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Background Purpose Methods and materials Results Conclusion Radiation therapy often requires the accumulation of doses from multiple treatment fractions or courses for plan evaluation and treatment response assessment. However, due to underlying mass changes, the accumulated dose may not accurately reflect the total deposited energy, leading to potential inaccuracies in characterizing the treatment input.This study introduces an energy‐conserving dose summation method to calculate the total dose in scenarios where patients experience changes in body mass during treatment.The proposed method transfers dose and mass data from dosimetry images, where the delivered doses were calculated, to a reference image using an energy and mass‐conserving dose reconstruction technique. The reconstructed dose assumes the same resolution and dimension as the reference image. The transferred masses are averaged at each image voxel in the reference image to generate an average mass. The transferred doses are then adjusted by multiplying by the ratio of their transferred mass to the average mass, and subsequently summed to calculate a mass‐weighted (MW) total dose at each voxel. This method is demonstrated with a case of lung cancer retreatment.The MW total dose was shown to be equivalent to the total deposited energy divided by the average mass. In the lung cancer retreatment case, the energy derived from the MW total dose was consistent with the sum of energy transferred from two treatments across all evaluated organs.The MW dose summation method can produce a total dose that accurately reflects the total energy deposited in each organ. The consistency may provide a robust foundation for verifying dose accumulations in adaptive radiotherapy. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions The field of artificial intelligence (AI)‐based patient‐specific quality assurance (PSQA) for volumetric modulated arc therapy (VMAT) faces challenges in terms of developing general models across institutions due to the prevalence of multi‐institution data collection and multivariate heterogeneity. Building a general model that is capable of handling diverse multi‐institution data is critical for enabling large‐scale integration and analysis.This study aims to develop a star generative adversarial network (StarGAN) and transformer‐based hybrid classification‐regression PSQA framework to address unification of heterogeneous data from different institutions.A StarGAN and transformer‐based hybrid classification‐regression model was developed as a general PSQA framework to predict gamma passing rates (GPRs) and classify quality assurance (QA) results as “Pass” or “Fail” at multiple institutions. A total of 1815 VMAT plans were collected from eight institutions to develop the general PSQA framework and perform clinical commissioning and implementation. Among them, 20 independent clinical plans from each of eight institutions, for a total of 160 plans, were used for the clinical commissioning, and 205 new clinical plans from eight institutions were used for clinical implementation.For the 3%/3, 3%/2, and 2%/2 mm gamma criteria, the sensitivity of the proposed PSQA framework with pretraining was 90.13%, 92.03%, and 95.84%, respectively, while the specificity was 76.01%, 76.12%, and 85.34%, respectively. The mean absolute errors (MAEs) of the proposed PSQA framework with pretraining were 1.36%, 2.37%, and 3.96%, respectively, while the root‐mean‐square errors (RMSEs) were 2.31%, 3.89%, and 5.17%, respectively. The results demonstrated visible improvement at multiple institutions. For clinical commissioning, the deviations between the predicted and measured results were all within 3% for 3%/3 and 3%/2 mm at eight institutions. For clinical implementation, all failure plans were correctly identified by the proposed PSQA framework.The general PSQA framework enables diverse clinical data sources to be handled to achieve enhanced model performance and generalizability, and provides a solution to the unification of heterogeneous data from different institutions to construct robust QA models. This approach can be clinically deployed for VMAT QA. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion The Joint AAPM‐ESTRO TG‐360 is developing a quantitative framework to evaluate treatment verification systems used for patient‐specific quality assurance (PSQA). A subgroup was commissioned to determine which potential failure modes had the greatest risk to treatment quality and safety, and therefore should be evaluated as part of the PSQA verification.To create an extensive database of potential radiotherapy failure modes that should be detected by PSQA and to determine their relative importance for maximizing treatment quality.The subgroup consisted of eight physicists from seven countries, including representatives from three international quality assurance groups. We collected error reports from RO‐ILS, SAFRON, AAPM TG publications, and other literature, including international audits. We focused on the subset of failure modes that impact whether the planned dose matches the dose received by the patient. We performed a failure‐mode‐and‐effects analysis (FMEA), estimating the severity (S), occurrence (O), and detectability (D) of each failure mode. Detectability was scored assuming that PSQA was not done but other routine clinical QA was performed, which allowed us to see the importance of PSQA for detecting each specific failure mode. We analyzed the risk priority number (RPN = O*S*D), O*S, and severity rankings to determine the priority of each failure mode.We collected 394 error reports, which we categorized into 33 failure modes that underwent FMEA. Five failure modes were in the top ranks for both RPN and O*S analysis: four involving treatment planning system (TPS) commissioning and one regarding patient model errors. The highest‐ranking RPN failure modes were: TPS algorithm limitations, TPS commissioning errors [multileaf collimator (MLC) modeling, output factor, percent‐depth‐dose/tissue‐maximum‐ratio (PDD/TMR), off‐axis factor], and patient weight variation. The highest O*S failure modes were similar, with the addition of external patient position variation and incorrect linear accelerator isocenter and cGy/monitor units calibration. RPN and O*S analyses prioritized failure modes that impacted multiple patients with high occurrence and detectability scores, while severity analysis gave higher priority to single‐patient modes with high severity scores. The highest‐ranking severity modes were MLC sequence deletion, collision, and TPS isocenter incorrect.We have developed a list of failure modes critical to be detected during PSQA and ranked them in order of importance. The top failure modes emphasize the importance of utilizing a variety of treatment verification systems for PSQA, from secondary dose calculation through in‐vivo dosimetry, in order to detect all possible errors. For failure modes in the top quartile, PSQA is critical. Without adequate PSQA, these errors may go undetected unless caught by an external audit. This analysis can be useful for optimizing PSQA workflows and for designing evaluations of treatment verification systems, and will be used by the Joint AAPM‐ESTRO TG‐360 to determine an appropriate validation strategy. [ABSTRACT FROM AUTHOR]

AAPM Task Group Report 135.B covers new technology components that have been added to an established radiosurgery platform and updates the components that were not well covered in the previous report. Considering the current state of the platform, this task group (TG) is a combination of a foundational task group to establish the basis for new processes/technology and an educational task group updating guidelines on the established components of the platform. Because the technology discussed in this document has a relatively small user base compared to C‐arm isocentric linacs, the authors chose to emphasize the educational components to assist medical physicists who are new to the technology and have not had the opportunity to receive in‐depth vendor training at the time of reading this report. The TG has developed codes of practice, introduced QA, and developed guidelines which are generally expected to become enduring practice. This report makes prescriptive recommendations as there has not been enough longitudinal experience with some of the new technical components to develop a data‐based risk analysis. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions In 2021, a Technical Meeting was hosted by the International Atomic Energy Agency (IAEA) where it was recommended that a standardized method for assessing the accuracy of film dose calculations should be established.To design an audit that evaluates the accuracy of film dosimetry processes. To propose a framework for identifying out‐of‐tolerance results and to perform an international pilot study to test the audit design.Six members of an international Dosimetry Audit Network (DAN) developed an audit for radiochromic film dosimetry. A single host center provided the materials to each participating DAN member to conduct the audits. Materials included: (1) a set of two irradiated audit films (10 Sq: 10 cm × 10 cm, 15 Sq: 15 cm × 15 cm), (2) a reference calibration film set, and (3) a blank sheet of film. The participants were blinded to the dose and tasked with producing dose maps using their standard film dosimetry process. Average Region‐Of‐Interest (ROI: 2 cm × 2 cm) dose was measured from the dose maps and compared to the known dose. In the audit, all participants used their local scanning and software protocols. Film calibration was performed in two distinct ways: (1) using a calibration film set which was provided by the host center and (2) using a calibration film set which was locally irradiated. Several variations of the audit were also performed to examine how scanning and software processing can affect film dosimetry results. In the first variation of the audit (VariantA), a set of film scans was processed using five different software solutions. In the second variation of the audit (VariantB), all films were scanned on the same scanner and processed using two in‐house software solutions.Taking one film scan from each participant, the standard deviations (1σ) (SD) in the dose returned from the host calibration and returned from the local calibration were ±7.2% and ±6.5% respectively, with variations from −12.4% to 12.9% of the known dose. The larger dose variations in the data set were attributed to the corrections applied for variations in scanner brightness during processing and incorrectly assigned calibration doses. When the raw image data set was processed by an expert user of each software solution (VariantA) the SDs were ±2.7% and ±3.7% for in‐house and vendor‐based software, respectively. When the films were scanned on a single scanner and processed with the two in‐house software solutions (VariantB) the results had a SD of ±2.3%.An audit has been designed and tested for radiotherapy film dosimetry at an international level. A framework for diagnosing issues within a film dosimetry process has been proposed that could be used to audit centers that use film as a dosimeter. Incorporating quality assurance throughout the film process is important in obtaining accurate and consistent film dosimetry. A better understanding of vendor‐based software systems is necessary for users to process accurate and consistent film dosimetry. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Accelerator‐based boron neutron capture therapy (AB‐BNCT) systems are becoming commercially available and are expected to be widely used in hospitals. To ensure the safety of BNCT, establishing a quality assurance (QA) program and properly managing the stability of the system are necessary. In particular, a high level of beam output stability is required to avoid accidents because beam output is a major factor in patient dose. However, no studies have analyzed the long‐term beam output stability of AB‐BNCT systems.This study aimed to retrospectively analyze the long‐term stability of the beam output by statistical process control (SPC) based on the QA results over 3 years.The data analyzed are the results of daily QA (DQA) and weekly QA (WQA) in an AB‐BNCT system and were taken between June 2020 and September 2023. The evaluation of the stability of the beam output was based on the reaction rate between gold and neutrons calculated using the activation foil method using a gold foil. In DQA, which can be performed in a short time, the gold foil was applied directly to the beam irradiation aperture in air. In WQA, measurements were performed at the phantom surface, 2‐cm depth, and 6‐cm depth using a dedicated water phantom. The acquired data were retrospectively analyzed by individuals and a moving range chart (I‐MR chart), exponentially weighted moving average control chart (EWMA chart), and several process capability indexes (PCIs).Over 99% of the DQA I‐MR chart results were within control limits, whereas the WQA I‐MR chart results showed that 1.8%, 4.1%, and 2.0% of the measurements exceeded the control limits at the surface, 2‐cm depth, and 6‐cm depth, respectively. The variation in the reaction rate of the gold foil before and after the replacement of the target was <0.5%. The EWMA chart results revealed no significant beam output drift for either DQA or WQA. Most measured data were normal based on the results of the Anderson–Darling test and met the requirements for PCI evaluation; most PCI values were >1.0; however, the Cpmk of DQA and the 2‐ and 6‐cm depth WQAs between August 2021 and November 2022 in treatment course 2 were 0.83, 0.77, and 0.87, respectively, which were <1.0.The long‐term stability of beam output was confirmed using SPC in an AB‐BNCT system. The results of the control chart revealed no significant variation or drift in the beam output, and the quantitative evaluation using PCI revealed high stability. A routine QA program will enable us to provide safe BNCT. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Pencil Beam Scanning proton therapy has many advantages from a therapeutic point of view, but raises technical constraints in terms of treatment verification. The treatment relies on a large number of planned pencil beams (PB) (up to thousands), whose delivery is divided in several low‐intensity pulses delivered a high frequency (1 kHz in this study).The purpose of this study was to develop a three‐dimensional quality assurance system allowing to verify all the PBs’ characteristics (position, energy, intensity in terms of delivered monitor unit—MU) of patient treatment plans on a pulse‐by‐pulse or a PB‐by‐PB basis.A system named SCICOPRO has been developed. It is based on a 10 × 10 × 10 cm3 scintillator cube and a fast camera, synchronized with beam delivery, recording two views (direct and using a mirror) of the scintillation distribution generated by the pulses. A specific calibration and analysis process allowed to extract the characteristics of all the pulses delivered during the treatment, and consequently of all the PBs. The system uncertainties, defined here as average value + standard deviation, were characterized with a customized irradiation plan at different PB intensities (0.02, 0.1, and 1 MU) and with two patient's treatment plans of three beams each. The system's ability to detect potential treatment delivery problems, such as positioning errors of the treatment table in this work (1° rotations and a 2 mm translation), was assessed by calculating the confidence intervals (CI) for the different characteristics and evaluating the proportion of PBs within these intervals.The performances of SCICOPRO were evaluated on a pulse‐by‐pulse basis. They showed a very good signal‐to‐noise ratio for all the pulse intensities (between 2 × 10−3 MU and 150 × 10−3 MU) allowing uncertainties smaller than 580 µm for the position, 180 keV for the energy and 3% for the intensity on patients treatment plans. The position and energy uncertainties were found to be little dependent from the pulse intensities whereas the intensity uncertainty depends on the pulses number and intensity distribution. Finally, treatment plans evaluations showed that 98% of the PBs were within the CIs with a nominal positioning against 83% or less with the table positioning errors, thus proving the ability of SCICOPRO to detect this kind of errors.The high acquisition rate and the very high sensitivity of the system developed in this work allowed to record pulses of intensities as low as 2 × 10−3 MU. SCICOPRO was thus able to measure all the characteristics of the spots of a treatment (position, energy, intensity) in a single measurement, making it possible to verify their compliance with the treatment plan. SCICOPRO thus proved to be a fast and accurate tool that would be useful for patient‐specific quality assurance (PSQA) on a pulse‐by‐pulse or PB‐by‐PB verification basis. [ABSTRACT FROM AUTHOR]

