Electronic Portal Imaging Device Images Research Articles

Turning the attention to time-resolved EPID-images: treatment error classification with transformer multiple instance learning

Objective. The aim of this work was to develop a novel artificial intelligence-assisted in vivo dosimetry method using time-resolved (TR) dose verification data to improve quality of external beam radiotherapy. Approach. Although threshold classification methods are commonly used in error classification, they may lead to missing errors due to the loss of information resulting from the compression of multi-dimensional electronic portal imaging device (EPID) data into one or a few numbers. Recent research has investigated the classification of errors on time-integrated (TI) in vivo EPID images, with convolutional neural networks showing promise. However, it has been observed previously that TI approaches may cancel out the error presence on γ-maps during dynamic treatments. To address this limitation, simulated TR γ-maps for each volumetric modulated arc radiotherapy angle were used to detect treatment errors caused by complex patient geometries and beam arrangements. Typically, such images can be interpreted as a set of segments where only set class labels are provided. Inspired by recent weakly supervised approaches on histopathology images, we implemented a transformer based multiple instance learning approach and utilized transfer learning from TI to TR γ-maps. Main results. The proposed algorithm performed well on classification of error type and error magnitude. The accuracy in the test set was up to 0.94 and 0.81 for 11 (error type) and 22 (error magnitude) classes of treatment errors, respectively. Significance. TR dose distributions can enhance treatment delivery decision-making, however manual data analysis is nearly impossible due to the complexity and quantity of this data. Our proposed model efficiently handles data complexity, substantially improving treatment error classification compared to models that leverage TI data.

Integrating cine EPID, dynamic delivery, and the off-axis Winston-Lutz test to enhance quality control in multiple brain metastasis stereotactic radiotherapy

PurposeStereotactic radiotherapy (SRT) has transformed cancer treatment, especially for brain metastases. Ensuring accurate SRT delivery is crucial, with the Winston-Lutz test being an important quality control tool. Off-axis Winston-Lutz (OAWL) tests are designed for accuracy assessment, but most are limited to fixed angles and hampered by local-field shifts caused by suboptimal Multi-Leaf Collimator (MLC) positioning. This study introduces a new OAWL approach for quality control in multi-brain-metastasis SRT. Utilizing cine Electronic Portal Imaging Device (EPID) images, it can be used with dynamic conformal arc (DCA) therapy. However, dynamic OAWL (DOAWL) is prone to more local-field shifts due to dynamic MLC movements. A two-step DOAWL is proposed: step 1 calculates local-field shifts using dynamic MLC movements in the beam-eye view data from the Treatment Planning System (TPS), while step 2 processes cine EPID images with an OAWL algorithm to isolate true deviations. MethodsValidation involved an anthropomorphic head phantom with metallic ball-bearings, Varian TrueBeam STx accelerator delivering six coplanar/non-coplanar DCA beams, cine EPID, and ImageJ's OAWL analysis algorithm. ResultsInherent local-field shifts ranged from 0.11 to 0.49 mm; corrected mean/max EPID-measured displacement was 0.34/1.03 mm. Few points exceeded 0.75/1.0-mm thresholds. ConclusionsThis two-step DOAWL test merges cine-EPID acquisitions, DCA, OAWL, and advanced analysis and offers effective quality control for multi-brain-metastasis SRT. Its routine implementation may also improve physicist knowledge of the treatment precision of their machines.

A Novel Approach for Position Verification and Dose Calculation through Local MVCT Reconstruction.

Traditional positioning verification using cone-beam computed tomography (CBCT) may incur errors due to potential misalignments between the isocenter of CBCT and the treatment beams in radiotherapy. This study introduces an innovative method for verifying patient positioning in radiotherapy. Initially, the transmission images from an electronic portal imaging device (EPID) are acquired from 10 distinct angles. Utilizing the ART-TV algorithm, a sparse reconstruction of local megavoltage computed tomography (MVCT) is performed. Subsequently, this MVCT is aligned with the planning CT via a three-dimensional mutual information registration technique, pinpointing any patient-positioning discrepancies and facilitating corrective adjustments to the treatment setup. Notably, this approach employs the same radiation source as used in treatment to obtain three-dimensional images, thereby circumventing errors stemming from misalignment between the isocenter of CBCT and the accelerator. The registration process requires only 10 EPID images, and the dose absorbed during this process is included in the total dose calculation. The results show that our method's reconstructed MVCT images fulfill the requirements for registration, and the registration algorithm accurately detects positioning errors, thus allowing for adjustments in the patient's treatment position and precise calculation of the absorbed dose.

