Resolving signal drift in the wall-mounted camera of the RGSC system.

PURPOSE: To present a modified calibration method to reduce signal drift due to table sagging in Respiratory Gating for Scanner (RGSC) systems with a wall-mounted camera. MATERIALS AND METHODS: Approximately 70 kg of solid water phantoms were evenly distributed on the CT couch, mimicking the patient's weight. New calibration measurements were performed at 9 points at the combination of three lateral positions, the CT isocenter and ±10 cm laterally from the isocenter, and three longitudinal locations, the CT isocenter and ±30 cm or ±40 cm from the isocenter. The new calibration was tested in two hospitals. RESULTS: Implementing the new weighed calibration method at the extended distance yielded improved results during the DIBH scan, reducing the drift to within 1 from 3 mm. The extended calibration positions exhibited similarly reduced drift in both hospitals, reinforcing the method's robustness and its potential applicability across all centers. CONCLUSION: This proposed solution aims to minimize the systematic error in radiation delivery for patients undergoing motion management with wall-mounted camera RGSC systems, especially in conjunction with a bariatric CT couchtop.

Technical note: Energy dependence of the Gafchromic EBT4 film: Dose-response curves for 70 kV, 6 MV, 6 MV FFF, 10 MV FFF, and 15 MV x-ray beams.

BACKGROUND: EBT4 was newly released for radiotherapy quality assurance to improve the signal-to-noise ratio in radiochromic film dosimetry. It is important to know its dose-response characteristics before its use in the clinic. PURPOSE: This study aims to investigate and compare the dose-response curves of the Gafchromic EBT4 film for megavoltage and kilovoltage x-ray beams with different dose levels, scanning spatial resolutions, and sizes of region of interest (ROI). METHODS: EBT4 film (Lot#07052201) calibration strips (3.5 × 20 cm2 ) were exposed to a 10×10 cm2 open field at doses of 0, 63, 125, 500, 750, 1000 cGy using 6 MV photon beam. EBT4 film strips from the same lot were then exposed to each x-ray beam (6 MV, 6 MV FFF, 10 MV FFF, 15 MV, and 70 kV) at six dose values (50, 100, 300, 600, 800, 1000 cGy). A full sheet (25 × 20 cm2 ) of EBT4 film was irradiated at each energy with 300 cGy for profile comparison with the treatment planning calculation. At two different spatial resolutions of 72 and 300 dpi, each film piece was scanned three consecutive times in the center of an Epson 10000XL flatbed scanner in 48-bit color. The scanned images were analyzed using FilmQA Pro. For each scanned image, an ROI of 2 × 2 cm2 at the field center was selected to obtain the average pixel value with its standard deviation in the ROI. An additional ROI of 1 cm diameter circle was also used to evaluate the impact of ROI shape and size, especially for FFF beams. The dose value, average dose-response value, and associated uncertainty were determined for each energy and relative responses were analyzed. The Student's t-test was performed to evaluate the statistical significance of the dose-response values with different color channels, ROI shapes, and spatial resolutions. RESULTS: The dose-response curves for the five x-ray energies were compared in three color channels. Weak energy dependence was found among the megavoltage beams. No significant differences (average ∼1.1%) were observed for all doses in this study among 6 MV, 6 MV FFF, 10 MV FFF, and 15 MV beams, regardless of spatial resolution and color channel. However, a statistically significant difference in dose-response was observed up to 12% between 70 kV and 6 MV beams. CONCLUSIONS: The dose-response curves for Gafchromic EBT4 films were nearly independent of the energy of the photon beams among 6 MV, 6 MV FFF, 10 MV FFF, and 15 MV. For very low-energy photons (e.g., 70 kV), a separate calibration from the same low-energy x-ray is necessary.

Comparative study of SRS end-to-end QA processes of a diode array device and an anthropomorphic phantom loaded with GafChromic XD film.

