation of head and neck CT images. CONCLUSIONS: We developed a deep learning approach that integrates the merits of CNNs and the self-attention mechanism. The proposed WAU-net can efficiently capture local and global dependencies and achieves state-of-the-art performance on the head and neck multi-organ segmentation task.

Semi-supervised semantic segmentation of prostate and organs-at-risk on 3D pelvic CT images.

The recent development of deep learning approaches has revoluted medical data processing, including semantic segmentation, by dramatically improving performance. Automated segmentation can assist radiotherapy treatment planning by saving manual contouring efforts and reducing intra-observer and inter-observer variations. However, training effective deep learning models usually Requires a large amount of high-quality labeled data, often costly to collect. We developed a novel semi-supervised adversarial deep learning approach for 3D pelvic CT image semantic segmentation. Unlike supervised deep learning methods, the new approach can utilize both annotated and un-annotated data for training. It generates un-annotated synthetic data by a data augmentation scheme using generative adversarial networks (GANs). We applied the new approach to segmenting multiple organs in male pelvic CT images. CT images without annotations and GAN-synthesized un-annotated images were used in semi-supervised learning. Experimental results, evaluated by three metrics (Dice similarity coefficient, average Hausdorff distance, and average surface Hausdorff distance), showed that the new method achieved comparable performance with substantially fewer annotated images or better performance with the same amount of annotated data, outperforming the existing state-of-the-art methods.

Application programming interface guided QA plan generation and analysis automation.

PURPOSE: Linear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standardized QA plan generation and analysis system in the Oncology Information System (OIS) to streamline the QA process. METHODS: Automating the QA process includes two software components: the AutoQA Builder to generate daily, monthly, quarterly, and miscellaneous periodic linear accelerator QA plans within the Treatment Planning System (TPS) and the AutoQA Analysis to analyze images collected on the Electronic Portal Imaging Device (EPID) allowing for a rapid analysis of the acquired QA images. To verify the results of the automated QA analysis, results were compared to the current standard for QA assessment for the jaw junction, light-radiation coincidence, picket fence, and volumetric modulated arc therapy (VMAT) QA plans across three linacs and over a 6-month period. RESULTS: The AutoQA Builder application has been utilized clinically 322 times to create QA patients, construct phantom images, and deploy common periodic QA tests across multiple institutions, linear accelerators, and physicists. Comparing the AutoQA Analysis results with our current institutional QA standard the mean difference of the ratio of intensity values within the field-matched junction and ball-bearing position detection was 0.012 ± 0.053 (P = 0.159) and is 0.011 ± 0.224 mm (P = 0.355), respectively. Analysis of VMAT QA plans resulted in a maximum percentage difference of 0.3%. CONCLUSION: The automated creation and analysis of quality assurance plans using multiple APIs can be of immediate benefit to linear accelerator quality assurance efficiency and standardization. QA plan creation can be done without following tedious procedures through API assistance, and analysis can be performed inside of the clinical OIS in an automated fashion.

Dosimetric impact of range uncertainty in passive scattering proton therapy.

PURPOSE: The objective of this study was to investigate the dosimetric impact of range uncertainty in a large cohort of patients receiving passive scatter proton therapy. METHODS: A cohort of 120 patients were reviewed in this study retrospectively, of which 61 were brain, 39 lung, and 20 prostate patients. Range uncertainties of ±3.5% (overshooting and undershooting by 3.5%, respectively) were added and recalculated on the original plans, which had been planned according to our clinical planning protocol while keeping beamlines, apertures, compensators, and dose grids intact. Changes in the coverage on CTV and DVH for critical organs were compared and analyzed. Correlation between dose change and minimal distance between CTV and critical organs were also investigated. RESULTS: Although CTV coverages and maximum dose to critical organs were largely maintained for most brain patients, large variations over 5% were still observed sporadically. Critical organs, such as brainstem and chiasm, could still be affected by range uncertainty at 4 cm away from CTV. Coverage and OARs in lung and prostate patients were less likely to be affected by range uncertainty with very few exceptions. CONCLUSION: The margin recipe in modern TPS leads to clinically acceptable OAR doses in the presence of range uncertainties. However, range uncertainties still pose a noticeable challenge for small but critical serial organs near tumors, and occasionally for large parallel organs that are located distal to incident proton beams.

Technical Note: An alternative approach to verify 6FFF beam dosimetry for Ethos and MR Linac without using a 3D water tank.