Comparison of deep learning models for building two-dimensional non-transit EPID Dosimetry on Varian Halcyon.

This study compared the effectiveness of five deep learning models in constructing non-transit dosimetry with an a-Si electronic portal imaging device (EPID) on Varian Halcyon. Deep learning model is increasingly used to support prediction and decision-making in several fields including oncology and radiotherapy. Forty-seven unique plans of data obtained from breast cancer patients were calculated using Eclipse treatment planning system (TPS) and extracted from DICOM format as the ground truth. Varian Halcyon was then used to irradiate the a-Si 1200 EPID detector without an attenuator. The EPID and TPS images were augmented and divided randomly into two groups of equal sizes to distinguish the validation and training-test data. Five different deep learning models were then created and validated using a gamma index of 3%/3 mm. Four models successfully improved the similarity of the EPID images and the TPS-generated planned dose images. Meanwhile, the mismatch of the constituent components and number of parameters could cause the models to produce wrong results. The average gamma pass rates were 90.07 ± 4.96% for A-model, 77.42 ± 7.18% for B-model, 79.60 ± 6.56% for C-model, 80.21 ± 5.88% for D-model, and 80.47 ± 5.98% for E-model. The deep learning model is proven to run fast and can increase the similarity of EPID images with TPS images to build non-transit dosimetry. However, more cases are needed to validate this model before being used in clinical activities.

The performance of a new type accelerator uRT-linac 506c evaluated by a quality assurance automation system.

The purpose of this study was to evaluate the performance of our quality assurance (QA) automation system and to evaluate the machine performance of a new type linear accelerator uRT-linac 506c within 6months using this system. This QA automation system consists of a hollow cylindrical phantom with 18 steel balls in the phantom surface and an analysis software to process electronic portal imaging device (EPID) measurement image data and report the results. The performance of the QA automation system was evaluated by the tests of repeatability, archivable precision, detectability of introduced errors, and the impact of set-up errors on QA results. The performance of this linac was evaluated by 31 items using this QA system over 6months. This QA system was able to automatically deliver QA plan, EPID image acquisition, and automatic analysis. All images acquiring and analysis took approximately 4.6min per energy. The preset error of 0.1mm in multi-leaf collimator (MLC) leaf were detected as 0.12±0.01mm for Bank A and 0.10±0.01mm in Bank B. The 2mm setup error was detected as -1.95±0.01mm, -2.02±0.01mm, 2.01±0.01mm for X, Y, Z directions, respectively. And data from the tests of repeatability and detectability of introduced errors showed the standard deviation were all within 0.1mm and 0.1°. and data of the machine performance were all within the tolerance specified by AAPM TG-142. The QA automation system has high precision and good performance, and it can improve the QA efficiency. The performance of the new accelerator has also performed very well during the testing period.

Tungsten Filled 3-Dimensional Printed Lung Blocks for Total Body Irradiation

Lung blocks for total-body irradiation are commonly used to reduce lung dose and prevent radiation pneumonitis. Currently, molten Cerrobend containing toxic materials, specifically lead and cadmium, is poured into molds to construct blocks. We propose a streamlined method to create 3-dimensional (3D)-printed lung block shells and fill them with tungsten ball bearings to remove lead and improve overall accuracy in the block manufacturing workflow. 3D-printed lung block shells were automatically generated using an inhouse software, printed, and filled with 2 to 3 mm diameter tungsten ball bearings. Clinical Cerrobend blocks were compared with the physician drawn blocks as well as our proposed tungsten filled 3D-printed blocks. Physical and dosimetric comparisons were performed on a linac. Dose transmission through the Cerrobend and 3D-printed blocks were measured using point dosimetry (ion-chamber) and the on-board Electronic-Portal-Imaging-Device (EPID). Dose profiles from the EPID images were used to compute the full-width-half-maximum and to compare with the treatment-planning-system. Additionally, the coefficient-of-variation in the central 80% of full-width-half-maximum was computed and compared between Cerrobend and 3D-printed blocks. The geometric difference between treatment-planning-system and 3D-printed blocks was significantly lower than Cerrobend blocks (3D: -0.88 ± 2.21 mm, Cerrobend: -2.28 ± 2.40 mm, P = .0002). Dosimetrically, transmission measurements through the 3D-printed and Cerrobend blocks for both ion-chamber and EPID dosimetry were between 42% to 48%, compared with the open field. Additionally, coefficient-of-variation was significantly higher in 3D-printed blocks versus Cerrobend blocks (3D: 4.2% ± 0.6%, Cerrobend: 2.6% ± 0.7%, P < .0001). We designed and implemented a tungsten filled 3D-printed workflow for constructing total-body-irradiation lung blocks, which serves as an alternative to the traditional Cerrobend based workflow currently used in clinics. This workflow has the capacity of producing clinically useful lung blocks with minimal effort to facilitate the removal of toxic materials from the clinic.