PURPOSE: End-to-end testing (E2E) is a necessary process for assessing the readiness of the stereotactic radiosurgery (SRS) program and annual QA of an SRS system according to the AAPM MPPG 9a. This study investigates the differences between using a new SRS MapCHECK (SRSMC) system and an anthropomorphic phantom film-based system in a large network with different SRS delivery techniques. METHODS AND MATERIALS: Three SRS capable Linacs (Varian Medical Systems, Palo Alto, CA) at three different regional sites were chosen to represent a hospital network, a Trilogy with an M120 multi-leaf collimator (MLC), a TrueBeam with an M120 MLC, and a TrueBeam Stx with an HD120 MLC. An anthropomorphic STEEV phantom (CIRS, Norfolk, VA) and a phantom/diode array: StereoPHAN/SRSMC (Sun Nuclear, Melbourne, FL) were CT scanned at each site. The new STV-PHANTOM EBT-XD films (Ashland, Bridgewater, NJ) were used. Six plans with various complexities were measured with both films and SRSMC in the StereoPHAN to establish their dosimetric correlations. Three SRS cranial plans with a total of sixteen fields using dynamic conformal arc and volumetric-modulated arc therapy, with 1-4 targets, were planned with Eclipse v15.5 treatment planning system (TPS) using a custom SRS beam model for each machine. The dosimetric and localization accuracy were compared. The time of analysis for the two systems by three teams of physicists was also compared to assess the throughput efficiency. RESULTS: The correlations between films and SRSMC were found to be 0.84 (p = 0.03) and 0.16 (p = 0.76) for γ (3%, 1 mm) and γ (3%, 2 mm), respectively. With film, the local dose differences (ΔD) relative to the average dose within the 50% isodose line from the three sites were found to be -3.2%-3.7%. The maximum localization errors (Elocal ) were found to be within 0.5 ± 0.2 mm. With SRSMC, the ΔD was found to be within 5% of the TPS calculation. Elocal were found to be within 0.7 to 1.1 ± 0.4 mm for TrueBeam and Trilogy, respectively. Comparing with film, an additional uncertainty of 0.7 mm was found with SRSMC. The delivery and analysis times were found to be 6 and 2 h for film and SRSMC, respectively. CONCLUSIONS: The SRS MapCHECK agrees dosimetrically with the films within measurement uncertainties. However, film dosimetry shows superior sub-millimeter localization resolving power for the MPPG 9a implementation.

A method for in vivo treatment verification of IMRT and VMAT based on electronic portal imaging device.

BACKGROUND: Intensity-modulated radiation therapy (IMRT) and volume-modulated arc therapy (VMAT) are rather complex treatment techniques and require patient-specific quality assurance procedures. Electronic portal imaging devices (EPID) are increasingly used in the verification of radiation therapy (RT). This work aims to develop a novel model to predict the EPID transmission image (TI) with fluence maps from the RT plan. The predicted TI is compared with the measured TI for in vivo treatment verification. METHODS: The fluence map was extracted from the RT plan and corrections of penumbra, response, global field output, attenuation, and scatter were applied before the TI was calculated. The parameters used in the model were calculated separately for central axis and off-axis points using a series of EPID measurement data. Our model was evaluated using a CIRS thorax phantom and 20 clinical plans (10 IMRT and 10 VMAT) optimized for head and neck, breast, and rectum treatments. RESULTS: Comparisons of the predicted and measured images were carried out using a global gamma analysis of 3%/2 mm (10% threshold) to validate the accuracy of the model. The gamma pass rates for IMRT and VMAT were greater than 97.2% and 94.5% at 3%/2 mm, respectively. CONCLUSION: We have developed an accurate and straightforward EPID-based quality assurance model that can potentially be used for in vivo treatment verification of the IMRT and VMAT delivery.

A Review and Analysis of Managing Commonly Seen Implanted Devices for Patients Undergoing Radiation Therapy.