Background: As radiotherapy techniques advance, so do planning methods for multi‐target intracranial SRS cases. Multi‐target‐single‐isocenter (MTSI) planning offers high‐precision beam delivery with shortened duration. However, accommodating all targets in a single Patient‐Specific‐Quality‐Assurance (PSQA) with QA devices like SRS MapCHECK (SRS MC) is generally impractical. Purpose: Consequently, we conducted PSQA, using a custom script, by relocating each Target or Neighboring‐Target‐Group (T‐NTG) relative to the beam isocenter on the PSQA device, ensuring each target's dose coverage at high precision. Methods: SRS treatment plans use 6MV–FFF beams, consisting of four Volumetric Modulated ARC Therapy (VMAT) arcs, including one full‐arc and three half arcs with couch‐kicks. A custom script calculated T‐NTG coordinates relative to the beam isocenter. QA verification plans were created for each T‐NTG, redefining the beam isocenter for precise alignment with the center of the SRS MC. CBCT images were acquired during PSQA for SRS MC alignment, and gamma‐index analysis (GIA) was performed. A single‐tail paired t‐test assessed the passing rate (PR) for 75 QA verification plans. Results: GIA with l.0 mm/2.0% criteria for each QA plan yielded a PR > 95.5%, with an average of 98.9%. Plans achieving PR > 99.0% and > 97.0% constituted 63% and 92% of studied plans, respectively. Statistical significance was observed in a t‐test with an ideal PR value of 100%, while insignificance was found with a PR value of 99%, suggesting that PSQA for individual targets consistently approaches 99% PR. In MTSI cases using 6MV‐FFF beams, targets within the lateral dose‐fall‐off region require careful verification for acceptability. Our clinical study on individual T‐NTG relocation demonstrates that the presented PSQA methods are generally acceptable, supported by a statistically insignificant PR against a 99% PR value. Conclusions: Presented statistical analysis results indicate that the proposed PSQA approach can serve as a reliable tool in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Clinical intensity modulated radiation therapy plans have been described using various complexity metrics to help identify problematic radiotherapy plans. Most previous studies related to the quantification of plan complexity and their utility have relied on institution‐specific plans which can be highly variable depending on the machines, planning techniques, delivery modalities, and measurement devices used. In this work, 1723 plans treating one of only four standardized geometries were simultaneously analyzed to investigate how radiation plan complexity metrics vary across four different sets of common objectives. Purpose: To assess the treatment plan complexity characteristics of plans developed for Imaging and Radiation Oncology Core (IROC) phantoms. Specifically, to understand the variability in plan complexity between institutions for a common plan objective, and to evaluate how various complexity metrics differentiate relevant groups of plans. Methods: 1723 plans treating one of four standardized IROC phantom geometries representing four different anatomical sites of treatment were analyzed. For each plan, 22 MLC‐descriptive plan complexity metrics were calculated, and principal component analysis (PCA) was applied to the 22 metrics in order to evaluate differences in plan complexity between groups. Across all metrics, pairwise comparisons of the IROC phantom data were made for the following classifications of the data: anatomical phantom treated, treatment planning system (TPS), and the combination of MLC model and treatment planning system. An objective k‐means clustering algorithm was also applied to the data to determine if any meaningful distinctions could be made between different subgroups. The IROC phantom database was also compared to a clinical database from the University of Wisconsin‐Madison (UW) which included plans treating the same four anatomical sites as the IROC phantoms using a TrueBeam™ STx and Pinnacle3 TPS. Results: The IROC head and neck and spine plans were distinct from the prostate and lung plans based on comparison of the 22 metrics. All IROC phantom plan group complexity metric distributions were highly variable despite all plans being designed for identical geometries and plan objectives. The clusters determined by the k‐means algorithm further supported that the IROC head and neck and spine plans involved similar amounts of complexity and were largely distinct from the prostate and lung plans, but no further distinctions could be made. Plan complexity in the head and neck and spine IROC phantom plans were similar to the complexity encountered in the UW clinical plans. Conclusions: There is substantial variability in plan complexity between institutions when planning for the same objective. For each IROC anatomical phantom treated, the magnitude of variability in plan complexity between institutions is similar to the variability in plan complexity encountered within a single institution database containing several hundred unique clinical plans treating corresponding anatomies in actual patients. [ABSTRACT FROM AUTHOR]

Background: There currently exists no widespread high dose‐rate (HDR) brachytherapy afterloader quality assurance (QA) tool for simultaneously assessing the afterloader's positional, temporal, transit velocity and air kerma strength accuracy. Purpose: The purpose of this study was to develop a precise and rigorous technique for performing daily QA of HDR brachytherapy afterloaders, incorporating QA of: dwell position accuracy, dwell time accuracy, transit velocity consistency and relative air kerma strength (AKS) of an Ir‐192 source. Method: A Sharp ProGuide 240 mm catheter (Elekta Brachytherapy, Veenendaal, The Netherlands) was fixed 5 mm above a 256 channel epitaxial diode array 'dose magnifying glass' (DMG256) (Centre for Medical and Radiation Physics, University of Wollongong). Three dwell positions, each of 5.0 s dwell times, were spaced 13.0 mm apart along the array with the Flexitron HDR afterloader (Elekta Brachytherapy, Veenendaal, The Netherlands). The DMG256 was connected to a data acquisition system (DAQ) and a computer via USB2.0 link for live readout and post‐processing. The outputted data files were analyzed using a Python script to provide positional and temporal localization of the Ir‐192 source by tracking the centroid of the detected response. Measurements were repeated on a weekly basis, for a period of 5 weeks to determine the consistency of the measured parameters over an extended period. Results: Using the DMG256 for relative AKS measurements resulted in measured values within 0.6%–3.0% of the expected activity over a 7‐week period. The sub‐millisecond temporal accuracy of the device allowed for measurements of the transit velocity with an average of (10.88 ± 1.01) cm/s for 13 mm steps. The dwell position localization for 1, 2, 3, 5, and 10 mm steps had an accuracy between 0.1 and 0.3 mm (3σ), with a fixed temporal accuracy of 10 ms. Conclusion: The DMG256 silicon strip detector allows for clinics to perform rigorous daily QA of HDR afterloader dwell position and dwell time accuracy with greater precision than the current standard methodology using closed circuit television and a stopwatch. Additionally, DMG256 unlocks the ability to perform measurements of transit velocity/time and relative AKS, which are not possible using current standard techniques. [ABSTRACT FROM AUTHOR]

Background: A dosimeter with high spatial and temporal resolution would be of significant interest for pencil beam scanning (PBS) proton beams' characterization, especially when facing small fields and beams with high temporal dynamics. Optical imaging of scintillators has potential in providing sub‐millimeter spatial resolution with pulse‐by‐pulse basis temporal resolution when the imaging system is capable of operating in synchrony with the beam‐producing accelerator. Purpose: We demonstrate the feasibility of imaging PBS proton beams as they pass through a plastic scintillator detector to simultaneously obtain multiple beam parameters, including proton range, pencil beam's widths at different depths, spot's size, and spot's position on a pulse‐by‐pulse basis with sub‐millimeter resolution. Materials and methods: A PBS synchrocyclotron was used for proton irradiation. A BC‐408 plastic scintillator block with 30 × 30 × 5 cm3 size, and another block with 30 × 30 × 0.5 cm3 size, positioned in an optically sealed housing, were used sequentially to measure the proton range, and spot size/location, respectively. A high‐speed complementary metal‐oxide‐semiconductor (CMOS) camera system synchronized with the accelerator's pulses through a gating module was used for imaging. Scintillation images, captured with the camera directly facing the 5‐cm‐thick scintillator, were corrected for background (BG), and ionization quenching of the scintillator to obtain the proton range. Spots' position and size were obtained from scintillation images of the 0.5‐cm‐thick scintillator when a 45° mirror was used to reflect the scintillation light toward the camera. Results: Scintillation images with 0.16 mm/pixel resolution corresponding to all proton pulses were captured. Pulse‐by‐pulse analysis showed that variations of the range, spots' position, and size were within ± 0.2% standard deviation of their average values. The absolute ranges were within ± 1 mm of their expected values. The average spot‐positions were mostly within ± 0.8 mm and spots' sigma agreed within 0.2 mm of the expected values. Conclusion: Scintillation‐imaging PBS beams with high‐spatiotemporal resolution is feasible and may help in efficient and cost‐effective acceptance testing and commissioning of existing and even emerging technologies such as FLASH, grid, mini‐beams, and so forth. [ABSTRACT FROM AUTHOR]

Purpose: As carbon ion radiotherapy increases in use, there are limited phantom materials for heterogeneous or anthropomorphic phantom measurements. This work characterized the radiological clinical equivalence of several phantom materials in a therapeutic carbon ion beam. Methods: Eight materials were tested for radiological material‐equivalence in a carbon ion beam. The materials were computed tomography (CT)‐scanned to obtain Hounsfield unit (HU) values, then irradiated in a monoenergetic carbon ion beam to determine relative linear stopping power (RLSP). The corresponding HU and RLSP for each phantom material were compared to clinical carbon ion calibration curves. For absorbed dose comparison, ion chamber measurements were made in the center of a carbon ion spread‐out Bragg peak (SOBP) in water and in the phantom material, evaluating whether the material perturbed the absorbed dose measurement beyond what was predicted by the HU‐RLSP relationship. Results: Polyethylene, solid water (Gammex and Sun Nuclear), Blue Water (Standard Imaging), and Techtron HPV had measured RLSP values that agreed within ±4.2% of RLSP values predicted by the clinical calibration curve. Measured RLSP for acrylic was 7.2% different from predicted. The agreement for balsa wood and cork varied between samples. Ion chamber measurements in the phantom materials were within 0.1% of ion chamber measurements in water for most materials (solid water, Blue Water, polyethylene, and acrylic), and within 1.9% for the rest of the materials (balsa wood, cork, and Techtron HPV). Conclusions: Several phantom materials (Blue Water, polyethylene, solid water [Gammex and Sun Nuclear], and Techtron HPV) are suitable for heterogeneous phantom measurements for carbon ion therapy. Low density materials should be carefully characterized due to inconsistencies between samples. [ABSTRACT FROM AUTHOR]