EPIDEEP: Using a Deep Learning Model to Predict In Vivo Electronic Portal Imaging Device (EPID) Transit Images

To create a deep-learning model to predict in-vivo electronic portal imaging device (EPID) transit images for IMRT treatments. This model was created to predict in-vivo images to identify machine and patient-related errors that occur during beam delivery and are undetectable with current QA approaches. The deep-learning model can make image predictions much faster than Monte Carlo approaches, making image prediction feasible for application in online adaptive radiotherapy. Additionally, the model does not rely on any proprietary information and can be easily utilized by other clinics. Our approach separates the primary and scatter components of in-vivo transit images. The attenuation of primary radiation reaching the EPID panel is modeled analytically, using attenuation measurements from phantoms of known thicknesses. The scatter component is estimated using a convolutional neural network (CNN). The CNN training uses information from the on-treatment cone-beam CTs (CBCTs), and a pretreatment EPID image with no patient in the beam. We acquired 193 IMRT fields/images from 118 patients previously treated on the Varian Halcyon. Treatment sites included the pelvis, abdomen, lungs, and extremities. CBCTs were collected immediately before treatment, to provide an accurate representation of the anatomy. A 3-channel input image was used, consisting of the pretreatment EPID image, a ray tracing projection through the CBCT to the EPID panel, and a projection to isocenter. Model training:validation:test set ratios were 133:20:40 images. The primary and scatter components are added together to give the predicted transit image. Prediction accuracy was assessed by comparing model-predicted and measured in-vivo EPID images with a 3%/3mm and 5%/3mm gamma pass rate. The gamma pass rate for the patients in the training:validation:test was 91.5%:90.4%:92.1% for 3%/3mm and 96.7%:96.6%:97.0% for 5%/3mm. The model can make image predictions in 20 milliseconds. The poor passing rates of some images may be due to CBCT artifacts and patient motion that occurs between the time of CBCT and treatment. This model can predict in-vivo EPID images with an average gamma pass rate greater than 90%. Image predictions from this model can be used to detect in-vivo treatment errors and changes in patient anatomy, providing an additional layer of patient-specific quality assurance. The speed of image predictions is 20 milliseconds, making use feasible for online adaptive treatments, which currently do not utilize patient-specific measurements of the delivered radiation. Upcoming studies will assess the model's ability in detecting clinically relevant errors and changes in patient anatomy that can adversely affect treatment. Future goals include acquiring more data to further improve the model and extending the model to make predictions for VMAT treatments.

Technical note: Preprocessing of portal images to improve image quality of VMAT-CT.

The concept of volumetric modulated arc therapy-computed tomography (VMAT-CT) was proposed more than a decade ago. However, its application has been very limited mainly due to the poor image quality. More specifically, the blurred areas in electronic portal imaging device (EPID) images collected during VMAT heavily degrade the image quality of VMAT-CT. The goal of this study was to propose systematic methods to preprocess EPID images and improve the image quality of VMAT-CT. Online region-based active contour method was introduced to binarize portal images. Multi-leaf collimator (MLC) motion modeling was developed to remove the MLC motion blur. Outlier filtering was then applied to replace the remaining artifacts with plausible data. To assess the impact of these preprocessing methods on the image quality of VMAT-CT, 44 clinical VMAT plans for several treatment sites (lung, esophagus, and head & neck) were delivered to a Rando phantom, and several real-patient cases were also acquired. VMAT-CT reconstruction was attempted for all the cases, and image quality was evaluated. All three preprocessing methods could effectively remove the blurred edges of EPID images. The combined preprocessing methods not only saved VMAT-CT from distortions and artifacts, but also increased the percentage of VMAT plans that can be reconstructed. The systematic preprocessing of portal images improves the image quality of VMAT-CT significantly, and facilitates the application of VMAT-CT as an effective image guidance tool.