PURPOSE: This review article aims to consolidate information regarding existing and emerging implanted devices used in patients undergoing radiation therapy and to categorize levels of attention needed for each device, including which devices require monitoring throughout treatment. METHODS AND MATERIALS: Based on the collective information from scholar searches, manufacturers' technical reports, and institutional experiences in the past years, commonly present devices in patients with cancer are compiled. This work summarizes cardiac pacemaker, implanted cardiac defibrillator, hepatic pump, intrathecal pain pump, neurostimulator, shunt, loop recorder, and mediport. Three different classifications of implanted devices can be made based on the potential effect of radiation: life-dependent, nonlife-dependent but with adverse effects if overdosed, and devices without electronic circuits. Implanted devices that contain electronic circuits that would be life-dependent or have adverse effects if overdosed, include cardiac pacemakers, implanted cardiac defibrillators, programmable hepatic pumps, pain pumps, neurostimulators, and loop recorders. RESULTS: Dose exposure to these devices need to be calculated or measured in vivo, especially for cardiac implanted devices, and they should be minimized to assure continued healthy functioning. Treatment planning techniques should be chosen to reduce entry, exit and internal scatter dose. Lower energy photon beams should be used to decrease potential neutron contamination. Implanted devices without electronic circuits are less of a concern. If a patient is life-dependent on the implanted device, it is not recommended to treat the patient with proton therapy. CONCLUSIONS: This study reviewed the management of patients with commonly seen implanted devices and summarized a workflow for identifying and planning when a patient has implanted devices. Classifications of implanted devices could help clinicians make proper decisions in regard to patients with specific implanted devices. Lastly, the management of such devices in the era of the pandemic is also discussed in this review article.

Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario.

BACKGROUND AND PURPOSE: To commission and implement an Autoencoder based Classification-Regression (ACLR) model for VMAT patient-specific quality assurance (PSQA) in a multi-institution scenario. MATERIALS AND METHODS: 1835 VMAT plans from seven institutions were collected for the ACLR model commissioning and multi-institutional validation. We established three scenarios to validate the gamma passing rates (GPRs) prediction and classification accuracy with the ACLR model for different delivery equipment, QA devices, and treatment planning systems (TPS). The prediction performance of the ACLR model was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The classification performance was evaluated using sensitivity and specificity. An independent end-to-end test (E2E) and routine QA of the ACLR model were performed to validate the clinical use of the model. RESULTS: For multi-institution validations, the MAEs were 1.30-2.80% and 2.42-4.60% at 3%/3 mm and 3%/2 mm, respectively, and RMSEs were 1.55-2.98% and 2.83-4.95% at 3%/3 mm and 3%/2 mm, respectively, with different delivery equipment, QA devices, and TPS, while the sensitivity was 90% and specificity was 70.1% at 3%/2 mm. For the E2E, the deviations between the predicted and measured results were within 3%, and the model passed the consistency check for clinical implementation. The predicted results of the model were the same in daily QA, while the deviations between the repeated monthly measured GPRs were all within 2%. CONCLUSIONS: The performance of the ACLR model in multi-institution scenarios was validated on a large scale. Routine QA of the ACLR model was established and the model could be used for VMAT PSQA clinically.

Commissioning of optical surface imaging systems for cranial frameless stereotactic radiosurgery.