PURPOSE: The current approach to Linac beam dosimetry verification is typically performed utilizing a three-dimensional (3D) water tank system. The 3D beam scanning process is cumbersome, labor intensive, error-prone, and costly. This is especially challenging for the new Ethos system and MR Linacs with a ring gantry. This work proposes an alternative approach to verify 6FFF beam dosimetry for Ethos, ViewRay MRIdian® Linac, and other Linacs with 6FFF beam quality using two-dimensional (2D) ion chamber arrays. METHODS: Percentage depth dose (PDD) and profiles of an Ethos, an MRIdian® Linac, and several Linacs with 6FFF beams were measured at the nominal beam current. The beam energy was detuned by changing the bending magnet current on one TrueBeam. PDDs and profiles were measured for detuned beam energies. The peak shape of the 6FFF profile was defined by a "slope" parameter and unflatness. Correlations between peak slope and unflatness metrics vs PDDs were used to evaluate the sensitivity of beam energy to beam profile changes at different field sizes and depths. RESULTS: Strong correlations were found between peak slope and PDDs for all Linacs with 6FFF beam. The R-squared values in the linear regression fitting between PDD and peak slope and unflatness were 0.99 and 0.84, respectively. Both profile slope and unflatness were proportional to PDD at the 10 cm depth and the peak slope was 4.3 times more sensitive than PDD. We have identified that measurements with a shallow depth are preferred to quantify the beam energy consistency. CONCLUSIONS: Our work shows the feasibility of verifying 6FFF beam quality of Ethos, MR Linac, and other Linacs by defining a profile slope measured from 2D ionization chambers array devices. This new approach provides a simplified method for performing a routine beam quality check without using a 3D water tank system while maximizing cost effectiveness and efficiency.

ARPM-net: A novel CNN-based adversarial method with Markov random field enhancement for prostate and organs at risk segmentation in pelvic CT images.

PURPOSE: The research is to develop a novel CNN-based adversarial deep learning method to improve and expedite the multi-organ semantic segmentation of CT images and to generate accurate contours on pelvic CT images. METHODS: Planning CT and structure datasets for 120 patients with intact prostate cancer were retrospectively selected and divided for tenfold cross-validation. The proposed adversarial multi-residual multi-scale pooling Markov random field (MRF) enhanced network (ARPM-net) implements an adversarial training scheme. A segmentation network and a discriminator network were trained jointly, and only the segmentation network was used for prediction. The segmentation network integrates a newly designed MRF block into a variation of multi-residual U-net. The discriminator takes the product of the original CT and the prediction/ground-truth as input and classifies the input into fake/real. The segmentation network and discriminator network can be trained jointly as a whole, or the discriminator can be used for fine-tuning after the segmentation network is coarsely trained. Multi-scale pooling layers were introduced to preserve spatial resolution during pooling using less memory compared to atrous convolution layers. An adaptive loss function was proposed to enhance the training on small or low contrast organs. The accuracy of modeled contours was measured with the dice similarity coefficient (DSC), average Hausdorff distance (AHD), average surface Hausdorff distance (ASHD), and relative volume difference (VD) using clinical contours as references to the ground-truth. The proposed ARPM-net method was compared to several state-of-the-art deep learning methods. RESULTS: ARPM-net outperformed several existing deep learning approaches and MRF methods and achieved state-of-the-art performance on a testing dataset. On the test set with 20 cases, the average DSC on the prostate, bladder, rectum, left femur, and right femur were 0.88 ( ± 0.11), 0.97 ( ± 0.07), 0.86 ( ± 0.12), 0.97 ( ± 0.01), and 0.97 ( ± 0.01), respectively. The average HD (mm) on these organs were 1.58 ( ± 1.77), 1.91 ( ± 1.29), 3.14 ( ± 2.39), 1.76 ( ± 1.57), and 1.92 ( ± 1.01). The average surface HD (mm) on these organs are 2.11 ( ± 2.03), 2.36 ( ± 2.43), 3.05 ( ± 2.11), 1.99 ( ± 1.66), and 2.00 ( ± 2.07). CONCLUSION: ARPM-net was designed for the automatic segmentation of pelvic CT images. With adversarial fine-tuning, ARPM-net produces state-of-the-art accurate contouring of multiple organs on CT images and has the potential to facilitate routine pelvic cancer radiation therapy planning process.

A Monte Carlo based analytic model of the in-room neutron ambient dose equivalent for a Mevion gantry-mounted passively scattered proton system.

The goal of this study was to develop a Monte Carlo (MC)-based analytical model that can predict the in-room ambient dose equivalent from a Mevion gantry-mounted passively scattered proton system. The Mevion S250 and treatment vault were simulated using the MCNPX MC code. The results of the in-room neutron dose measurements, using an FHT 762 WENDI-II detector, were employed to benchmark the MC-derived values. After tuning the MCNPX MC code, for the same beam delivery parameters, the code was used to calculate the neutron spectra and ambient dose equivalent in the vault and at varying angles from the isocenter. Then, based on the calculations, an analytical model was reconstructed and data were fitted to derive the model parameters at 95% confidence intervals (CI). The MCNPX codes were tuned to within about 19% of the measured values for most of the measurements in the vault. For the maze, up to 0.08 mSv Gy-1 discrepancies were found between the experimental measurements and MCNPX calculated results. The analytical model showed up to 18% discrepancy for distances between 100 and 600 cm from the isocenter compared to the MC calculations. The model may underestimate the neutron ambient dose equivalent up to 21% for distances less than 100 cm from the isocenter. The proposed analytical model can be used to estimate the contribution of the secondary neutron dose from the Mevion S250 for the design of local shielding inside the proton therapy treatment vault.