Background: Phantoms are commonly used to evaluate and compare the performance of imaging systems given the known ground truth. Positron emission tomography (PET) scanners are routinely validated using the NEMA image quality phantom, in which lesions are modeled using 10 to 37 mm fillable spheres. The NEMA phantom neglects, however, to model focal (3–10‐mm), high‐uptake lesions that are increasingly observed in prostate‐specific membrane antigen (PSMA) PET images. PSMA‐targeting radiopharmaceuticals allow for enhanced detection of metastatic prostate cancers. As such, there is significant need to develop an updated phantom which considers both the quantitative and lesion detectability of this new paradigm in oncological PET imaging. Purpose: In this work, we present the Quantitative PET Prostate Phantom (Q3P); a portable and modular phantom that can be used to improve and harmonize imaging protocols for 18F‐PSMA PET scans. Methods: A one‐piece cylindrical phantom was designed effectively in two halves, which we call modules. Module 1 was designed to mimic lesions in the presence of background, and Module 2 mimicked very high contrast conditions (i.e., very low background) that can be observed in 18F‐PSMA PET scans. Shell‐less radioactive spheres (3–16‐mm) were cast using epoxy resin mixed with sodium‐22 (22Na), a long half‐life positron emitter with positron range similar to 18F. To establish realistic lesion contrast, the 22Na spheres were mounted in a cylindrical chamber that can be filled with an 18F background (module 1). Thirteen exchangeable spherical cavity inserts (3–37‐mm) were machined in two parts and solvent welded together, and filled with 18F (50 kBq/mL) to model lesions with very high contrast (module 2). Five 2.5‐min PET scans were acquired on a 5‐ring GE Discovery MI PET/CT scanner (General Electric, USA). Lesions were segmented using 41% of SUVmax fixed thresholding (41% FT) and recovery coefficients (RCs) were computed from 5 noise realizations. Results: The manufactured phantom is portable (5.7 kg) and scan preparation takes less than 40 min. The total 22Na activity is 250 kBq, allowing it to be shipped as an exempt package under International Atomic Energy Agency (IAEA) regulations. Recovery coefficients, computed using PSF modeling and no post‐reconstruction smoothing, were 130.3% (16 mm), 147.1% (10 mm), 87.2% (6 mm), and 7.0% (3 mm) for RCmax, which decreased to 91.1% (16 mm), 90.6% (10 mm), 53.2% (6 mm), and 3.6% (3 mm) for RCmean in the 22Na spheres. Comparatively, 18F sphere recovery was 110.7% (17 mm), 123.6% (10 mm), 106.5% (7 mm), and 23.3% (3 mm) for RCmax, which was reduced to 76.7% (17 mm), 77.7% (10 mm), 66.8% (7 mm), and 13.5% (3 mm), for RCmean. Conclusions: A standardized imaging phantom was developed for lesion quantification assessment in 18F‐PSMA PET images. The phantom is configurable, providing users with the opportunity to modify background activity levels or sphere sizes according to clinical demands. Distributed to the community, the Q3P phantom has the potential to enable better assessment of lesion quantification and harmonization of 18F‐PSMA PET imaging, which may lead to more robust predictive metrics and better outcome prediction in metastatic prostate cancer. [ABSTRACT FROM AUTHOR]

Background: Electromagnetic tracking (EMT) systems have proven to be a valuable source of information regarding the location and geometry of applicators in patients undergoing brachytherapy (BT). As an important element of an enhanced and individualized pre‐treatment verification, EMT can play a pivotal role in detecting treatment errors and uncertainties to increase patient safety. Purpose: The purpose of this study is two‐fold: to design, develop and test a dedicated measurement protocol for the use of EMT‐enabled afterloaders in BT and to collect and compare the data acquired from three different radiation oncology centers in different clinical environments. Methods: A novel quality assurance (QA) phantom composed of a scaffold with supports to fix the field generator, different BT applicators, and reference sensors (sensor verification tools) was used to assess the precision (jitter error) and accuracy (relative distance errors and target registration error) of the EMT sensor integrated into an afterloader prototype. Measurements were repeated in different environments where EMT measurements are likely to be performed, namely an electromagnetically clean laboratory, a BT suite, an operating room, and, if available, a CT suite and an MRI suite dedicated to BT. Results: The mean positional jitter was consistently under 0.1 mm across all measurement points, with a slight trend of increased jitter at greater distances from the field generator. The mean variability of sensor positioning in the tested tandem and ring gynecological applicator was also below 0.1 mm. The tracking accuracy close to the center of the measurement volume was higher than at its edges. The relative distance error at the center was 0.2‐0.3 mm with maximum values reaching 1.2‐1.8 mm, but up to 5.5 mm for measurement points close to the edges. In general, similar accuracy results were obtained in the clinical environments and in all investigated institutions (median distance error 0.1‐0.4 mm, maximum error 1.0‐2.0 mm), however, errors were found to be larger in the CT suite (median distance error up to 1.0 mm, maximum error up to 3.6 mm). Conclusion: The presented quality assessment protocol for EMT systems in BT has demonstrated that EMT offers a high‐accuracy determination of the applicator/implant geometry even in clinical environments. In addition to that, it has provided valuable insights into the performance of EMT‐enabled afterloaders across different radiation oncology centers. [ABSTRACT FROM AUTHOR]

Background: Standards for image quality evaluation in multi‐detector CT (MDCT) and cone‐beam CT (CBCT) are evolving to keep pace with technological advances. A clear need is emerging for methods that facilitate rigorous quality assurance (QA) with up‐to‐date metrology and streamlined workflow suitable to a range of MDCT and CBCT systems. Purpose: To evaluate the feasibility and workflow associated with image quality (IQ) assessment in longitudinal studies for MDCT and CBCT with a single test phantom and semiautomated analysis of objective, quantitative IQ metrology. Methods: A test phantom (CorgiTM Phantom, The Phantom Lab, Greenwich, New York, USA) was used in monthly IQ testing over the course of 1 year for three MDCT scanners (one of which presented helical and volumetric scan modes) and four CBCT scanners. Semiautomated software analyzed image uniformity, linearity, contrast, noise, contrast‐to‐noise ratio (CNR), 3D noise‐power spectrum (NPS), modulation transfer function (MTF) in axial and oblique directions, and cone‐beam artifact magnitude. The workflow was evaluated using methods adapted from systems/industrial engineering, including value stream process modeling (VSPM), standard work layout (SWL), and standard work control charts (SWCT) to quantify and optimize test methodology in routine practice. The completeness and consistency of DICOM data from each system was also evaluated. Results: Quantitative IQ metrology provided valuable insight in longitudinal quality assurance (QA), with metrics such as NPS and MTF providing insight on root cause for various forms of system failure—for example, detector calibration and geometric calibration. Monthly constancy testing showed variations in IQ test metrics owing to system performance as well as phantom setup and provided initial estimates of upper and lower control limits appropriate to QA action levels. Rigorous evaluation of QA workflow identified methods to reduce total cycle time to ∼10 min for each system—viz., use of a single phantom configuration appropriate to all scanners and Head or Body scan protocols. Numerous gaps in the completeness and consistency of DICOM data were observed for CBCT systems. Conclusion: An IQ phantom and test methodology was found to be suitable to QA of MDCT and CBCT systems with streamlined workflow appropriate to busy clinical settings. [ABSTRACT FROM AUTHOR]

Background: For autosegmentation models, the data used to train the model (e.g., public datasets and/or vendor‐collected data) and the data on which the model is deployed in the clinic are typically not the same, potentially impacting the performance of these models by a process called domain shift. Tools to routinely monitor and predict segmentation performance are needed for quality assurance. Here, we develop an approach to perform such monitoring and performance prediction for cardiac substructure segmentation. Purpose: To develop a quality assurance (QA) framework for routine or continuous monitoring of domain shift and the performance of cardiac substructure autosegmentation algorithms. Methods: A benchmark dataset consisting of computed tomography (CT) images along with manual cardiac substructure delineations of 241 breast cancer radiotherapy patients were collected, including one "normal" image domain of clean images and five "abnormal" domains containing images with artifact (metal, contrast), pathology, or quality variations due to scanner protocol differences (field of view, noise, reconstruction kernel, and slice thickness). The QA framework consisted of an image domain shift detector which operated on the input CT images and a shape quality detector on the output of an autosegmentation model, and a regression model for predicting autosegmentation model performance. The image domain shift detector was composed of a trained denoising autoencoder (DAE) and two hand‐engineered image quality features to detect normal versus abnormal domains in the input CT images. The shape quality detector was a variational autoencoder (VAE) trained to estimate the shape quality of the auto‐segmentation results. The output from the image domain shift and shape quality detectors was used to train a regression model to predict the per‐patient segmentation accuracy, measured by Dice coefficient similarity (DSC) to physician contours. Different regression techniques were investigated including linear regression, Bagging, Gaussian process regression, random forest, and gradient boost regression. Of the 241 patients, 60 were used to train the autosegmentation models, 120 for training the QA framework, and the remaining 61 for testing the QA framework. A total of 19 autosegmentation models were used to evaluate QA framework performance, including 18 convolutional neural network (CNN)‐based and one transformer‐based model. Results: When tested on the benchmark dataset, all abnormal domains resulted in a significant DSC decrease relative to the normal domain for CNN models (p<0.001$p < 0.001$), but only for some domains for the transformer model. No significant relationship was found between the performance of an autosegmentation model and scanner protocol parameters (p=0.42$p = 0.42$) except noise (p=0.01$p = 0.01$). CNN‐based autosegmentation models demonstrated a decreased DSC ranging from 0.07 to 0.41 with added noise, while the transformer‐based model was not significantly affected (ANOVA, p=0.99$p=0.99$). For the QA framework, linear regression models with bootstrap aggregation resulted in the highest mean absolute error (MAE) of 0.041±0.002$0.041 \pm 0.002$, in predicted DSC (relative to true DSC between autosegmentation and physician). MAE was lowest when combining both input (image) detectors and output (shape) detectors compared to output detectors alone. Conclusions: A QA framework was able to predict cardiac substructure autosegmentation model performance for clinically anticipated "abnormal" domain shifts. [ABSTRACT FROM AUTHOR]

Background: Deep learning has shown promising results to generate MRI‐based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and reliability are required but still lacking. Purpose: This study aims to evaluate the predictive value of uncertainty maps generated with Monte Carlo dropout (MCD) for verifying proton dose calculations on deep‐learning‐based synthetic CTs (sCTs) derived from MRIs in online adaptive proton therapy. Methods: Two deep‐learning models (DCNN and cycleGAN) were trained for CT image synthesis using 101 paired CT‐MR images. sCT images were generated using MCD for each model by performing 10 inferences with activated dropout layers. The final sCT was obtained by averaging the inferred sCTs, while the uncertainty map was obtained from the HU variance corresponding to each voxel of 10 sCTs. The resulting uncertainty maps were compared to the observed HU‐, range‐, WET‐, and dose‐error maps between the sCT and planning CT. For range and WET errors, the generated uncertainty maps were projected along the 90‐degree angle. To evaluate the dose distribution, a mask based on the 5%‐isodose curve was applied to only include voxels along the beam paths. Pearson's correlation coefficients were calculated to determine the correlation between the uncertainty maps and HUs, range, WET, and dose errors. To evaluate the dosimetric accuracy of synthetic CTs, clinical proton treatment plans were recalculated and compared to the pCTs Results: Evaluation of the correlation showed an average of r = 0.92 ± 0.03 and r = 0.92 ± 0.03 for errors between uncertainty‐HU, r = 0.66 ± 0.09 and r = 0.62 ± 0.06 between uncertainty‐range, r = 0.64 ± 0.06 and r = 0.58 ± 0.07 between uncertainty‐WET, and r = 0.65 ± 0.09 and r = 0.67 ± 0.07 between uncertainty and dose difference for DCNN and cycleGAN model, respectively. Dosimetric comparison for target volumes showed an average 3%/3 mm gamma pass rate of 99.76 ± 0.43 (DCNN) and 99.10 ± 1.27 (cycleGAN). Conclusion: The observed correlations between uncertainty maps and the various metrics (HU, range, WET, and dose errors) demonstrated the potential of MCD‐based uncertainty maps as a reliable QA tool to evaluate the accuracy of deep learning‐based sCTs. [ABSTRACT FROM AUTHOR]

Background: Inadequate computed tomography (CT) number calibration curves affect dose calculation accuracy. Although CT number calibration curves registered in treatment planning systems (TPSs) should be consistent with human tissues, it is unclear whether adequate CT number calibration is performed because CT number calibration curves have not been assessed for various types of CT number calibration phantoms and TPSs. Purpose: The purpose of this study was to investigate CT number calibration curves for mass density (ρ) and relative electron density (ρe). Methods: A CT number calibration audit phantom was sent to 24 Japanese photon therapy institutes from the evaluating institute and scanned using their individual clinical CT scan protocols. The CT images of the audit phantom and institute‐specific CT number calibration curves were submitted to the evaluating institute for analyzing the calibration curves registered in the TPSs at the participating institutes. The institute‐specific CT number calibration curves were created using commercial phantom (Gammex, Gammex Inc., Middleton, WI, USA) or CIRS phantom (Computerized Imaging Reference Systems, Inc., Norfolk, VA, USA)). At the evaluating institute, theoretical CT number calibration curves were created using a stoichiometric CT number calibration method based on the CT image, and the institute‐specific CT number calibration curves were compared with the theoretical calibration curve. Differences in ρ and ρe over the multiple points on the curve (Δρm and Δρe,m, respectively) were calculated for each CT number, categorized for each phantom vendor and TPS, and evaluated for three tissue types: lung, soft tissues, and bones. In particular, the CT‐ρ calibration curves for Tomotherapy TPSs (ACCURAY, Sunnyvale, CA, USA) were categorized separately from the Gammex CT‐ρ calibration curves because the available tissue‐equivalent materials (TEMs) were limited by the manufacturer recommendations. In addition, the differences in ρ and ρe for the specific TEMs (ΔρTEM and Δρe,TEM, respectively) were calculated by subtracting the ρ or ρe of the TEMs from the theoretical CT‐ρ or CT‐ρe calibration curve. Results: The mean ± standard deviation (SD) of Δρm and Δρe,m for the Gammex phantom were −1.1 ± 1.2 g/cm3 and −0.2 ± 1.1, −0.3 ± 0.9 g/cm3 and 0.8 ± 1.3, and −0.9 ± 1.3 g/cm3 and 1.0 ± 1.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm and Δρe,m for the CIRS phantom were 0.3 ± 0.8 g/cm3 and 0.9 ± 0.9, 0.6 ± 0.6 g/cm3 and 1.4 ± 0.8, and 0.2 ± 0.5 g/cm3 and 1.6 ± 0.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm for Tomotherapy TPSs was 2.1 ± 1.4 g/cm3 for soft tissues, which is larger than those for other TPSs. The mean ± SD of Δρe,TEM for the Gammex brain phantom (BRN‐SR2) was −1.8 ± 0.4, implying that the tissue equivalency of the BRN‐SR2 plug was slightly inferior to that of other plugs. Conclusions: Latent deviations between human tissues and TEMs were found by comparing the CT number calibration curves of the various institutes. [ABSTRACT FROM AUTHOR]