PURPOSE: This study aimed to evaluate and compare different system calibration methods from a large cohort of systems to establish a commissioning procedure for surface-guided frameless cranial stereotactic radiosurgery (SRS) with intrafractional motion monitoring and gating. Using optical surface imaging (OSI) to guide non-coplanar SRS treatments, the determination of OSI couch-angle dependency, baseline drift, and gated-delivered-dose equivalency are essential. METHODS: Eleven trained physicists evaluated 17 OSI systems at nine clinical centers within our institution. Three calibration methods were examined, including 1-level (2D), 2-level plate (3D) calibration for both surface image reconstruction and isocenter determination, and cube phantom calibration to assess OSI-megavoltage (MV) isocenter concordance. After each calibration, a couch-angle dependency error was measured as the maximum registration error within the couch rotation range. A head phantom was immobilized on the treatment couch and the isocenter was set in the middle of the brain, marked with the room lasers. An on-site reference image was acquired at couch zero, the facial region of interest (ROI) was defined, and static verification images were captured every 10° for 0°-90° and 360°-270°. The baseline drift was assessed with real-time monitoring of the motionless phantom over 20 min. The gated-delivered-dose equivalency was assessed using the electron portal imaging device and gamma test (1%/1mm) in reference to non-gated delivery. RESULTS: The maximum couch-angle dependency error occurs in longitudinal and lateral directions and is reduced significantly (P < 0.05) from 1-level (1.3 ± 0.4 mm) to 2-level (0.8 ± 0.3 mm) calibration. The MV cube calibration does not further reduce the couch-angle dependency error (0.8 ± 0.2 mm) on average. The baseline drift error plateaus at 0.3 ± 0.1 mm after 10 min. The gated-delivered-dose equivalency has a >98% gamma-test passing rate. CONCLUSION: A commissioning method is recommended using the 3D plate calibration, which is verified by radiation isocenter and validated with couch-angle dependency, baseline drift, and gated-delivered-dose equivalency tests. This method characterizes OSI uncertainties, ensuring motion-monitoring accuracy for SRS treatments.

Multi-task autoencoder based classification-regression model for patient-specific VMAT QA.

Patient-specific quality assurance (PSQA) of volumetric modulated arc therapy (VMAT) to assure accurate treatment delivery is resource-intensive and time-consuming. Recently, machine learning has been increasingly investigated in PSQA results prediction. However, the classification performance of models at different criteria needs further improvement and clinical validation (CV), especially for predicting plans with low gamma passing rates (GPRs). In this study, we developed and validated a novel multi-task model called autoencoder based classification-regression (ACLR) for VMAT PSQA. The classification and regression were integrated into one model, both parts were trained alternatively while minimizing a defined loss function. The classification was used as an intermediate result to improve the regression accuracy. Different tasks of GPRs prediction and classification based on different criteria were trained simultaneously. Balanced sampling techniques were used to improve the prediction accuracy and classification sensitivity for the unbalanced VMAT plans. Fifty-four metrics were selected as inputs to describe the plan modulation-complexity and delivery-characteristics, while the outputs were PSQA GPRs. A total of 426 clinically delivered VMAT plans were used for technical validation (TV), and another 150 VMAT plans were used for CV to evaluate the generalization performance of the model. The ACLR performance was compared with the Poisson Lasso (PL) model and found significant improvement in prediction accuracy. In TV, the absolute prediction error (APE) of ACLR was 1.76%, 2.60%, and 4.66% at 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively; whereas the APE of PL was 2.10%, 3.04%, and 5.29% at 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. No significant difference was found between CV and TV in prediction accuracy. ACLR model set with 3%/3 mm can achieve 100% sensitivity and 83% specificity. The ACLR model could classify the unbalanced VMAT QA results accurately, and it can be readily applied in clinical practice for virtual VMAT QA.

Report of AAPM Task Group 235 Radiochromic Film Dosimetry: An Update to TG-55.

The use of radiochromic film (RCF) dosimetry in radiation therapy is extensive due to its high level of achievable accuracy for a wide range of dose values and its suitability under a variety of measurement conditions. However, since the publication of the 1998 AAPM Task Group 55, Report No. 63 on RCF dosimetry, the chemistry, composition, and readout systems for RCFs have evolved steadily. There are several challenges in using the new RCFs, readout systems and validation of the results depending on their applications. Accurate RCF dosimetry requires understanding of RCF selection, handling and calibration methods, calibration curves, dose conversion methods, correction methodologies as well as selection, operation and quality assurance (QA) programs of the readout systems. Acquiring this level of knowledge is not straight forward, even for some experienced users. This Task Group report addresses these issues and provides a basic understanding of available RCF models, dosimetric characteristics and properties, advantages and limitations, configurations, and overall elemental compositions of the RCFs that have changed over the past 20 yr. In addition, this report provides specific guidelines for data processing and analysis schemes and correction methodologies for clinical applications in radiation therapy.

