Purpose: Patient-specific IMRT QA measurements are important components of processes designed to identify discrepancies between calculated and delivered radiation doses. Discrepancy tolerance limits are neither well defined nor consistently applied across centers. The AAPM TG-218 report provides a comprehensive review aimed at improving the understanding and consistency of these processes as well as recommendations for methodologies and tolerance limits in patient-specific IMRT QA. Methods: The performance of the dose difference/distance-to-agreement (DTA) and c dose distribution comparison metrics are investigated. Measurement methods are reviewed and followed by a discussion of the pros and cons of each. Methodologies for absolute dose verification are discussed and new IMRT QA verification tools are presented. Literature on the expected or achievable agreement between measurements and calculations for different types of planning and delivery systems are reviewed and analyzed. Tests of vendor implementations of the c verification algorithm employing benchmark cases are presented. Results: Operational shortcomings that can reduce the c tool accuracy and subsequent effectiveness for IMRT QA are described. Practical considerations including spatial resolution, normalization, dose threshold, and data interpretation are discussed. Published data on IMRT QA and the clinical experience of the group members are used to develop guidelines and recommendations on tolerance and action limits for IMRT QA. Steps to check failed IMRT QA plans are outlined. Conclusion: Recommendations on delivery methods, data interpretation, dose normalization, the use of c analysis routines and choice of tolerance limits for IMRT QA are made with focus on detecting differences between calculated and measured doses via the use of robust analysis methods and an in-depth understanding of IMRT verification metrics. The recommendations are intended to improve the IMRT QA process and establish consistent, and comparable IMRT QA criteria among institutions.

PurposeMagnetic resonance image guided radiation therapy (MR-IGRT) has been used at our institution since 2014. We report on more than 2 years of clinical experience in treating patients with the world's first MR-IGRT system.Methods and materialsA clinical service was opened for MR-IGRT in January 2014 with an MR-IGRT system consisting of a split 0.35T magnetic resonance scanner that straddles a ring gantry with 3 multileaf collimator-equipped 60Co heads. The service was expanded to include online adaptive radiation therapy (ART) MR-IGRT and cine gating after 6 and 9 months, respectively. Patients selected for MR-IGRT were enrolled in a prospective registry between January 2014 and June 2016. Patients were treated with a variety of radiation therapy techniques including intensity modulated radiation therapy and stereotactic body radiation therapy (SBRT). When applicable, online ART was performed and gating on sagittal 2-dimensional cine MR was used. The charts of patients treated with MR-IGRT were reviewed to report on the clinical and treatment characteristics of the initial patients who were treated with this novel technique.ResultsA total of 316 patients have been treated with the MR-IGRT system, which has been integrated into a high-volume clinic. The cases were most commonly selected for improved soft tissue visualization, ART, and cine gating. Seventy-six patients were treated with 3-dimensional conformal radiation therapy, 146 patients with intensity modulated radiation therapy, and 94 patients with SBRT. The most commonly treated disease sites were the abdomen (28%), breast (26%), pelvis (22%), thorax (19%), and head and neck (5%). Sixty-seven patients were treated with online ART over a total of 244 adapted fractions. Cine treatment gating was used for a total of 81 patients.ConclusionsMR-IGRT has been successfully implemented in a high-volume radiation clinic and provides unique advantages in the treatment of a variety of malignancies. Additional clinical trials are in development to formally evaluate MR-IGRT in the treatment of multiple disease sites with techniques such as SBRT and ART.

Purpose Intensity‐modulated radiation therapy (IMRT) quality assurance (QA) measurements are routinely performed prior to treatment delivery to verify dose calculation and delivery accuracy. In this work, we applied a machine learning‐based approach to predict portal dosimetry based IMRT QA gamma passing rates. Methods 182 IMRT plans for various treatment sites were planned and delivered with portal dosimetry on two TrueBeam and two Trilogy LINACs. A total of 1497 beams were collected and analyzed using gamma criteria of 2%/2 mm with a 5% threshold. The datasets for building the machine learning models consisted of 1269 beams. Ten‐fold cross‐validation was utilized to tune the model and prevent “overfitting.” A separate test set with the remaining 228 beams was used to evaluate model performance. Each beam was characterized by a set of 31 features including both plan complexity metrics and machine characteristics. Three tree‐based machine learning algorithms (AdaBoost, Random Forest, and XGBoost) were used to train the models and predict gamma passing rates. Results Both AdaBoost and Random Forest had 98% of predictions within 3% of the measured 2%/2 mm gamma passing rates with a maximum error less than 4% and a mean absolute error < 1%. XGBoost showed a slightly worse prediction accuracy with 95% of the predictions within 3% of the measured gamma passing rates and a maximum error of 4.5%. The three models identified the same nine features in the top 10 most important ones that are related to plan complexity and maximum aperture displacement from the central axis or the maximum jaw size in a beam. Conclusion We have demonstrated that portal dosimetry IMRT QA gamma passing rates can be accurately predicted using tree‐based ensemble learning models. The machine learning based approach allows physicists to better identify the failures of IMRT QA measurements and to develop proactive QA approaches.