Background: This Special Report summarizes the 2022 AAPM Grand Challenge on Deep‐Learning spectral Computed Tomography (DL‐spectral CT) image reconstruction. Purpose: The purpose of the challenge is to develop the most accurate image reconstruction algorithm possible for solving the inverse problem associated with a fast kilovolt switching dual‐energy CT scan using a three tissue‐map decomposition. Participants could choose to use a deep‐learning (DL), iterative, or a hybrid approach. Methods: The challenge is based on a 2D breast CT simulation, where the simulated breast phantom consists of three tissue maps: adipose, fibroglandular, and calcification distributions. The phantom specification is stochastic so that multiple realizations can be generated for DL approaches. A dual‐energy scan is simulated where the x‐ray source potential of successive views alternates between 50 and 80 kilovolts (kV). A total of 512 views are generated, yielding 256 views for each source voltage. We generate 50 and 80 kV images by use of filtered back‐projection (FBP) on negative logarithm processed transmission data. For participants who develop a DL approach, 1000 cases are available. Each case consists of the three 512 × 512 tissue maps, 50 and 80‐kV transmission data sets and their corresponding FBP images. The goal of the DL network would then be to predict the material maps from either the transmission data, FBP images, or a combination of the two. For participants developing a physics‐based approach, all of the required modeling parameters are made available: geometry, spectra, and tissue attenuation curves. The provided information also allows for hybrid approaches where physics is exploited as well as information about the scanned object derived from the 1000 training cases. Final testing is performed by computation of root‐mean‐square error (RMSE) for predictions on the tissue maps from 100 new cases. Results: Test phase submission were received from 18 research groups. Of the 18 submissions, 17 were results obtained with algorithms that involved DL. Only the second place finishing team developed a physics‐based image reconstruction algorithm. Both the winning and second place teams had highly accurate results where the RMSE was nearly zero to single floating point precision. Results from the top 10 also achieved a high degree of accuracy; and as a result, this special report outlines the methodology developed by each of these groups. Conclusions: The DL‐spectral CT challenge successfully established a forum for developing image reconstruction algorithms that address an important inverse problem relevant for spectral CT. [ABSTRACT FROM AUTHOR]

Background: Predicting models of the gamma passing rate (GPR) have been studied to substitute the measurement‐based gamma analysis. Since these studies used data from different radiotherapy systems comprising TPS, linear accelerator, and detector array, it has been difficult to compare the performances of the predicting models among institutions with different radiotherapy systems. Purpose: We aimed to develop unbiased scoring methods to evaluate the performance of the models predicting the GPR, by introducing both best and worst limits for the performance of the GPR prediction. Methods: Two hundred head‐and‐neck VMAT plans were used to develop a framework. The GPRs were measured using the ArcCHECK device. The predicted GPR [p] was generated using a deep learning‐based model [pDL]. The predicting model was evaluated using four metrics: standard deviation (SD) [σ], Pearson's correlation coefficient (CC) [r], mean squared error (MSE) [s], and mean absolute error (MAE) [a]. The best limit [σm${\sigma _m}$, rm${r_m}$, sm${s_m}$, and am${a_m}$] was estimated by measuring the SD of measured GPR [m] by shifting the device along the longitudinal direction to measure different sampling points. Mimicked best and worst p's [pbest and pworst] were generated from pDL. The worst limit was defined such that m and p have no correlation [CC ∼ 0]. The worst limit [σMix, rMix, sMix, and aMix] was generated using the event‐mixing (EM) technique originally introduced in high‐energy physics experiments. The range of σ, r, s, and a was defined to be [σm,σMix]$[ {{\sigma _m},{\sigma _{{\mathrm{Mix}}}}} ]$, [0,rm]$[ {0,{r_m}} ]$, [sm,sMix]$[ {{s_m},{s_{{\mathrm{Mix}}}}} ]$, and [am,aMix]$[ {{a_m},{a_{{\mathrm{Mix}}}}} ]$. The achievement score (AS) independently based on σ, r, s, and a were calculated for pDL, pbest and pworst. The probability that p fails the gamma analysis (alert frequency; AF) was estimated as a function of σd${\sigma _d}$ values within the [σm${\sigma _m}$, σMix] range for the 3%/2 mm data with a 95% criterion. Results: SDs of the best limit were well reproduced by σm=0.531100−m${\sigma _m} = \;0.531\sqrt {100 - m} $. The EM technique successfully generated the (m,p)$({m,p})$ pairs with no correlation. The AS using four metrics showed good agreement. This agreement indicates successful definitions of both best and worst limits, consistent definitions of the AS, and successful generations of mixed events. The AF for the DL‐based model with the 3%/2 mm tolerance was 31.5% and 63.0% with CL's 99% and 99.9%, respectively. Conclusion: We developed the AS to evaluate the predicting model of the GPR in an unbiased manner by excluding the effects of the precision of the radiotherapy system and the spreading of the GPR. The best and worst limits of the GPR prediction were successfully generated using the measured precision of the GPR and the EM technique, respectively. The AS and σp${\sigma _p}$ are expected to enable objective evaluation of the predicting model and setting exact achievement goal of precision for the predicted GPR. [ABSTRACT FROM AUTHOR]

Purpose: A comprehensive, centrally‐monitored physics quality control (QC) program was developed for the Tomosynthesis Imaging Screening Trial (TMIST), a randomized controlled trial of digital breast tomosynthesis (TM) versus digital mammography (DM) for cancer screening. As part of the program, in addition to a set of phantom‐based tests, de‐identified data on image acquisition and processing parameters were captured from the DICOM headers of all individual patient images in the trial. These data were analyzed to assess the potential usefulness of header data from digital mammograms and tomosynthesis images of patients for quality assurance in breast imaging. Methods: Data were automatically extracted from the headers of all de‐identified patient mammograms and tomosynthesis images in the TMIST study. Image acquisition parameters and estimated radiation doses were tracked for individual sites, systems and across system types. These parameters included (among others) kV, target/filter use, number of acquired views per examination, AEC mode, compression thickness and force and detector temperature. Consistency of manually entered study data parameters (subject ID, screening time‐point) from TMIST was evaluated. Preliminary observations from the program are presented. Results: We report on data from 812 651 images from 135 525 examinations acquired between October, 2017 and December, 2022. Data came from 6 system models from 3 manufacturers. There was greater variability both in the number of views used and in the estimated (proxy) doses received in DM exams compared to TM. Mean proxy doses per examination varied among manufacturers from 2.76–4.54 mGy for DM and 3–4.84 mGy for the tomosynthesis component in the TM arm with maximum examination proxy doses of 20 and 26 mGy for DM and TM respectively. Mean proxy doses per examination for the combination examination in TM (tomosynthesis plus digital mammography) varied from 6.6 to 7.6 mGy among manufacturers with a maximum of 44.5 mGy. Conclusions: Overall, modern digital mammography and tomosynthesis systems used in TMIST have operated very reliably. Doses vary considerably due to variation in the number of views per examination, thickness and fibro‐glandularity of the breast, and choices in the use of synthesized versus actual 2D mammography in the TM examination. These data may also be useful in predicting equipment problems. Header information is valuable not only for automated QC, but also for cross‐checking accuracy and consistency of data in a clinical study. [ABSTRACT FROM AUTHOR]

Background: Mixed photon–electron beam radiotherapy (MBRT) is a technique that combines the use of both photons and electrons in one single treatment plan to exploit their advantageous and complimentary characteristics. Compared to other photon treatment modalities, it has been shown that the MBRT technique contributes to better target coverage and organ‐at‐risk (OAR) sparing. However, the use of combined photons and electrons in one delivery makes the technique more complex and a well‐established quality assurance (QA) protocol for MBRT is essential. Purpose: To investigate the feasibility of using MapCHECK and log file‐dose reconstruction for MBRT plan verification and to recommend a patient‐specific quality assurance (PSQA) protocol for MBRT. Methods: MBRT plans were robustly optimized for five soft‐tissue sarcoma (STS) patients. Each plan comprised step‐and‐shoot deliveries of a six MV photon beam and a combination of five electron beam energies at an SAD of 100 cm. The plans were delivered to the MapCHECK device with collapsed gantry angle and the 2D dose distributions at the detector depth were measured. To simulate the expected dose distribution delivered to the MapCHECK, a MapCHECK computational phantom was modeled in EGSnrc based on vendor‐supplied blueprint information. The dose to the detectors in the model was scored using the DOSXYZnrc user code. The agreement between the measured and the simulated dose distribution was evaluated using 2D gamma analysis with a gamma criterion of 3%/2 mm and a low dose threshold of 10%. One of the plans was selected and delivered with a rotating gantry angle for trajectory log file collection. To evaluate the potential interlinac and intralinac differences, the plan was delivered repeatedly on three linacs. From the collected log files, delivery parameters were retrieved to recalculate the 3D dose distributions in the patient's anatomy with DOSXYZnrc. The recalculated mean dose to the clinical target volume (CTV) and OARs from all deliveries were computed and compared with the planned dose in terms of percentage difference. To validate the accuracy of log file‐based QA, the log file‐recalculated dose was also compared with film measurement. Results: The agreement of the total dose distribution between the MapCHECK measurement and simulation showed gamma passing rates of above 97% for all five MBRT plans. In the log file‐dose recalculation, the difference between the recalculated and the planned dose to the CTV and OARs was below 1% for all deliveries. No significant inter‐ or intralinac differences were observed. The log file‐dose had a gamma passing rate of 98.6% compared to film measurement. Conclusion: Both the MapCHECK measurements and log file‐dose recalculations showed excellent agreement with the expected dose distribution. This study demonstrates the potential of using MapCHECK and log files as MBRT QA tools. [ABSTRACT FROM AUTHOR]

Background: Beam data commissioning is a core task of radiotherapy physicists. Despite multiple detectors available, a feasible measurement program compromises between detector properties and time constraints. Therefore, it is important to understand how nonideal measurement data propagates into patient dose calculation. Purpose: We simulated the effects of realistic errors, due to beam commissioning with presumably nonoptimal detectors, on the resulting patient dose distributions. Additionally, the detectability of such beam commissioning errors during patient plan quality assurance (QA) was evaluated. Methods: A clinically used beam model was re‐commissioned introducing changes to depth dose curves, output factors, profiles or combinations of those. Seventeen altered beam models with incremental changes of the modelling parameters were created to analyze dose changes on simplified anatomical phantoms. Additionally, fourteen altered models incorporate changes in the order of signal differences reported for typically used detectors. Eighteen treatment plans of different types were recalculated on patient CT data sets using the altered beam models. Results: For the majority of clinical plans, dose distributions in the target volume recalculated on the patient computed tomography data were similar between the original and the modified beam models, yielding global 2%/2 mm gamma pass rates above 98.9%. Larger changes were observed for certain combinations of beam modelling errors and anatomical sites, most extreme for output factor changes in a small target volume plan with a pass rate of 80.6%. Modelling an enlarged penumbra as if measured with a 0.125 cm3 ion chamber had the largest effect on the dose distribution (average pass rate of 96.5%, lowest 85.4%). On different QA phantom geometries, dose distributions between calculations with modified and unmodified models typically changed too little to be detected in actual measurements. Conclusion: While the simulated errors during beam modelling had little effect on most plans, in some cases changes were considerable. High‐quality penumbra and small field output factor should be a main focus of commissioning measurements. Detecting modelling issues using standard patient QA phantoms is unlikely. Verification of a beam model should be performed especially for plans with high modulation and in different depths or geometries representing the variety of situations expected clinically. [ABSTRACT FROM AUTHOR]