Integration of AI and Machine Learning in Radiotherapy QA.

The use of machine learning and other sophisticated models to aid in prediction and decision making has become widely popular across a breadth of disciplines. Within the greater diagnostic radiology, radiation oncology, and medical physics communities promising work is being performed in tissue classification and cancer staging, outcome prediction, automated segmentation, treatment planning, and quality assurance as well as other areas. In this article, machine learning approaches are explored, highlighting specific applications in machine and patient-specific quality assurance (QA). Machine learning can analyze multiple elements of a delivery system on its performance over time including the multileaf collimator (MLC), imaging system, mechanical and dosimetric parameters. Virtual Intensity-Modulated Radiation Therapy (IMRT) QA can predict passing rates using different measurement techniques, different treatment planning systems, and different treatment delivery machines across multiple institutions. Prediction of QA passing rates and other metrics can have profound implications on the current IMRT process. Here we cover general concepts of machine learning in dosimetry and various methods used in virtual IMRT QA, as well as their clinical applications.

Commissioning and Evaluation of a Third-Party 6 Degrees-of-Freedom Couch Used in Radiotherapy.

PURPOSES: The newly released Protura 6 degrees-of-freedom couch (CIVCO) has limited quality assurance protocols and pertinent publications. Herein, we report our experiences of the Protura system acceptance, commissioning, and quality assurance. METHODS: The Protura system integration was tested with peripheral equipment on the following items: couch movement range limit, 6 degrees-of-freedom movement accuracy, weight test and couch sagging, system connection with Linac, isocentricity of couch and rotation alignment, kV and cone-beam computed tomography imaging of HexaCHECK with MIMI phantom (Standard Imaging), and an in-house custom 6 degrees-of-freedom quality assurance phantom. A couch transmission measurement was also performed. RESULTS: The vertical, longitudinal, and lateral ranges of the 6 degrees-of-freedom couch pedestal are 43.9 to 0.0 cm, 24.6 to 149.5 cm, -20.6 to 20.7 cm, respectively. The couch movement accuracy was within 1 mm in all directions. The couch sagging with a 200 lbs (∼91 kg) evenly distributed object is 1.0 cm and 0.4° pitch in the distal end of the couch. The isocentricity of the couch was about 0.5 mm in diameter of all crosshair projections on the couch isocenter level, and the largest couch rotation alignment observed was (0.3°) at the couch angle of 90°. The deviation from the reference position (zero position) of the HexaCHECK phantom, measured by matching the cone-beam computed tomography with the reference planning computed tomography, was found to be below 0.2 mm in the anterior-posterior and right-left dimensions, 0.4 mm in superior-inferior dimension, and 0.1° in roll, pitch, and yaw directions. CONCLUSIONS: A 6 degrees-of-freedom quality assurance phantom is helpful for the commissioning and routine quality assurance tests. Due to the third-party integration with Linac, the system is prone to "double-correction" errors. A rigorous quality assurance program is the key to a successful clinical implementation of the Protura system.

Machine Learning for Patient-Specific Quality Assurance of VMAT: Prediction and Classification Accuracy.