Radiotherapy delivery error detection with electronic portal imaging device (EPID)-based in vivo dosimetry.

Radiotherapy delivery error detection with electronic portal imaging device (EPID)-based in vivo dosimetry.

In-vivo quality assurance of dynamic tumor tracking (DTT) for liver SABR using EPID images.

To assess dynamic tumor tracking (DTT) target localization uncertainty for in-vivo marker-based stereotactic ablative radiotherapy (SABR) treatments of the liver using electronic-portal-imaging-device (EPID) images. The Planning Target Volume (PTV) margin contribution for DTT is estimated. Phantom and patient EPID images were acquired during non-coplanar 3DCRT-DTT delivered on a Vero4DRT linac. A chain-code algorithm was applied to detect Multileaf Collimator (MLC)-defined radiation field edges. Gold-seed markers were detected using a connected neighbor algorithm. For each EPID image, the absolute differences between the measured center-of-mass (COM) of the markers relative to the aperture-center (Tracking Error, (ET )) was reported in pan, tilt, and 2D-vector directions at the isocenter-plane. An acrylic cube phantom implanted with gold-seed markers was irradiated with non-coplanar 3DCRT-DTT beams and EPID images collected. Patient Study: Eight liver SABR patients were treated with non-coplanar 3DCRT-DTT beams. All patients had three to four implanted gold-markers. In-vivo EPID images were analyzed. Phantom Study: On the 125 EPID images collected, 100% of the markers were identified. The average±SD of ET were 0.24±0.21, 0.47±0.38, and 0.58±0.37mm in pan, tilt and 2D directions, respectively. Patient Study: Of the 1430 EPID patient images acquired, 78% had detectable markers. Over all patients, the average±SD of ET was 0.33±0.41mm in pan, 0.63±0.75mm in tilt and 0.77±0.80mm in 2D directions The random 2D-error, σ, for all patients was 0.79mm and the systematic 2D-error, Σ, was 0.20mm. Using the Van Herk margin formula 1.1mm planning target margin can represent the marker based DTT uncertainty. Marker-based DTT uncertainty can be evaluated in-vivo on a field-by-field basis using EPID images. This information can contribute to PTV margin calculations for DTT.

Accuracy of a time-of-flight (ToF) imaging system for monitoring deep-inspiration breath-hold radiotherapy (DIBH-RT) for left breast cancer patients.

Deep inspiration breath-hold radiotherapy (DIBH-RT) reduces cardiac dose by over 50%. However, poor breath-hold reproducibility could result in target miss which compromises the treatment success. This study aimed to benchmark the accuracy of a Time-of-Flight (ToF) imaging system for monitoring breath-hold during DIBH-RT. The accuracy of an Argos P330 3D ToF camera (Bluetechnix, Austria) was evaluated for patient setup verification and intra-fraction monitoring among 13 DIBH-RT left breast cancer patients. The ToF imaging was performed simultaneously with in-room cone beam computed tomography (CBCT) and electronic portal imaging device (EPID) imaging systems during patient setup and treatment delivery, respectively. Patient surface depths (PSD) during setup were extracted from the ToF and the CBCT images during free breathing and DIBH using MATLAB (MathWorks, Natick, MA) and the chest surface displacement were compared. The mean difference ± standard deviation, correlation coefficient, and limit of agreement between the CBCT and ToF were 2.88 ± 5.89mm, 0.92, and -7.36, 1.60mm, respectively. The breath-hold stability and reproducibility were estimated using the central lung depth extracted from the EPID images during treatment and compared with the PSD from the ToF. The average correlation between ToF and EPID was -0.84. The average intra-field reproducibility for all the fields was within 2.70mm. The average intra-fraction reproducibility and stability were 3.74mm, and 0.80mm, respectively. The study demonstrated the feasibility of using ToF camera for monitoring breath-hold during DIBH-RT and shows good breath-hold reproducibility and stability during the treatment delivery.