Background: Automatic patient‐specific quality assurance (PSQA) is recently explored using artificial intelligence approaches, and several studies reported the development of machine learning models for predicting the gamma pass rate (GPR) index only. Purpose: To develop a novel deep learning approach using a generative adversarial network (GAN) to predict the synthetic measured fluence. Methods and materials: A novel training method called "dual training," which involves the training of the encoder and decoder separately, was proposed and evaluated for cycle GAN (cycle‐GAN) and conditional GAN (c‐GAN). A total of 164 VMAT treatment plans, including 344 arcs (training data: 262, validation data: 30, and testing data: 52) from various treatment sites, were selected for prediction model development. For each patient, portal‐dose‐image‐prediction fluence from TPS was used as input, and measured fluence from EPID was used as output/response for model training. Predicted GPR was derived by comparing the TPS fluence with the synthetic measured fluence generated by the DL models using gamma evaluation of criteria 2%/2 mm. The performance of dual training was compared against the traditional single‐training approach. In addition, we also developed a separate classification model specifically designed to detect automatically three types of errors (rotational, translational, and MU‐scale) in the synthetic EPID‐measured fluence. Results: Overall, the dual training improved the prediction accuracy of both cycle‐GAN and c‐GAN. Predicted GPR results of single training were within 3% for 71.2% and 78.8% of test cases for cycle‐GAN and c‐GAN, respectively. Moreover, similar results for dual training were 82.7% and 88.5% for cycle‐GAN and c‐GAN, respectively. The error detection model showed high classification accuracy (>98%) for detecting errors related to rotational and translational errors. However, it struggled to differentiate the fluences with "MU scale error" from "error‐free" fluences. Conclusion: We developed a method to automatically generate the synthetic measured fluence and identify errors within them. The proposed dual training improved the PSQA prediction accuracy of both the GAN models, with c‐GAN demonstrating superior performance over the cycle‐GAN. Our results indicate that the c‐GAN with dual training approach combined with error detection model, can accurately generate the synthetic measured fluence for VMAT PSQA and identify the errors. This approach has the potential to pave the way for virtual patient‐specific QA of VMAT treatments. [ABSTRACT FROM AUTHOR]

Background: In recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. Purpose: To develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. Methods: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5‐point scale (5, acceptable as‐is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. Results: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel‐wise dose difference was −0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D1%${D}_{1{\mathrm{\% }}}$ and D98%${D}_{98{\mathrm{\% }}}$ were −1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were −0.30 ± 1.66 Gy and −0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. Conclusions: Deep‐learning dose prediction can be used to predict high‐quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization. [ABSTRACT FROM AUTHOR]

Background: Independent dosimetry audits are an important intervention in radiotherapy for quality assurance. Electron beams, used for superficial radiotherapy treatments, must also be tested in dosimetry audits as part of a good quality assurance program to help prevent clinical errors. Purpose: To establish a new service for IAEA/WHO postal dosimetry audits in electron beams using RPL dosimeters. Methods: A novel postal audit methodology employing a PMMA holder system for RPLDs was developed. The associated correction factors including holder dependence, energy dependence, dose response non‐linearity, and fading were obtained and tested in a multi‐center (n = 12) pilot study. A measurement uncertainty budget was estimated and employed in analyzing the irradiated dosimeters. Results: Holder and energy correction factors ranged from 1.004 to 1.010 and 1.019 to 1.059 respectively across the energy range. The non‐linearity and fading correction models used for photon beams were tested in electron beams and did not significantly increase measurement uncertainty. The mean dose ratio ± SD of the multi‐center study was 1.001 ± 0.011. The overall uncertainty budget was estimated as ± 1.42% (k = 1). Conclusions: A methodology for IAEA/WHO postal dosimetry audits in electron beams was developed and validated in a multi‐center study and is now made available to radiotherapy centers as a routine service. [ABSTRACT FROM AUTHOR]

Background: Pulsed wave Doppler ultrasound is a useful modality for assessing vascular health as it quantifies blood flow characteristics. To facilitate accurate diagnosis, accuracy and consistency of this modality should be assessed through Doppler quality assurance (QA). Purpose: The purpose of this study was to characterize the accuracy, reproducibility, and inter‐scanner variability of ultrasound flow velocity measurements via a flow phantom, with a focus on the effect of systematic acquisition parameters on measured flow velocity accuracy. Methods: Using a manufacturer‐calibrated flow phantom, pulsed wave measurements were acquired on five clinical systems (iU22, Philips) with three models of transducers, including both linear and curvilinear models. The peak and mean flow velocities were estimated by vendor‐supplied spectral analysis tools. To investigate intra‐ and inter‐scanner variability, measurements were repeated using each scanner‐transducer pair under a standardized set of conditions. Inter‐scanner variability was assessed using ANOVA. Flow velocity accuracy was investigated by mean absolute percentage error. The impacts of receive gain, measurement depth, and beam steering on measured flow velocity accuracy were examined by varying each parameter over its available range and comparing to the ground truth flow velocity. Results: Inter‐scanner variability was statistically significant for peak flow measurements made using both linear and curvilinear transducers, though absolute differences in measured velocity were small. Inter‐scanner variability was not statistically significant for mean flow velocity. Receive gain, measurement depth, and beam steering were all found to impact the accuracy of measured flow characteristics for linear transducers. Accuracy of the flow measurements made with the curvilinear transducer demonstrated high consistency to changes in receive gain at a constant depth, though were impacted by increasing the measurement depth. Conclusions: Carefully and consistently selected acquisition and set‐up parameters are essential in order to establish a reliable and meaningful QA program. [ABSTRACT FROM AUTHOR]

Background: Inaccurate manual organ delineation is one of the high‐risk failure modes in radiation treatment. Numerous automated contour quality assurance (QA) systems have been developed to assess contour acceptability; however, manual inspection of flagged cases is a time‐consuming and challenging process, and can lead to users overlooking the exact error location. Purpose: Our aim is to develop and validate a contour QA system that can effectively detect and visualize subregional contour errors, both qualitatively and quantitatively. Methods/Materials: A novel contour subregion error detection (CSED) system was developed using subregional surface distance discrepancies between manual and deep learning auto‐segmentation (DLAS) contours. A validation study was conducted using a head and neck public dataset containing 339 cases and evaluated according to knowledge‐based pass criteria derived from a clinical training dataset of 60 cases. A blind qualitative evaluation was conducted, comparing the results from the CSED system with manual labels. Subsequently, the CSED‐flagged cases were re‐examined by a radiation oncologist. Results: The CSED system could visualize the diverse types of subregional contour errors qualitatively and quantitatively. In the validation dataset, the CSED system resulted in true positive rates (TPR) of 0.814, 0.800, and 0.771; false positive rates (FPR) of 0.310, 0.267, and 0.298; and accuracies of 0.735, 0.759, and 0.730, for brainstem and left and right parotid contours, respectively. The CSED‐assisted manual review caught 13 brainstem, 19 left parotid, and 21 right parotid contour errors missed by conventional human review. The TPR/FPR/accuracy of the CSED‐assisted manual review improved to 0.836/0.253/0.784, 0.831/0.171/0.830, and 0.808/0.193/0.807 for each structure, respectively. Further, the time savings achieved through CSED‐assisted review improved by 75%, with the time for review taking 24.81 ± 12.84, 26.75 ± 10.41, and 28.71 ± 13.72 s for each structure, respectively. Conclusions: The CSED system enables qualitative and quantitative detection, localization, and visualization of manual segmentation subregional errors utilizing DLAS contours as references. The use of this system has been shown to help reduce the risk of high‐risk failure modes resulting from inaccurate organ segmentation. [ABSTRACT FROM AUTHOR]

Background: Quality assurance (QA) is a prerequisite for safe and accurate pencil‐beam proton therapy. Conventional measurement‐based patient‐specific QA (pQA) can only verify limited aspects of patient treatment and is labor‐intensive. Thus, a better method is needed to ensure the integrity of the treatment plan. Purpose: Line scanning, which involves continuous and rapid delivery of pencil beams, is a state‐of‐the‐art proton therapy technique. Machine performance in delivering scanning protons is dependent on the complexity of the beam modulations. Moreover, it contributes to patient treatment accuracy. A Monte Carlo (MC) simulation‐based QA method that reflects the uncertainty related to the machine during scanning beam delivery was developed and verified for clinical applications to pQA. Methods: Herein, a tool for particle simulation (TOPAS) for nozzle modeling was used, and the code was commissioned against the measurements. To acquire the beam delivery uncertainty for each plan, patient plans were delivered. Furthermore, log files recorded every 60 μs by the monitors downstream of the nozzle were exported from the treatment control system. The spot positions and monitor unit (MU) counts in the log files were converted to dipole magnet strengths and number of particles, respectively, and entered into the TOPAS. For the 68 clinical cases, MC simulations were performed in a solid water phantom, and two‐dimensional (2D) absolute dose distributions at 20‐mm depth were measured using an ionization chamber array (Octavius 1500, PTW, Freiburg, Germany). Consequently, the MC‐simulated 2D dose distributions were compared with the measured data, and the dose distributions in the pre‐treatment QA plan created with RayStation (RaySearch Laboratories, Stockholm, Sweden). Absolute dose comparisons were made using gamma analysis with 3%/3 mm and 2%/2 mm criteria for 47 clinical cases without considering daily machine output variation in the MC simulation and 21 cases with daily output variation, respectively. All cases were analyzed with 90% or 95% of passing rate thresholds. Results: For 47 clinical cases not considering daily output variations, the absolute gamma passing rates compared with the pre‐treatment QA plan were 99.71% and 96.97%, and the standard deviations (SD) were 0.70% and 3.78% with the 3%/3 mm or 2%/2 mm criteria, respectively. Compared with the measurements, the passing rate of 2%/2 mm gamma criterion was 96.76% with 3.99% of SD. For the 21 clinical cases compared with pre‐treatment QA plan data and measurements considering daily output variations, the 2%/2 mm absolute gamma analysis result was 98.52% with 1.43% of SD and 97.67% with 2.72% of SD, respectively. With a 95% passing rate threshold of 2%/2 mm criterion, the false‐positive and false‐negative were 21.8% and 8.3% for without and with considering output variation, respectively. With a 90% threshold, the false‐positive and false‐negative reduced to 11.4% and 0% for without and with considering output variation, respectively. Conclusions: A log‐file‐based MC simulation method for patient QA of line‐scanning proton therapy was successfully developed. The proposed method exhibited clinically acceptable accuracy, thereby exhibiting a potential to replace the measurement‐based dosimetry QA method with a 90% gamma passing rate threshold when applying the 2%/2 mm criterion. [ABSTRACT FROM AUTHOR]

Background: Many studies have utilized optical camera systems with volumetric scintillators for quality assurances (QA) to estimate the proton beam range. However, previous analytically driven range estimation methods have the difficulty to derive the dose distributions from the scintillation images with quenching and optical effects. Purpose: In this study, a deep learning method utilized to QA was used to predict the beam range and spread‐out Bragg peak (SOBP) for two‐dimensional (2D) map conversion from the scintillation light distribution (LD) into the dose distribution in a water phantom. Methods: The 2D residual U‐net modeling for deep learning was used to predict the 2D water dose map from a 2D scintillation LD map. Monte Carlo simulations for dataset preparation were performed with varying monoenergetic proton beam energies, field sizes, and beam axis shifts. The LD was reconstructed using photons backpropagated from the aperture as a virtual lens. The SOBP samples were constructed based on monoenergetic dose distributions. The training set, including the validation set, consisted of 8659 image pairs of LD and water dose maps. After training, dose map prediction was performed using a 300 image pair test set generated under random conditions. The pairs of simulated and predicted dose maps were analyzed by Bragg peak fitting and gamma index maps to evaluate the model prediction. Result: The estimated beam range and SOBP width resolutions were 0.02 and 0.19 mm respectively for varying beam conditions, and the beam range and SOBP width deviations from the reference simulation result were less than 0.1 and 0.8 mm respectively. The simulated and predicted distributions showed good agreement in the gamma analysis, except for rare cases with failed gamma indices in the proximal and field‐marginal regions. Conclusion: The deep learning conversion method using scintillation LDs in an optical camera system with a scintillator is feasible for estimating proton beam range and SOBP width with high accuracy. [ABSTRACT FROM AUTHOR]

