Sun Nuclear (United States)

Abstract Multi‐dimensional detector arrays have been adopted in radiation therapy clinics by medical physicists for various types of measurements. Typical applications of such devices include plan quality assurance (QA) measurements, periodic QA measurements for constancy checks of beam flatness and symmetry, as well as dose and beam quality constancy checks for linear accelerators (linacs). The multi‐dimensional arrays vary in terms of their size, detector types, and usage. This report provides guidelines to the users on initial and ongoing performance assessments that should be performed to ensure proper functionality of ion chamber and diode multi‐detector arrays that are positioned on the linac couch or gantry mounts and used for patient‐specific quality assurance (PSQA) and machine QA. The characteristics of the most common detector arrays are provided in this report along with their intended use. After reading this report, the user should understand how to assess the detector array's performance and proceed with its use. The report describes the most relevant array parameters for testing and offers detailed test instructions and frequencies. Test results from the TG members and other clinical users are presented in a table. The tolerances for the parameters with some statistics are recommended based on those test results and other publications. This report is not intended to provide a comprehensive review of linac quality assurance procedures or patient‐specific Intensity Modulated Radiation Therapy (IMRT) QA.

Multi-energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single-energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user-selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task-specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single-energy CT, the substantial differences between single-energy CT and MECT in terms of system implementations, imaging protocols, and clinical tasks warrant QC tests specific to MECT. This task group was therefore charged with developing a systematic QC program designed to meet the needs of MECT applications. In this report, we review the various MECT approaches that are commercially available, including information about hardware implementation, MECT image types, image reconstruction, and postprocessing techniques that are unique to MECT. We address the requirements for MECT phantoms, review representative commercial MECT phantoms, and offer guidance regarding homemade MECT phantoms. We discuss the development of MECT protocols, which must be designed carefully with proper consideration of MECT technology, imaging task, and radiation dose. We then outline specific recommended QC tests in terms of general image quality, radiation dose, differentiation and quantification tasks, and diagnostic and therapeutic applications.

Electronic portal imaging devices (EPIDs) have been widely utilized for patient-specific quality assurance (PSQA) and their use for transit dosimetry applications is emerging. Yet there are no specific guidelines on the potential uses, limitations, and correct utilization of EPIDs for these purposes. The American Association of Physicists in Medicine (AAPM) Task Group 307 (TG-307) provides a comprehensive review of the physics, modeling, algorithms and clinical experience with EPID-based pre-treatment and transit dosimetry techniques. This review also includes the limitations and challenges in the clinical implementation of EPIDs, including recommendations for commissioning, calibration and validation, routine QA, tolerance levels for gamma analysis and risk-based analysis. Characteristics of the currently available EPID systems and EPID-based PSQA techniques are reviewed. The details of the physics, modeling, and algorithms for both pre-treatment and transit dosimetry methods are discussed, including clinical experience with different EPID dosimetry systems. Commissioning, calibration, and validation, tolerance levels and recommended tests, are reviewed, and analyzed. Risk-based analysis for EPID dosimetry is also addressed. Clinical experience, commissioning methods and tolerances for EPID-based PSQA system are described for pre-treatment and transit dosimetry applications. The sensitivity, specificity, and clinical results for EPID dosimetry techniques are presented as well as examples of patient-related and machine-related error detection by these dosimetry solutions. Limitations and challenges in clinical implementation of EPIDs for dosimetric purposes are discussed and acceptance and rejection criteria are outlined. Potential causes of and evaluations of pre-treatment and transit dosimetry failures are discussed. Guidelines and recommendations developed in this report are based on the extensive published data on EPID QA along with the clinical experience of the TG-307 members. TG-307 focused on the commercially available EPID-based dosimetric tools and provides guidance for medical physicists in the clinical implementation of EPID-based patient-specific pre-treatment and transit dosimetry QA solutions including intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) treatments.