PURPOSE: To assess the accuracy of machine learning to predict and classify quality assurance (QA) results for volumetric modulated arc therapy (VMAT) plans. METHODS AND MATERIALS: Three hundred three VMAT plans, including 176 gynecologic cancer and 127 head and neck cancer plans, were chosen in this study. Fifty-four complexity metrics were extracted from the QA plans and considered as inputs. Patient-specific QA was performed, and gamma passing rates (GPRs) were used as outputs. One Poisson lasso (PL) regression model was developed, aiming to predict individual GPR, and 1 random forest (RF) classification model was developed to classify QA results as "pass" or "fail." Both technical validation (TV) and clinical validation (CV) were used to evaluate the model reliability. GPR prediction accuracy of PL and classification performance of PL and RF were evaluated. RESULTS: In TV, the mean prediction error of PL was 1.81%, 2.39%, and 4.18% at 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. No significant differences in prediction errors between TV and CV were observed. In QA results classification, PL had a higher specificity (accurately identifying plans that can pass QA), whereas RF had a higher sensitivity (accurately identifying plans that may fail QA). By using 90% as the action limit at a 3%/2 mm criterion, the specificity of PL and RF was 97.5% and 87.7% in TV and 100% and 71.4% in CV, respectively. The sensitivity of PL and RF was 31.6% and 100% in TV and 33.3% and 100% in CV, respectively. With 100% sensitivity, the QA workload of 81.2% of plans in TV and 62.5% of plans in CV could be reduced by RF. CONCLUSIONS: The PL model could accurately predict GPR for most VMAT plans. The RF model with 100% sensitivity was preferred for QA results classification. Machine learning can be a useful tool to assist VMAT QA and reduce QA workload.

Impact of delivery characteristics on dose delivery accuracy of volumetric modulated arc therapy for different treatment sites.

This study aimed to investigate the impact of delivery characteristics on the dose delivery accuracy of volumetric modulated arc therapy (VMAT) for different treatment sites. The pretreatment quality assurance (QA) results of 344 VMAT patients diagnosed with gynecological (GYN), head and neck (H&N), rectal or prostate cancer were randomly chosen in this study. Ten metrics reflecting VMAT delivery characteristics were extracted from the QA plans. Compared with GYN and rectal plans, H&N and prostate plans had higher aperture complexity and monitor units (MU), and smaller aperture area. Prostate plans had the smallest aperture area and lowest leaf speed compared with other plans (P < 0.001). No differences in gantry speed were found among the four sites. The gamma passing rates (GPRs) of GYN, rectal and H&N plans were inversely associated with union aperture area (UAA) and leaf speed (Pearson's r: -0.39 to -0.68). GPRs of prostate plans were inversely correlated with aperture complexity, MU and small aperture score (SAS) (absolute Pearson's r: 0.34 to 0.49). Significant differences in GPR between high SAS and low SAS subgroups were found only when leaf speed was <0.42 cm s-1 (P < 0.001). No association of GPR with gantry speed was found in four sites. Leaf speed was more strongly associated with UAA. Aperture complexity and MU were more strongly associated with SAS. VMAT plans from different sites have distinct delivery characteristics. Affecting dose delivery accuracy, leaf speed is the key factor for GYN, rectal and H&N plans, while aperture complexity, MU and small apertures have a higher influence on prostate plans.

Towards real-time respiratory motion prediction based on long short-term memory neural networks.

Radiation therapy of thoracic and abdominal tumors requires incorporating the respiratory motion into treatments. To precisely account for the patient's respiratory motions and predict the respiratory signals, a generalized model for predictions of different types of patients' respiratory motions is desired. The aim of this study is to explore the feasibility of developing a long short-term memory (LSTM)-based generalized model for the respiratory signal prediction. To achieve that, 1703 sets of real-time position management (RPM) data were collected from retrospective studies across three clinical institutions. These datasets were separated as the training, internal validity and external validity groups. Among all the datasets, 1187 datasets were used for model development and the remaining 516 datasets were used to test the model's generality power. Furthermore, an exhaustive grid search was implemented to find the optimal hyper-parameters of the LSTM model. The hyper-parameters are the number of LSTM layers, the number of hidden units, the optimizer, the learning rate, the number of epochs, and the length of time lags. The obtained model achieved superior accuracy over conventional artificial neural network (ANN) models: with the prediction window equaling to 500 ms, the LSTM model achieved an average relative mean absolute error (MAE) of 0.037, an average root mean square error (RMSE) of 0.048, and a maximum error (ME) of 1.687 in the internal validity data, and an average relative MAE of 0.112, an average RMSE of 0.139 and an ME of 1.811 in the external validity data. Compared to the LSTM model trained with default hyper-parameters, the MAE of the optimized model results decreased by 20%, indicating the importance of tuning the hyper-parameters of LSTM models to obtain superior accuracies. This study demonstrates the potential of deep LSTM models for the respiratory signal prediction and illustrates the impacts of major hyper-parameters in LSTM models.