Development of optical sensor system and hardware base frame for checking the light and radiation fields of a linear accelerator

This study aims to quantitatively evaluate the light and radiation fields of a linear accelerator by developing an optical sensor quality assurance (QA) system using a cadmium sulfide (CdS) photodetector cell and a hardware base frame manufactured in-house. The CdS photodetector cell used as the optical sensor has a size of 3.4 × 4.1 mm2, can measure up to 100 lux of light, and has a response time of approximately 20 to 30 ms. To check the alignment of the light field, it was manufactured with a CdS cell optical fiber holder, and the microcontroller board and control module were configured to scan the light field profile. Additionally, for accurate operation of the sensor, it was programmed using the integrated development environment (IDE) sketch. The alignment check of the radiation field was analyzed using the image data measured with electronic portal imaging device (EPID) using the manufactured hardware base frame. An analysis program written in MATLAB was used to evaluate the alignment of the radiation field. For symmetric fields measured using the optical sensor QA system, the full width at half maximum (FWHM) values of crossline (X) and inline (Y) profiles were 100.391 mm and 98.725 mm, respectively, for a field size of 100 × 100 mm2, and 199.093 mm and 198.886 mm, respectively, for a field size of 200 × 200 mm2. The X and Y FWHM values measured by the sensor system for the light fields were within 1 mm of the field size of the visual inspection. In addition, the FWHM value of the asymmetric radiation field measured using the fabricated frame and EPID was measured from the center bearing ball of the field to the bearing ball in each field of the four directions in the EPID image, and when the X1, X2, Y1, and Y2 jaws were opened by 50 mm, FWHM values were 50.120 mm, 50.240 mm, 48.860 mm, and 49.870 mm, respectively. The corresponding values were 99.520 mm, 99.570 mm, 99.010 mm, and 99.880 mm when the jaw fields were opened by 100 mm. The optical sensor system and hardware base frame developed in this study demonstrated the ability to evaluate both light and radiation fields more simply and quantitatively when compared to the conventional QA process. This QA system will provide medical physicists with more reliable QA results than the conventional QA method that uses graph paper and film.

Dose rate versus gantry speed performance evaluation for slow gantry speeds using DICOM RT plans.

PurposeVarian provides a DICOM RT Plan file that users can deliver to the electronic portal imaging device (EPID) panel to confirm the linear accelerator delivers consistent dose output across several regions of interest for varying dose rates and gantry speeds (DRGS). This work investigates if (a) the vendor‐provided DRGS DICOM RT Plan is valid within the gantry speed range of stereotactic body radiation therapy (SBRT) treatments, and (b) if output constancy is maintained at those gantry speeds on a TrueBeam.MethodsPython code was written to iterate through all control points in the DICOM RT Plan files for 21 SBRT patients and the MU per degree values were calculated for each control point. A histogram was generated to illustrate how MU per degree was distributed among the control points from the patient plans. Then, the total number of MUs was increased in the vendor‐provided DRGS DICOM file to make a “modified DRGS DICOM RT Plan,” which surpasses the maximum MU per degree value found in the patient plans, forcing the gantry to travel at slow speeds and deliver more MU per degree over the same arc length (representative of those during SBRT treatment delivery). The modified DRGS DICOM RT Plan file was then delivered on a TrueBeam to acquire EPID images of the dose distribution. The EPID images were analyzed with Pylinac, a Python library that analyzes DICOM RT images acquired during routine linac QA.ResultsOver 83% of patient DICOM RT Plan control points had MU per degree values greater than the MU per degree values in the vendor‐provided DRGS DICOM file. The Pylinac analysis of the EPID‐acquired images found a maximum deviation of 0.4% from machine baselines.ConclusionsThe modified DRGS DICOM file can be used to determine if a TrueBeam linac is operating within specifications even when very low gantry speeds are reached.

First clinical experience with real-time portal imaging-based breath-hold monitoring in tangential breast radiotherapy.