A Monte Carlo-based analytic model of neutron dose equivalent for a mevion gantry-mounted passively scattered proton system for craniospinal irradiation.

PURPOSE: To calculate in- and out-of-field neutron spectra and dose equivalent, using Monte Carlo (MC) simulation, for a Mevion gantry-mounted passively scattered proton system in craniospinal irradiation. An analytical model based on the MC calculations that estimates in- and out-of-field neutron dose equivalent from proton Craniospinal irradiation (CSI) was also developed. METHODS: The MCNPX MC code was used to simulate a Mevion S250 proton therapy system. The simulated proton depth doses and profiles for pristine and spread-out Bragg peaks were benchmarked against the measured data. Previous measurements using extended-range Bonner spheres were used to verify the calculated neutron spectra and dose equivalent. Using the benchmarked results as a reference condition, a correction-based analytical model was reconstructed by fitting the data to derive model parameters at 95% confidence interval. Sensitivity analysis of brass aperture opening, thickness of the Lucite (PMMA) range compensator, and modulation width was performed to obtain correction parameters for nonreference conditions. RESULTS: For the neutron dose equivalent per therapeutic proton dose, the MCNPX calculated dose equivalent matched the measured values to within 8%. The benchmarked neutron dose equivalent at the isocenter was 41.2 and 20.8 mSv/Gy, for cranial and spinal fields, respectively. For in- and out-of-field neutron dose calculations, the correction-based analytical model showed up to 17% discrepancy compared to the MC calculations. The correction factors may provide a conservative estimation of neutron dose, especially for depth ≤ 5 cm and regions underneath the brass aperture. CONCLUSION: The proposed analytical model can be used to estimate the contribution of the neutron dose to the overall CSI treatment dose. Moreover, the model can be employed to estimate the neutron dose to the implantable cardiac electronic devices.

Feasibility of proton FLASH irradiation using a synchrocyclotron for preclinical studies.

PURPOSE: It has been recently shown that radiotherapy at ultrahigh dose rates (>40 Gy/s, FLASH) has a potential advantage in sparing healthy organs compared to that at conventional dose rates. The purpose of this work is to show the feasibility of proton FLASH irradiation using a gantry-mounted synchrocyclotron as a first step toward implementing an experimental setup for preclinical studies. METHODS: A clinical Mevion HYPERSCAN® synchrocyclotron was modified to deliver ultrahigh dose rates. Pulse widths of protons with 230 MeV energy were manipulated from 1 to 20 µs to deliver in conventional and ultrahigh dose rate. A boron carbide absorber was placed in the beam for range modulation. A Faraday cup was used to determine the number of protons per pulse at various dose rates. Dose rate was determined by the dose measured with a plane-parallel ionization chamber with respect to the actual delivery time. The integral depth dose (IDD) was measured with a Bragg ionization chamber. Monte Carlo simulation was performed in TOPAS as the secondary check for the measurements. RESULTS: Maximum protons charge per pulse, measured with the Faraday cup, was 54.6 pC at 20 µs pulse width. The measured IDD agreed well with the Monte Carlo simulation. The average dose rate measured using the ionization chamber showed 101 Gy/s at the entrance and 216 Gy/s at the Bragg peak with a full width at half maximum field size of 1.2 cm. CONCLUSIONS: It is feasible to deliver protons at 100 and 200 Gy/s average dose rate at the plateau and the Bragg peak, respectively, in a small ~1 cm2 field using a gantry-mounted synchrocyclotron.

Sensitivity analysis of Monte Carlo model of a gantry-mounted passively scattered proton system.

PURPOSE: This study aimed to present guidance on the correlation between treatment nozzle and proton source parameters, and dose distribution of a passive double scattering compact proton therapy unit, known as Mevion S250. METHODS: All 24 beam options were modeled using the MCNPX MC code. The calculated physical dose for pristine peak, profiles, and spread out Bragg peak (SOBP) were benchmarked with the measured data. Track-averaged LET (LETt ) and dose-averaged LET (LETd ) distributions were also calculated. For the sensitivity investigations, proton beam line parameters including Average Energy (AE), Energy Spread (ES), Spot Size (SS), Beam Angle (BA), Beam Offset (OA), and Second scatter Offset (SO) from central Axis, and also First Scatter (FS) thickness were simulated in different stages to obtain the uncertainty of the derived results on the physical dose and LET distribution in a water phantom. RESULTS: For the physical dose distribution, the MCNPX MC model matched measurements data for all the options to within 2 mm and 2% criterion. The Mevion S250 was found to have a LETt between 0.46 and 8.76 keV.µm-1 and a corresponding LETd between 0.84 and 15.91 keV.µm-1 . For all the options, the AE and ES had the greatest effect on the resulting depth of pristine peak and peak-to-plateau ratio respectively. BA, OA, and SO significantly decreased the flatness and symmetry of the profiles. The LETs were found to be sensitive to the AE, ES, and SS, especially in the peak region. CONCLUSIONS: This study revealed the importance of considering detailed beam parameters, and identifying those that resulted in large effects on the physical dose distribution and LETs for a compact proton therapy machine.