Background: Deformable image registration (DIR)‐based dose accumulation (DDA) is regularly used in adaptive radiotherapy research. However, the applicability and reliability of DDA for direct clinical usage are still being debated. One primary concern is the validity of DDA, particularly for scenarios with substantial anatomical changes, for which energy‐conservation problems were observed in conceptual studies. Purpose: We present and validate an energy‐conservation (EC)‐based DDA validation workflow and further investigate its usefulness for actual patient data, specifically for lung cancer cases. Methods: For five non‐small cell lung cancer (NSCLC) patients, DDA based on five selective DIR methods were calculated for five different treatment plans, which include one intensity‐modulated photon therapy (IMRT), two intensity‐modulated proton therapy (IMPT), and two combined proton‐photon therapy (CPPT) plans. All plans were optimized on the planning CT (planCT) acquired in deep inspiration breath‐hold (DIBH) and were re‐optimized on the repeated DIBH CTs of three later fractions. The resulting fractional doses were warped back to the planCT using each DIR. An EC‐based validation of the accumulation process was implemented and applied to all DDA results. Correlations between relative organ mass/volume variations and the extent of EC violation were then studied using Bayesian linear regression (BLR). Results: For most OARs, EC violation within 10% is observed. However, for the PTVs and GTVs with substantial regression, severe overestimation of the fractional energy was found regardless of treatment type and applied DIR method. BLR results show that EC violation is linearly correlated to the relative mass variation (R^2 > 0.95) and volume variation (R^2 > 0.60). Conclusion: DDA results should be used with caution in regions with high mass/volume variation for intensity‐based DIRs. EC‐based validation is a useful approach to provide patient‐specific quality assurance of the validity of DDA in radiotherapy. [ABSTRACT FROM AUTHOR]

Background: Patient‐specific QA verification ensures patient safety and treatment by verifying radiation delivery and dose calculations in treatment plans for errors. However, a two‐dimensional (2D) dose distribution is insufficient for detecting information on the three‐dimensional (3D) dose delivered to the patient. In addition, 3D radiochromic plastic dosimeters (RPDs) such as PRESAGE® represent the volume effect in which the dosimeters have different sensitivities according to the size of the dosimeters. Therefore, to solve the volume effect, a Quasi‐3D dosimetry system was proposed to perform patient‐specific QA using predetermined‐sized and multiple RPDs. Purpose: For patient‐specific quality assurance (QA) in radiation treatment, this study aims to assess a quasi‐3D dosimetry system using an RPD. Methods: Gamma analysis was performed to verify the agreement between the measured and estimated dose distributions of intensity‐modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). We fabricated cylindrical RPDs and a quasi‐3D dosimetry phantom. A practicability test for a pancreatic patient utilized a quasi‐3D dosimetry device, an in‐house RPD, and a quasi‐3D phantom. The dose distribution of the VMAT design dictated the placement of nine RPDs. Moreover, a 2D diode array detector was used for 2D gamma analysis (MapCHECK2). The patient‐specific QA was performed for IMRT, VMAT, and stereotactic ablative radiotherapy (SABR) in 20 prostate and head‐and‐neck patients. For each patient, six RPDs were positioned according to the dose distribution. VMAT SABR and IMRT/VMAT plans employed a 2%/2 mm gamma criterion, whereas IMRT/VMAT plans used a 3%/2 mm gamma criterion, a 10% threshold value, and a 90% passing rate tolerance. 3D gamma analysis was conducted using the 3D Slicer software. Results: The average gamma passing rates with 2%/2 mm and 3%/3 mm criteria for relative dose distribution were 91.6% ± 1.4% and 99.4% ± 0.7% for the 3D gamma analysis using the quasi‐3D dosimetry system, respectively, and 97.5% and 99.3% for 2D gamma analysis using MapCHECK2, respectively. The 3D gamma analysis for patient‐specific QA of 20 patients showed passing rates of over 90% with 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria. Conclusions: The quasi‐3D dosimetry system was evaluated by performing patient‐specific QAs with RPDs and quasi‐3D phantom. The gamma indices for all RPDs showed more than 90% for 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria. We verified the feasibility of a quasi‐3D dosimetry system by performing the conventional patient‐specific QA with the quasi‐3D dosimeters. [ABSTRACT FROM AUTHOR]

Background: Clinical evidence has demonstrated that proton therapy can achieve comparable tumor control probabilities compared to conventional photon therapy but with the added benefit of sparing healthy tissues. However, proton therapy is sensitive to inter‐fractional anatomy changes. Online pre‐fraction evaluation can effectively verify proton dose before delivery to patients, but there is a lack of guidelines for implementing this workflow. Purpose: The purpose of this study is to develop a cone‐beam CT‐based (CBCT) online evaluation framework for proton therapy that enables knowledge transparency and evaluates the efficiency and accuracy of each essential component. Methods: Twenty‐three patients with various lesion sites were included to conduct a retrospective study of implementing the proposed CBCT evaluation framework for the clinic. The framework was implemented on the RayStation 11B Research platform. Two synthetic CT (sCT) methods, corrected CBCT (cCBCT), and virtual CT (vCT), were used, and the ground truth images were acquired from the same‐day deformed quality assurance CT (dQACT) for the comparisons. The evaluation metrics for the framework include time efficiency, dose‐difference distributions (gamma passing rates), and water equivalent thickness (WET) distributions. Results: The mean online CBCT evaluation times were 1.6 ± 0.3 min and 1.9 ± 0.4 min using cCBCT and vCT, respectively. The dose calculation and deformable image registration dominated the evaluation efficiency, and accounted for 33% and 30% of the total evaluation time, respectively. The sCT generation took another 19% of the total time. Gamma passing rates were greater than 91% and 97% using 1%/1 mm and 2%/2 mm criteria, respectively. When the appropriate sCT was chosen, the target mean WET difference from the reference were less than 0.5 mm. The appropriate sCT method choice determined the uncertainty for the framework, with the cCBCT being superior for head‐and‐neck patient evaluation and vCT being better for lung patient evaluation. Conclusions: An online CBCT evaluation framework was proposed to identify the use of the optimal sCT algorithm regarding efficiency and dosimetry accuracy. The framework is extendable to adopt advanced imaging methods and has the potential to support online adaptive radiotherapy to enhance patient benefits. It could be implemented into clinical use in the future. [ABSTRACT FROM AUTHOR]

Background: Many commercial tools are available for plan‐specific quality assurance (QA) of radiotherapy plans, with their functionality assessed in isolation. However, multiple QA tools are required to review the full range of potential errors. It is important to assess their effectiveness in combination with each other to look for ways to both streamline the QA process and to make certain that errors of high impact and/or high occurrence are caught before reaching patient treatment. Purpose: To develop a structured method to assess the effective risk reduction of combinations of QA methods for IMRT/VMAT treatments. Methods: First, a structured prospective risk assessment was performed to establish the major failure modes (FMs) of IMRT/VMAT QA, and assign occurrence (O), severity (S), and baseline detectability (BD) rankings to them. The baseline assumed that chart checks and linear accelerator QA was performed, but no plan‐specific secondary dose calculation or measurement was done. Second, the detectability of each FM for two secondary dose calculation methods and four plan measurement methods (point‐based dose calculation, Monte‐Carlo‐based dose calculation, 2D fluence‐based measurement, 2.5D phantom‐based measurement, log file analysis with dose recalculation, and log file analysis combined with MLC QA) was determined. Third, we used a minimum detectability approach in addition to each FM's occurrence and severity to determine the optimal combination of QA methods. We analyzed the cumulative risk priority number of eight combinations of QA methods. The analysis was done on (1) all FMs, (2) FMs with high severity, (3) FMs with high‐risk priority numbers (RPN) of O*S*BD, and (4) on FMs with both high severity and high RPN. Results: Our analysis resulted in 54 FMs, including commissioning, planning, data transfer, and linear accelerator failures. 1D secondary dose calculation plus measurement provided a 19%–22% risk reduction from baseline. 1D/3D secondary dose calculation plus log files created a 25%–32% reduction. 3D secondary dose calculation plus measurement resulted in a 27%–34% reduction. 3D secondary dose calculation plus log files with additional machine QA provided the greatest reduction of 31%–42%. Conclusion: This novel structured approach to comparing combinations of QA methods will allow us to optimize our procedures, with the goal of detecting all clinically significant FMs. Our results show that log‐file QA with 3D dose recalculation and supplemental machine QA provides better risk reduction than measurement‐based QA. This work builds evidence to justify reducing or eliminating measurement‐based PSQA with an independent 3D dose verification, log‐file measurement, and appropriate supplementation of machine QA. The process also highlights FMs that cannot be caught by pre‐treatment QA, prompting us to consider future directions for on‐treatment QA. [ABSTRACT FROM AUTHOR]

Background: Electromagnetic tracking (EMT) is a promising technology that holds great potential to advance patient‐specific pre‐treatment verification in interstitial brachytherapy (iBT). It allows easy determination of the implant geometry without line‐of‐sight restrictions and without dose exposure to the patient. What it cannot provide, however, is a link to anatomical landmarks, such as the exit points of catheters or needles on the skin surface. These landmarks are required for the registration of EMT data with other imaging modalities and for the detection of treatment errors such as incorrect indexer lengths, and catheter or needle shifts. Purpose: To develop an easily applicable method to detect reference points in the positional data of the trajectory of an EMT sensor, specifically the exit points of catheters in breast iBT, and to apply the approach to pre‐treatment error detection. Methods: Small metal objects were attached to catheter fixation buttons that rest against the breast surface to intentionally induce a local, spatially limited perturbation of the magnetic field on which the working principle of EMT relies. This perturbation can be sensed by the EMT sensor as it passes by, allowing it to localize the metal object and thus the catheter exit point. For the proof‐of‐concept, different small metal objects (magnets, washers, and bushes) and EMT sensor drive speeds were used to find the optimal parameters. The approach was then applied to treatment error detection and validated in‐vitro on a phantom. Lastly, the in‐vivo feasibility of the approach was tested on a patient cohort of four patients to assess the impact on the clinical workflow. Results: All investigated metal objects were able to measurably perturb the magnetic field, which resulted in missing sensor readings, that is two data gaps, one for the sensor moving towards the tip end and one when retracting from there. The size of the resulting data gaps varied depending on the choice of gap points used for calculation of the gap size; it was found that the start points of the gaps in both directions showed the smallest variability. The median size of data gaps was ⩽8 mm for all tested materials and sensor drive speeds. The variability of the determined object position was ⩽0.5 mm at a speed of 1.0 cm/s and ⩽0.7 mm at 2.5 cm/s, with an increase up to 2.3 mm at 5.0 cm/s. The in‐vitro validation of the error detection yielded a 100% detection rate for catheter shifts of ≥2.2 mm. All simulated wrong indexer lengths were correctly identified. The in‐vivo feasibility assessment showed that the metal objects did not interfere with the routine clinical workflow. Conclusions: The developed approach was able to successfully detect reference points in EMT data, which can be used for registration to other imaging modalities, but also for treatment error detection. It can thus advance the automation of patient‐specific, pre‐treatment quality assurance in iBT. [ABSTRACT FROM AUTHOR]