Background and purposeReal-time treatment monitoring with the electronic portal imaging device (EPID) can conceptually provide a more accurate assessment of the quality of deep inspiration breath-hold (DIBH) and patient movement during tangential breast radiotherapy (RT). A system was developed to measure two geometrical parameters, the lung depth (LD) and the irradiated width (named here skin distance, SD), along three user-selected lines in MV EPID images of breast tangents. The purpose of this study was to test the system during tangential breast RT with DIBH.Materials and methodsMeasurements of LDs and SDs were carried out in real time. DIBH was guided with a commercial system using a marker block. Results from 17 patients were assessed. Mean midline LDs, <mLDs>, per tangent were compared to the planned mLDs; differences between the largest and smallest observed <mLDs> (<mSDs>) per tangent were calculated.ResultsFor 56% (162/288) of the tangents tested, <mLDs> were outside the tolerance window. All but one patient had at least one fraction showing this behaviour. The largest difference found between an <mLD> and its planned mLD was −16.9 mm. The accuracy of patient positioning and the quality of marker-block-based DIBH guidance contributed to the differences. Fractions with patient position verification using a single EPID image taken before treatment showed a lower rate (34%), suggesting reassessment of setup procedures.ConclusionsReal-time treatment monitoring of the internal anatomy during DIBH delivery of tangential breast RT is feasible and useful. The new system requires no additional radiation for the patient.

The fast calibration model for dosimetry with an electronic portal imaging device.

The aim of this study was to develop an algorithm that corrects the image of an electronic portal imaging device (EPID) of a linear accelerator so that it can be used for dosimetric purposes, such as in vivo dosimetry or quality assurance for photon radiotherapy. For that purpose, the impact of the field size, phantom thickness, and the varying spectral photon distribution within the irradiation field on the EPID image was investigated. The EPID measurements were verified using reference measurements with ionization chambers. Therefore, absolute dose measurements with an ionization chamber and relative dose measurements with a detector array were performed. An EPID calibration and correction algorithm was developed to convert the EPID image to a dose distribution. The algorithm was validated by irradiating inhomogeneous phantoms using square fields as well as irregular IMRT fields. It was possible to correct the influence of the field size, phantom thickness on the EPID signal as well as the homogenization of the image profile by several correction factors within 0.6%. A gamma index analysis (3%, 3mm) of IMRT fields showed a pass rate of above 99%, when comparing to the planning system. The developed algorithm enables an online dose measurement with the EPID during the radiation treatment. The algorithm is characterized by a robust, non-iterative, and thus real-time capable procedure with little measuring effort and does not depend on system-specific parameters. The EPID image is corrected by multiplying three independent correction factors. Therefore, it can easily be extent by further correction factors for other influencing variables, so it can be transferred to other linear accelerators and EPID configurations.

Deep Neural Network With Structural Similarity Difference and Orientation-Based Loss for Position Error Classification in the Radiotherapy of Graves' Ophthalmopathy Patients.

Identifying position errors for Graves' ophthalmopathy (GO) patients using electronic portal imaging device (EPID) transmission fluence maps is helpful in monitoring treatment. However, most of the existing models only extract features from dose difference maps computed from EPID images, which do not fully characterize all information of the positional errors. In addition, the position error has a three-dimensional spatial nature, which has never been explored in previous work. To address the above problems, a deep neural network (DNN) model with structural similarity difference and orientation-based loss is proposed in this paper, which consists of a feature extraction network and a feature enhancement network. To capture more information, three types of Structural SIMilarity (SSIM) sub-index maps are computed to enhance the luminance, contrast, and structural features of EPID images, respectively. These maps and the dose difference maps are fed into different networks to extract radiomic features. To acquire spatial features of the position errors, an orientation-based loss function is proposed for optimal training. It makes the data distribution more consistent with the realistic 3D space by integrating the error deviations of the predicted values in the left-right, superior-inferior, anterior-posterior directions. Experimental results on a constructed dataset demonstrate the effectiveness of the proposed model, compared with other related models and existing state-of-the-art methods.