Precise mechanical operation of a linear accelerator (linac) is critical for accurate radiation therapy dose delivery. Quantitative procedures for linac mechanical quality assurance (QA) used in the standard of care are time consuming and therefore conducted on a relatively infrequent basis. We present a method for evaluating the mechanical performance of a linac based on a series of projection portal images of a prototype cylindrical phantom with embedded radiopaque fiducial markers. The marker autodetection process included modeling imager response to the radiation beam where the projected cylinder attenuation yielded a non-uniform image background. The linac mechanical characteristics were estimated based on nonlinear multi-objective optimization of the projected marker locations. The estimated geometry parameters for the tested commercial model were gantry angle deviation 0.075 +/- 0.076 degrees (1 SD), gantry sag 0.026 +/- 0.02 degrees , source-to-axis distance SAD 998.3 +/- 1.7 mm, source-to-detector distance SDD 1493 +/- 5.0 mm, couch vertical motion 0.6 +/- 0.45 mm, couch rotation 0.154 +/- 0.1 degrees and average linac rotation center (1.02, -0.27, -0.37) +/- (0.36,0.333,1.20) mm relative to the laser intersection. The imager shift was [-0.44, 2.6] +/- [0.20, 1.1] mm and the imager orientation was in-plane rotation 0.05 +/- 0.03 degrees , roll -0.14 +/- 0.09 degrees and pitch -0.9 +/- 0.604 degrees . The performance of this procedure concerning marker detection and optimization was examined by comparing the detected set of marker coordinates to its back-calculated counterpart for three subgroups of markers: central, wall and intermediate relative to the center of the phantom. The maximum difference was less than 0.25 mm with a mean of 0.146 mm and a standard deviation of 0.07 mm. The clinical use of this automated procedure will allow more efficient, more thorough, and more frequent mechanical linac QA.

Amorphous silicon based electronic portal imaging devices (EPIDs) have been shown to be a good alternative to radiographic film for routine quality assurance (QA) of multileaf collimator (MLC) positioning accuracy. In this work, we present a method of acquiring an EPID image of a traditional strip-test image using analytical fits of the interleaf and leaf abutment image signatures. After exposure, the EPID image pixel values are divided by an open field image to remove EPID response and radiation field variations. Profiles acquired in the direction orthogonal to the leaf motion exhibit small peaks caused by interleaf leakage. Gaussian profiles are fitted to the interleaf leakage peaks, the results of which are, using multiobjective optimization, used to calculate the image rotational angle with respect to the collimator axis of rotation. The relative angle is used to rotate the image to align the MLC leaf travel to the image pixel axes. The leaf abutments also present peaks that are fitted by heuristic functions, in this case modified Lorentzian functions. The parameters of the Lorentzian functions are used to parameterize the leaf gap width and positions. By imaging a set of MLC fields with varying gaps forming symmetric and asymmetric abutments, calibration curves with regard to relative peak height (RPH) versus nominal gap width are obtained. Based on this calibration data, the individual leaf positions are calculated to compare with the nominal programmed positions. The results demonstrate that the collimator rotation angle can be determined as accurate as 0.01 degrees. A change in MLC gap width of 0.2 mm leads to a change in RPH of about 10%. For asymmetrically produced gaps, a 0.2 mm MLC leaf gap width change causes 0.2 pixel peak position change. Subpixel resolution is obtained by using a parameterized fit of the relatively large abutment peaks. By contrast, for symmetrical gap changes, the peak position remains unchanged with a standard deviation of 0.05 pixels, or 0.026 mm. A trial run of 36 test images, each with gap widths varying from 0.4 to 1.4 mm, were used to analyze 8640 abutments. The leaf position variations were detected with a precision of 0.1 mm at a 95% confidence level, with a mean of 0.04 mm and a standard deviation of 0.03 mm. The proposed method is robust and minimizes the effect of image noise and pixel size and may help physicists to establish reliable and reasonable action levels in routine MLC QA.

The MRIdian MLC has a good RF noise shielding design, low radiation leakage, good positioning accuracy, comparable TG effect, and can be modeled by an independent Monte Carlo calculation platform.

The proposed motion tracking method can provide accurate upper airway motion tracking results, and enable automatic and quantitative identification and analysis of in-treatment H&N upper airway motion. By integrating explicit and implicit linked-shape representations within a hierarchical model-fitting process, the proposed tracking method can process complex H&N structures and low-contrast/resolution cine MRI images. Future research will focus on the improvement of method reliability, patient motion pattern analysis for providing more information on patient-specific prediction of structure displacements, and motion effects on dosimetry for better H&N motion management in radiation therapy.

A novel 3D remote dosimetry protocol is introduced for validating off-site dosimetrically complex radiotherapy systems, including MRgRT. The protocol involves correcting for temporal and spatially dependent changes in PRESAGE radiochromic dosimeters readout by optical-CT. Application of the protocol to TG-119 irradiations enabled verification of MRgRT dose distributions with high resolution.