Background: Deformable image registration is increasingly used in radiotherapy to adapt the treatment plan and accumulate the delivered dose. Consequently, clinical workflows using deformable image registration require quick and reliable quality assurance to accept registrations. Additionally, for online adaptive radiotherapy, quality assurance without the need for an operator to delineate contours while the patient is on the treatment table is needed. Established quality assurance criteria such as the Dice similarity coefficient or Hausdorff distance lack these qualities and also display a limited sensitivity to registration errors beyond soft tissue boundaries. Purpose: The purpose of this study is to investigate the existing intensity‐based quality assurance criteria structural similarity and normalized mutual information for their ability to quickly and reliably identify registration errors for (online) adaptive radiotherapy and compare them to contour‐based quality assurance criteria. Methods: All criteria were tested using synthetic and simulated biomechanical deformations of 3D MR images as well as manually annotated 4D CT data. The quality assurance criteria were scored for classification performance, for their ability to predict the registration error, and for their spatial information. Results: We found that besides being fast and operator‐independent, the intensity‐based criteria have the highest area under the receiver operating characteristic curve and provide the best input for models to predict the registration error on all data sets. Structural similarity furthermore provides spatial information with a higher gamma pass rate of the predicted registration error than commonly used spatial quality assurance criteria. Conclusions: Intensity‐based quality assurance criteria can provide the required confidence in decisions about using mono‐modal registrations in clinical workflows. They thereby enable automated quality assurance for deformable image registration in adaptive radiotherapy treatments. [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging scanner faults can be missed during routine quality assurance (QA) if they are subtle, intermittent, or the test being performed is insensitive to the type of fault. Coil element malfunction is a common fault within MRI scanners, which may go undetected for quite some time. Consequently, this may lead to poor image quality and the potential for misdiagnoses. Purpose: Daily QA typically consists of an automated signal to noise ratio test and in some instances this test is insensitive to coil element malfunction. Instead of relying on daily QA testing, it was proposed to utilize patient images in conjunction with a trained neural network to detect coil element malfunction, even when it presents as a very subtle defect. The advantage to using patient images over phantom testing is real‐time monitoring can be achieved. This allows clinical staff to focus more on patient throughput without being burdened by daily testing. Methods: A neural network was trained using simulated coil failure in 3958 abdominal or pelvic images from 497 patients. The accuracy of the trained network was then tested on an unseen dataset of 109 images from which 44 patients which had coil element malfunction present. Five MRI radiographers were shown 249 images with and without real coil malfunction to assess their accuracy compared to the neural network in identifying the scanner fault. Results: The neural network achieved an accuracy of 91.74% in identifying coil element malfunction in the unseen data. Radiographers tasked with identifying coil element malfunction had an average accuracy of 59.99%. In the same test case, the neural network outperformed all radiographers with an accuracy of 91.56%. Conclusion: This work demonstrates that neural networks trained with artificial data can successfully identify MRI scanner coil element malfunction in clinical images. The method provided better accuracy than MRI radiographers (technologists) at identifying coil element malfunction and highlights the potential utility of AI methods as an alternative to support traditional QA. Further, our methodology of training neural networks with simulated data could potentially identify other faults, allowing centers to produce robust fault detection systems with minimal data. [ABSTRACT FROM AUTHOR]

Background: Projection imaging phantoms are often optimized for 2‐dimensional image characteristics in homogeneous backgrounds. Therefore, evaluation of image quality in tomosynthesis (DBT) lacks accepted and established phantoms. Purpose: We describe a 3D breast phantom with a structured, variable background. The phantom is an adaptable and advanced version of the L1 phantom by Cockmartin et al. Phantom design and its use for quality assurance measurements for DBT devices are described. Four phantoms were compared to assess the objectivity. Methods: The container size was increased to a diameter of 24 cm and a total height of 53.5 mm. Spiculated masses were replaced by five additional non‐spiculated masses for higher granularity in threshold diameter resolution. These patterns are adjustable to the imaging device. The masses were printed in one session with a base layer using two‐component 3D printing. New materials compared to the L1 phantom improved the attenuation difference between the lesion models and the background. Four phantoms were built and intra‐human observer, inter‐human observer and inter‐phantom variations were determined. The latter assess the reproducibility of the phantom production. Coefficients of variance (V) were calculated for all three variations. Results: The difference of the attenuation coefficients between the lesion models and the background was 0.20 cm−1 (with W/Al at 32 kV, equivalent to 19–20 keV effective energy) compared to 0.21 cm−1 for 50/50 glandular/adipose breast tissue and cancerous lesions. PMMA equivalent thickness of the phantom was 47.0 mm for the Siemens Mammomat Revelation. For the masses, the Vintra$V_{intra}$ for the intra‐observer variation was 0.248, the averaged inter‐observer variation, V¯inter$\overline{V}_{inter}$ was 0.383. Vphantom$V_{phantom}$ for phantom variance was 0.321. For the micro‐calcifications, Vintra$V_{intra}$ was 0.0429, V¯inter=$\overline{V}_{inter}=$ 0.0731 and Vphantom=$V_{phantom}=$ 0.0759. Conclusions: Position, orientation and shape of the masses are reproducible and attenuation differences appropriate. The phantom presented proved to be a candidate test object for quality control. [ABSTRACT FROM AUTHOR]

Background: The production of individualized anthropomorphic phantoms via three‐dimensional (3D) printing methods offers promising possibilities to assess and optimize radiation exposures for specifically relevant patient groups (i.e., overweighed or pregnant persons) that are not adequately represented by standardized anthropomorphic phantoms. However, the equivalence of printed phantoms must be demonstrated exemplarily with respect to the resulting image contrasts and dose distributions. Purpose: To reproduce a conventionally produced anthropomorphic phantom of a female chest and breasts and to evaluate their equivalence with respect to image contrasts and absorbed doses at the example of a computed tomography (CT) examination of the chest. Methods: In a first step, the effect of different print settings on the CT values of printed samples was systematically investigated. Subsequently, a transversal slice and breast add‐ons of a conventionally produced female body phantom were reproduced using a multi‐material extrusion‐based printer, considering six different types of tissues (muscle, lung, adipose, and glandular breast tissue, as well as bone and cartilage). CT images of the printed and conventionally produced phantom parts were evaluated with respect to their geometric correspondence, image contrasts, and absorbed doses measured using thermoluminescent dosimeters. Results: CT values of printed objects are highly sensitive to the selected print settings. The soft tissues of the conventionally produced phantom could be reproduced with a good agreement. Minor differences in CT values were observed for bone and lung tissue, whereas absorbed doses to the relevant tissues were identical within the measurement uncertainties. Conclusion: 3D‐printed phantoms are with exception of minor contrast differences equivalent to their conventionally manufactured counterparts. When comparing the two production techniques, it is important to note that conventionally manufactured phantoms should not be considered as absolute benchmarks, as they also only approximate the human body in terms of its absorption, and attenuation of x‐rays as well as its geometry. [ABSTRACT FROM AUTHOR]

Background: Mechanical accuracy should be verified before implementing a proton stereotactic radiosurgery (SRS) program. Linear accelerator (Linac)‐based SRS systems often use electronic portal imaging devices (EPIDs) to verify beam isocentricity. Because proton therapy systems do not have EPID, beam isocentricity tests of proton SRS may still rely on films, which are not efficient. Purpose: To validate that our proton SRS system meets mechanical precision requirements and to present an efficient method to evaluate the couch and gantry's rotational isocentricity for our proton SRS system. Methods: A dedicated applicator to hold brass aperture for proton SRS system was designed. The mechanical precision of the system was tested using a metal ball and film for 11 combinations of gantry and couch angles. A more efficient quality assurance (QA) procedure was developed, which used a scintillator device to replace the film. The couch rotational isocentricity tests were performed using orthogonal kV x‐rays with the couch rotated isocentrically to five positions (0°, 315°, 270°, 225°, and 180°). At each couch position, the distance between the metal ball in kV images and the imaging isocenter was measured. The gantry isocentricity tests were performed using a cone‐shaped scintillator and proton beams at five gantry angles (0°, 45°, 90°, 135°, and 180°), and the isocenter position and the distance of each beam path to the isocenter were obtained. Daily QA procedure was performed for 1 month to test the robustness and reproducibility of the procedure. Results: The gantry and couch rotational isocentricity exhibited sub‐mm precision, with most measurements within ±0.5 mm. The 1‐month QA results showed that the procedure was robust and highly reproducible to within ±0.2 mm. The gantry isocentricity test using the cone‐shaped scintillator was accurate and sensitive to variations of ±0.2 mm. The QA procedure was efficient enough to be completed within 30 min. The 1‐month isocentricity position variations were within 0.5 mm, which demonstrating that the overall proton SRS system was stable and precise. Conclusion: The proton SRS Winston–Lutz QA procedure using a cone‐shaped scintillator was efficient and robust. We were able to verify radiation delivery could be performed with sub‐mm mechanical precision. [ABSTRACT FROM AUTHOR]

Background: While many have speculated on the reasons for gamma comparison insensitivity for patient‐specific quality assurance analysis, the true reasons for insensitivity have not yet been elucidated. Failing to understand the reasons for this technique's insensitivity limits our ability to either improve the gamma metric to increase sensitivity of the comparison or the capacity to develop new comparison techniques that circumvent the limitations of the gamma comparison. Purpose: To understand the underlying cause(s) for gamma comparison insensitivity and determine if simple plan characteristics can quantitatively predict for gamma comparison sensitivity. Methods: Known MLC and MU errors of varying magnitudes were induced on simple test fields to preliminarily investigate where gamma failures first begin to appear as error magnitude is increased. Gamma value maps between error‐induced plan calculations and error‐free plan calculations were created for 20 IMRT and 20 VMAT cases, each on three different detector geometries—ArcCHECK, MapCHECK, and Delta4. Gamma value maps were qualitatively compared to dose‐gradient maps, and quantitative comparisons were performed between various plan descriptors and the computed gamma sensitivity for five different classes of induced errors were utilized to determine if any plan descriptor could predict the gamma sensitivity on a case‐by‐case basis. All comparisons were performed in a calculation‐only scenario to remove uncertainties introduced by comparisons made with real patient specific QA measurements. Results: Gamma value maps with increasing induced error magnitude illustrated that gamma comparisons fail first in high‐dose, low‐gradient regions of the field. Conversely, in areas of high gradient, gamma values typically remain low, even in the presence of large errors, regardless of detector geometry and gamma normalization setting. Thus, the complex, and often overlapping, high dose gradients in plans appear to be a limiting factor in gamma comparison sensitivity as the number of points along these gradients may often outnumber the points available for failing the comparison in lower gradient regions of the field. None of the simple plan descriptors studied were able to quantitively predict gamma comparison sensitivity, suggesting that quantitatively predicting the sensitivity of gamma comparisons on a case‐by‐case basis may require a combination of multiple factors or metrics not studied here. Conclusions: Simple plan descriptors and the number of points in high‐dose, low‐gradient regions of the field did not quantitively predict for gamma comparison sensitivity. However, it is clear from gradient and gamma value maps that gamma comparisons fail first in high‐dose, low‐gradient regions of the field in the presence of known induced errors, which we have shown to be independent of detector geometry and gamma comparison normalization setting. Gamma comparison sensitivity is thus limited by the ever‐increasing complexity of plans and is particularly important to consider as treatment volumes become smaller and the complexity of overlapping plan gradients increases. This suggests that new methods for patient‐specific QA comparisons are required to circumvent this limitation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning‐based auto‐segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics. Purpose: The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors. Methods and materials: DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were determined from the comparisons between the two segmentation approaches. Feature selection was performed to optimize the training of a machine learning classification model to identify potential contouring errors. A public dataset with 339 cases was used to train and test the classifier. Four independent classifiers were trained using five‐fold cross validation, and the predictions from each classifier were ensembled using soft voting. The trained model was validated on a held‐out testing dataset. An additional independent clinical dataset with 60 cases was used to test the generalizability of the model. Model predictions were reviewed by an expert to confirm or reject the findings. Results: The proposed machine learning multiple features (ML‐MF) approach outperformed traditional nonmachine‐learning‐based approaches that are based on only one or two geometric agreement metrics. The machine learning model achieved recall (precision) values of 0.842 (0.899), 0.762 (0.762), 0.727 (0.842), and 0.773 (0.773) for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively compared to 0.526 (0.909), 0.619 (0.765), 0.682 (0.882), 0.773 (0.568) for an approach based solely on Dice similarity coefficient values. In the external validation dataset, 66.7, 93.3, 94.1, and 58.8% of flagged cases were confirmed to have contouring errors by an expert for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively. Conclusions: The proposed ML‐MF approach, which includes multiple geometric agreement metrics to flag manual contouring errors, demonstrated superior performance in comparison to traditional methods. This method is easy to implement in clinical practice and can help to reduce the significant time and labor costs associated with manual segmentation and review. [ABSTRACT FROM AUTHOR]