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

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

Assessing the accuracy of electronic portal imaging device (EPID)‐based dosimetry: II. Evaluation of a dosimetric uncertainty budget and development of a new film‐in‐EPID absorbed dose calibration methodology

The aim of this study is to reduce the uncertainty associated with determining dose-to-water using an amorphous silicon electronic portal imaging detector (EPID) under reference conditions by developing a direct calibration formalism based on radiochromic film measurements made within the EPID panel and detailed Monte Carlo simulations. To our knowledge, this is the first EPID-based dosimetry study reporting an uncertain budget METHODS: Pixel sensitivity and relative off-axis response were mapped by simultaneously irradiating film contained within the imager panel and acquiring an EPID image set. The detector panel was disassembled for the purpose of modeling the EPID in detail using the EGSnrc DOSXYZnrc usercode, which was in turn used to calculate dose-to-film in the EPID and dose-to-water in water conversion factors RESULTS: A direct comparison of the two correction methodologies investigated in this work, the previously established empirical method and the proposed simultaneous measurement approach involving in-EPID film dosimetry, produced an agreement with an RMS deviation of 1.4% overall. A combined standard relative uncertainty of 3.3% (k=1) was estimated for the determination of absorbed dose to water at the position of the EPID using the proposed calibration methodology CONCLUSIONS: This work describes a direct method of calibrating EPID response in terms of absorbed dose to water requiring fewer measurements than other empirical approaches, and without 2D spatial interpolation of correction factors.

Development and Evaluation of Jaw Position Detection Method in Jaw-tracking Delivery Using an Electronic Portal Imaging Device Cine Mode.

To develop a method for detecting jaw positions during jaw-tracking delivery to ensure an accurate delivery of radiation to patients using an electronic portal imaging device (EPID) in the cine mode on a linear accelerator for radiotherapy. A bidirectional picket fence (BPF) plan was used in a novel application to detect jaw positions during jaw-tracking delivery. In the BPF plan, jaws tracked multileaf collimator (MLC) apertures. The irradiated patterns were acquired by an EPID in the cine mode. The upper- and lower-half leaves in the MLC moved in opposite directions to facilitate detection of jaw positions on EPID images. A picket-fence-like image was created by summing all acquired cine images and evaluated to detect MLC leaf positions. Jaw positions determined on the cine images were compared with those expected from the delivered BPF plan. The absolute differences (average ± 1 standard deviation) were 0.16 ± 0.19 mm for the X1 jaw and 0.11 ± 0.16 mm for the X2 jaw. The maximum error in the MLC leaf positions detected in the picket-fence-like pattern were 0.11 mm. Jaw positions during jaw-tracking delivery were identified using the cine EPID images and could be determined within an accuracy better than 0.5 mm. The BPF plan is also available as a picket fence test and can determine the MLC leaf positions to an accuracy better than 0.5 mm.

A system for real-time monitoring of breath-hold via assessment of internal anatomy in tangential breast radiotherapy.

The deep inspiration breath‐hold (DIBH) technique assists in sparing the heart, lungs, and liver during breast radiotherapy (RT). The quality of DIBH is currently assessed via surrogates which correlate to varying degrees with the patient's internal anatomy. Since modern linacs are equipped with an electronic portal imaging device (EPID), images of the irradiated anatomy streamed from EPIDs and analyzed in real time could significantly improve assessment of the quality of DIBH.A system has been developed to quantify the quality of DIBH during tangential breast RT by analyzing the “beam's eye view” images of the treatment fields. The system measures the lung depth (LD) and the distance from the breast surface to the posterior tangential radiation field edge (skin distance, SD) at three user‐defined locations.LD and SD measured in real time in EPID images of two RT phantoms showing different geometrical characteristics of their chest wall regions (computed tomography dose index [CTDI] and “END‐TO‐END” stereotactic body radiation therapy [E2E SBRT]) were compared with ground truth displacements provided by a precision motion platform. Performance of the new system was evaluated via static and dynamic (sine wave motion) measurements of LD and SD, covering clinical situations with stable and unstable breath‐hold. The accuracy and precision of the system were calculated as the mean and standard deviation of the differences between the ground truth and measured values.The accuracy of the static measurements of LD and SD for the CTDI phantom was 0.31 (1.09) mm [mean (standard deviation)] and –0.10 (0.14) mm, respectively. The accuracy of the static measurements for E2E SBRT phantom was 0.01 (0.18) mm and 0.05 (0.08) mm. The accuracy of the dynamic LD and SD measurements for the CTDI phantom was –0.50 (1.18) mm and 0.01 (0.12) mm, respectively. The accuracy of the dynamic measurements for E2E SBRT phantom was –0.03 (0.19) mm and 0.01 (0.11) mm.