Monte Carlo calculation of the mass stopping power of EBT3 and EBT-XD films for protons for energy ranges of 50-400 MeV.

OBJECTIVE: The goal of the present study was to calculate the continuous slowing down approximation (CDSA) ranges and derive mass stopping power for EBT3 and EBT-XD films for therapeutic protons energy ranges of 50-400 MeV. METHODS: The MCNPX and TRansport of Ions in Matter (TRIM) Monte Carlo codes were used in this study. Utilizing the published International Commission on Radiation Units and Measurement 49 data for the water mass stopping power and CSDA ranges, the mass stopping powers of EBT3 and EBT-XD films were derived using the approximation proposed by Newhauser and Zhang in 2009. RESULTS: The calculated CSDA ranges by MCNPX and TRIM in water were first benchmarked to International Commission on Radiation Units and Measurement 49 published data for water, and found to be within 1% with a 1.4-mm maximum difference. The calculated CSDA values in EBT3 film, compared with the measured CSDA ranges in the EBT3 film, were within 2% of the calculated values with a 3-mm maximum difference. The MCNPX and TRIM results for CSDA ranges agreed with each other to within 2.7% for EBT3 film and 4.4% for EBT-XD film. The overall uncertainties of the MCNPX and TRIM-derived CSDA ranges were 3% and 1.3%, respectively. CONCLUSION: The mass stopping powers for Gafchromic EBT3 and EBT-XD films were derived.

IMRT QA using machine learning: A multi-institutional validation.

PURPOSE: To validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions. METHODS: A Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input. RESULTS: The methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle. CONCLUSIONS: We have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process.

Patient-Specific QA of Spot-Scanning Proton Beams using Radiochromic Film.

Radiochromic film for spot-scanning QA provides high spatial resolution and efficiency gains from one-shot irradiation for multiple depths. However, calibration can be a tedious procedure which may limit widespread use. Moreover, since there may be an energy dependence, which manifests as a depth dependence, this may require additional measurements for each patient. We present a one-scan protocol to simplify the procedure. A calibration using an EBT3 film, exposed by a 6-level step-wedge plan on a Proteus®PLUS proton system (IBA, Belgium), was performed at depths of 18, 20, 24cm using Plastic Water® (CIRS, Norfolk, VA). The calibration doses ranged from 65-250 cGy(RBE) (relative biological effectiveness) for proton energies of 170-200 MeV. A clinical prostate+nodes plan was used for validation. The planar doses at selected depths were measured with EBT3 films and analyzed using One-scan protocol (one-scan digitization of QA film and at least one film exposed to a known dose). The gamma passing rates, dose-difference maps, and profiles of 2D planar doses measured with EBT3 film and IBA MatriXX-PT, versus the RayStation TPS calculations were analyzed and compared. The EBT3 film measurement results matched well with the TPS calculation data with an average passing rate of ~95% for 2%/2mm and slightly lower passing rates were obtained from an ion chamber array detector. We were able to demonstrate that the use of a proton step-wedge provided clinically acceptable results and minimized variations between film-scanner orientation, inter-scan, and scanning conditions. Furthermore, for relative dosimetry (calibration is not done at the time of experiment) it could be derived from no more than two films exposed to known doses (one could be zero) for rescaling the master calibration curve at each depth. The sensitivity of the calibration to depth variations has been explored. One-scan protocol results appear to be comparable to that of the ion chamber array detector. The use of a proton step-wedge for calibration of EBT3 film potentially increases efficiency in patient-specific QA of proton beams.

Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field.