Background: Acceptance testing and quality assurance (QA) of computed tomography (CT) scans are of great importance to ensure the appropriate performance of the systems. However, current standards and guidelines do not include a dedicated QA program for spectral photon‐counting CT (SPCCT), nor adapted tolerance levels. Purpose: To evaluate the technical performance, in terms of image quality and radiation dose, of the first point‐of‐care SPCCT for the upper extremities (MARS Extremity 5X120, MARS Bioimaging Ltd., Christchurch, New Zealand) and to establish a comprehensive QA program. Methods: The specific dimensions of the scanner with a 125 mm diameter gantry and a small voxel size of 0.1 × 0.1 × 0.1 mm3 require the use of suitable phantoms and evaluation techniques. Indicators such as CT number accuracy, image noise, uniformity, and slice thickness were assessed to characterize the image quality. The in‐plane and longitudinal spatial resolutions were evaluated by means of the modulation transfer function (MTF). Noise power spectra (NPS) were calculated to further evaluate the image noise. Material identification capabilities were assessed using clinically relevant high‐Z materials (iodine, gold, gadolinium, and calcium). A 100‐mm diameter CTDI‐like phantom was used to measure the dose indices. A complete radiation survey was carried out to measure the radiation exposure at different points around the scanner. Results: The proposed QA program is based on international and local recommendations as well as practical experience. It includes standardised CT tests and SPCCT‐specific methods. Additional methodologies to further assess the system performance are also presented. Tolerance levels are discussed and revised when appropriate. Both in‐plane and longitudinal high spatial resolutions were evidenced by the MTF measurements with 1.8 lp· mm−1 and 5.0 lp· mm−1 at 10%, respectively. The calculated effective slice thickness ranged between 0.15 and 0.16 mm for the five energy bins and for a reconstructed voxel size of 0.1 × 0.1 × 0.1 mm3. Reference values of the linear attenuation coefficient of water have been calculated and used to assess the CT number uniformity of water. Evaluation of the CT number accuracy and stability of various clinically relevant materials showed excellent spectral correlation and linearity between HU values and concentrations (r2 > 0.99). The NPS showed less noise correlation between slices than within transverse slice, as well as a systematic increase at low spatial frequencies. The volume CT dose index (CTDIvol$_{vol}$) for a custom‐made 100 mm diameter phantom was 9.32 mGy. Radiation measurements around the scanner showed that it is completely shielded except for the access port, and that no additional protective measures are necessary for the patient. Conclusions: A routine QA framework for SPCCT systems has been proposed. Image quality and radiation dose were assessed using newly designed phantoms, relevant metrics, and automated algorithms. Baseline values were established and tolerance levels discussed for the MARS SPCCT scanner based on collected data and international recommendations. [ABSTRACT FROM AUTHOR]

Background: Linear accelerator (Linac) beam data commissioning and quality assurance (QA) play a vital role in accurate radiation treatment delivery and entail a large number of measurements using a variety of field sizes. How to optimize the effort in data acquisition while maintaining high quality of medical physics practice has been sought after. Purpose: We propose to model Linac beam data through implicit neural representation (NeRP) learning. The potential of the beam model in predicting beam data from sparse measurements and detecting data collection errors was evaluated, with the goal of using the beam model to verify beam data collection accuracy and simplify the commissioning and QA process. Materials and Methods: NeRP models with continuous and differentiable functions parameterized by multilayer perceptrons (MLPs) were used to represent various beam data including percentage depth dose (PDD) and profiles of 6 MV beams with and without flattening filter. Prior knowledge of the beam data was embedded into the MLP network by learning the NeRP of a vendor‐provided "golden" beam dataset. The prior‐embedded network was then trained to fit clinical beam data collected at one field size and used to predict beam data at other field sizes. We evaluated the prediction accuracy by comparing network‐predicted beam data to water tank measurements collected from 14 clinical Linacs. Beam datasets with intentionally introduced errors were used to investigate the potential use of the NeRP model for beam data verification, by evaluating the model performance when trained with erroneous beam data samples. Results: Linac beam data predicted by the model agreed well with water tank measurements, with averaged Gamma passing rates (1%/1 mm passing criteria) higher than 95% and averaged mean absolute errors less than 0.6%. Beam data samples with measurement errors were revealed by inconsistent beam predictions between networks trained with correct versus erroneous data samples, characterized by a Gamma passing rate lower than 90%. Conclusion: A NeRP beam data modeling technique has been established for predicting beam characteristics from sparse measurements. The model provides a valuable tool to verify beam data collection accuracy and promises to simplify commissioning/QA processes by reducing the number of measurements without compromising the quality of medical physics service. [ABSTRACT FROM AUTHOR]

Background/Purpose: Adequate image registration of anatomical and functional magnetic resonance imaging (MRI) scans is necessary for MR-guided head and neck cancer (HNC) adaptive radiotherapy planning. Despite the quantitative capabilities of diffusion-weighted imaging (DWI) MRI for treatment plan adaptation, geometric distortion remains a considerable limitation. Therefore, we systematically investigated various deformable image registration (DIR) methods to co-register DWI and T2-weighted (T2W) images. Materials/Methods: We compared three commercial (ADMIRE, Velocity, Raystation) and three open-source (Elastix with default settings [Elastix Default], Elastix with parameter set 23 [Elastix 23], Demons) post-acquisition DIR methods applied to T2W and DWI MRI images acquired during the same imaging session in twenty immobilized HNC patients. In addition, we used the nonregistered images (None) as a control comparator. Ground-truth segmentations of radiotherapy structures (tumour and organs at risk) were generated by a physician expert on both image sequences. For each registration approach, structures were propagated from T2W to DWI images. These propagated structures were then compared with ground-truth DWI structures using the Dice similarity coefficient and mean surface distance. Results: 19 left submandibular glands, 18 right submandibular glands, 20 left parotid glands, 20 right parotid glands, 20 spinal cords, and 12 tumours were delineated. Most DIR methods took <30 s to execute per case, with the exception of Elastix 23 which took ∼458 s to execute per case. ADMIRE and Elastix 23 demonstrated improved performance over None for all metrics and structures (Bonferroni-corrected p < 0.05), while the other methods did not. Moreover, ADMIRE and Elastix 23 significantly improved performance in individual and pooled analysis compared to all other methods. Conclusions: The ADMIRE DIR method offers improved geometric performance with reasonable execution time so should be favoured for registering T2W and DWI images acquired during the same scan session in HNC patients. These results are important to ensure the appropriate selection of registration strategies for MR-guided radiotherapy. [ABSTRACT FROM AUTHOR]

Purpose: Deformable dose accumulation (DDA) has uncertainties which impede the implementation of DDA-based adaptive radiotherapy (ART) in clinic. The purpose of this study is to develop a multi-layer quality assurance (MLQA) program to evaluate uncertainties in DDA. Methods: A computer program is developed to generate a pseudo-inverse displacement vector field (DVF) for each deformable image registration (DIR) performed in Accuray’s PreciseART. The pseudo-inverse DVF is first used to calculate a pseudo-inverse consistency error (PICE) and then implemented in an energy and mass congruent mapping (EMCM) method to reconstruct a deformed dose. The PICE is taken as a metric to estimate DIR uncertainties. A pseudo-inverse dose agreement rate (PIDAR) is used to evaluate the consequence of the DIR uncertainties in DDA and the principle of energy conservation is used to validate the integrity of dose mappings. The developed MLQA program was tested using the data collected from five representative cancer patients treated with tomotherapy. Results: DIRs were performed in PreciseART to generate primary DVFs for the five patients. The fidelity index and PICE of these DVFs on average are equal to 0.028 mm and 0.169 mm, respectively.With the criteria of 3 mm/3% and 5 mm/5%,the PIDARs of the PreciseART-reconstructed doses are 73.9 ± 4.4% and 87.2 ± 3.3%, respectively. The PreciseART and EMCM-based dose reconstructions have their deposited energy changed by 5.6 ± 3.9% and 2.6 ± 1.5% in five GTVs, and by 9.2 ± 7.8% and 4.7 ± 3.6% in 30 OARs, respectively. Conclusions: A pseudo-inverse map-based EMCM program has been developed to evaluate DIR and dose mapping uncertainties. This program could also be used as a sanity check tool for DDA-based ART. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

PurposeTo assess the performance of routine cone-beam computed tomography (CBCT) quality assurance (QA) at predicting and diagnosing clinically recognizable linac CBCT image quality issues.MethodsMonthly automated linac CBCT image quality QA data were acquired on eight Varian linacs (Varian Medical Systems, Palo Alto, CA) using the CATPHAN 500 series phantom (The Phantom Laboratory, Inc., Greenwich, NY) and Total QA software (Image Owl, Inc., Greenwich, NY) over 34 months between July 2014 and May 2017. For each linac, the following image quality metrics were acquired: geometric distortion, spatial resolution, Hounsfield Unit (HU) constancy, uniformity, and noise. Quality control (QC) limits were determined by American Association of Physicists in Medicine (AAPM) expert consensus documents Task Group (TG-142 and TG-179) and the manufacturer acceptance testing procedure. Clinically recognizable CBCT issues were extracted from the in-house incident learning system (ILS) and service reports. The sensitivity and specificity of CATPHAN QA at predicting clinically recognizable image quality issues was investigated. Sensitivity was defined as the percentage of clinically recognizable CBCT image quality issues that followed a failing CATPHAN QA. Quality assurance results are categorized as failing if one or more image quality metrics are outside the QC limits. The specificity of CATPHAN QA was defined as one minus the fraction of failing CATPHAN QA results that did not have a clinically recognizable CBCT image quality issue in the subsequent month. Receiver operating characteristic (ROC) curves were generated for each image quality metric by plotting the true positive rate (TPR) against the false-positive rate (FPR).ResultsOver the study period, 18 image quality issues were discovered by clinicians while using CBCT to set up the patient and five were reported prior to x-ray tube repair. The incidents ranged from ring artifacts to uniformity problems. The sensitivity of the TG-142/179 limits was 17% (four of the prior monthly QC tests detected a clinically recognizable image quality issue). The area under the curve (AUC) calculated for each image quality metric ROC curve was: 0.85 for uniformity, 0.66 for spatial resolution, 0.51 for geometric distortion, 0.56 for noise, 0.73 for HU constancy, and 0.59 for contrast resolution.ConclusionAutomated monthly QA is not a good predictor of CBCT image quality issues. Of the available metrics, uniformity has the best predictive performance, but still has a high FPR and low sensitivity. The poor performance of CATPHAN QA as a predictor of image quality problems is partially due to its reliance on region-of-interest (ROI) based algorithms and a lack of a global algorithm such as correlation. The manner in which image quality issues occur (trending toward failure or random) is still not known and should be studied further. CBCT image quality QA should be adapted based on how CBCT is used clinically.

PurposeProton dose distribution is sensitive to uncertainties in range estimation and patient positioning. Currently, the proton robustness is managed by worst-case scenario optimization methods, which are computationally inefficient. To overcome these challenges, we develop a novel intensity-modulated proton therapy (IMPT) optimization method that integrates dose fidelity with a sensitivity term that describes dose perturbation as the result of range and positioning uncertainties.MethodsIn the integrated optimization framework, the optimization cost function is formulated to include two terms: a dose fidelity term and a robustness term penalizing the inner product of the scanning spot sensitivity and intensity. The sensitivity of an IMPT scanning spot to perturbations is defined as the dose distribution variation induced by range and positioning errors. To evaluate the sensitivity, the spatial gradient of the dose distribution of a specific spot is first calculated. The spot sensitivity is then determined by the total absolute value of the directional gradients of all affected voxels. The fast iterative shrinkage-thresholding algorithm is used to solve the optimization problem. This method was tested on three skull base tumor (SBT) patients and three bilateral head-and-neck (H&N) patients. The proposed sensitivity-regularized method (SenR) was implemented on both clinic target volume (CTV) and planning target volume (PTV). They were compared with conventional PTV-based optimization method (Conv) and CTV-based voxel-wise worst-case scenario optimization approach (WC).ResultsUnder the nominal condition without uncertainties, the three methods achieved similar CTV dose coverage, while the CTV-based SenR approach better spared organs at risks (OARs) compared with the WC approach, with an average reduction of [Dmean, Dmax] of [4.72, 3.38]  GyRBE for the SBT cases and [2.54, 3.33] GyRBE for the H&N cases. The OAR sparing of the PTV-based SenR method was comparable with the WC method. The WC method, and SenR approaches all improved the plan robustness from the conventional PTV-based method. On average, under range uncertainties, the lowest [D95%, V95%, V100%] of CTV were increased from [93.75%, 88.47%, 47.37%] in the Conv method, to [99.28%, 99.51%, 86.64%] in the WC method, [97.71%, 97.85%, 81.65%] in the SenR-CTV method and [98.77%, 99.30%, 85.12%] in the SenR-PTV method, respectively. Under setup uncertainties, the average lowest [D95%, V95%, V100%] of CTV were increased from [95.35%, 94.92%, 65.12%] in the Conv method, to [99.43%, 99.63%, 87.12%] in the WC method, [96.97%, 97.13%, 77.86%] in the SenR-CTV method, and [98.21%, 98.34%, 83.88%] in the SenR-PTV method, respectively. The runtime of the SenR optimization is eight times shorter than that of the voxel-wise worst-case method.ConclusionWe developed a novel computationally efficient robust optimization method for IMPT. The robustness is calculated as the spot sensitivity to both range and shift perturbations. The dose fidelity term is then regularized by the sensitivity term for the flexibility and trade-off between the dosimetry and the robustness. In the stress test, SenR is more resilient to unexpected uncertainties. These advantages in combination with its fast computation time make it a viable candidate for clinical IMPT planning.