Despite large investment cancer continues to be a major source of mortality and morbidity throughout the world. Traditional methods of detection and diagnosis such as biopsy and imaging, tend to be expensive and have risks of complications. As data becomes more abundant and machine learning continues advancing, it is natural to ask how they can help solve some of these problems. In this paper we show that using a person's personal health data it is possible to predict their risk for a wide variety of cancers. We dub this process a “statistical biopsy.” Specifically, we train two neural networks, one predicting risk for 16 different cancer types in females and the other predicting risk for 15 different cancer types in males. The networks were trained as binary classifiers identifying individuals that were diagnosed with the different cancer types within 5 years of joining the PLOC trial. However, rather than use the binary output of the classifiers we show that the continuous output can instead be used as a cancer risk allowing a holistic look at an individual's cancer risks. We tested our multi-cancer model on the UK Biobank dataset showing that for most cancers the predictions generalized well and that looking at multiple cancer risks at once from personal health data is a possibility. While the statistical biopsy will not be able to replace traditional biopsies for diagnosing cancers, we hope there can be a shift of paradigm in how statistical models are used in cancer detection moving to something more powerful and more personalized than general population screening guidelines.

A novel routine dual‐energy computed tomography (DECT) quality control (QC) program was developed to address the current deficiency of routine QC for this technology. The dual‐energy quality control (DEQC) program features (1) a practical phantom with clinically relevant materials and concentrations, (2) a clinically relevant acquisition, reconstruction, and postprocessing protocol, and (3) a fully automated analysis software to extract quantitative data for database storage and trend analysis. The phantom, designed for easy set up for standalone or adjacent imaging next to the ACR phantom, was made in collaboration with an industry partner and informed by clinical needs to have four iodine inserts (0.5, 1, 2, and 5 mg/ml) and one calcium insert (100 mg/ml) equally spaced in a cylindrical water‐equivalent background. The imaging protocol was based on a clinical DECT abdominal protocol capable of producing material specific concentration maps, virtual unenhanced images, and virtual monochromatic images. The QC automated analysis software uses open‐source technologies which integrates well with our current automated CT QC database. The QC program was tested on a GE 750 HD scanner and two Siemens SOMATOM FLASH scanners over a 3‐month period. The automated algorithm correctly identified the appropriate region of interest (ROI) locations and stores measured values in a database for monitoring and trend analysis. Slight variations in protocol settings were noted based on manufacturer. Overall, the project proved to provide a convenient and dependable clinical tool for routine oversight of DE CT imaging within the clinic.

Background Quantitative blood flow (QBF) measurements that use pulsed-wave US rely on difficult-to-meet conditions. Imaging biomarkers need to be quantitative and user and machine independent. Surrogate markers (eg, resistive index) fail to quantify actual volumetric flow. Standardization is possible, but relies on collaboration between users, manufacturers, and the U.S. Food and Drug Administration. Purpose To evaluate a Quantitative Imaging Biomarkers Alliance-supported, user- and machine-independent US method for quantitatively measuring QBF. Materials and Methods In this prospective study (March 2017 to March 2019), three different clinical US scanners were used to benchmark QBF in a calibrated flow phantom at three different laboratories each. Testing conditions involved changes in flow rate (1-12 mL/sec), imaging depth (2.5-7 cm), color flow gain (0%-100%), and flow past a stenosis. Each condition was performed under constant and pulsatile flow at 60 beats per minute, thus yielding eight distinct testing conditions. QBF was computed from three-dimensional color flow velocity, power, and scan geometry by using Gauss theorem. Statistical analysis was performed between systems and between laboratories. Systems and laboratories were anonymized when reporting results. Results For systems 1, 2, and 3, flow rate for constant and pulsatile flow was measured, respectively, with biases of 3.5% and 24.9%, 3.0% and 2.1%, and -22.1% and -10.9%. Coefficients of variation were 6.9% and 7.7%, 3.3% and 8.2%, and 9.6% and 17.3%, respectively. For changes in imaging depth, biases were 3.7% and 27.2%, -2.0% and -0.9%, and -22.8% and -5.9%, respectively. Respective coefficients of variation were 10.0% and 9.2%, 4.6% and 6.9%, and 10.1% and 11.6%. For changes in color flow gain, biases after filling the lumen with color pixels were 6.3% and 18.5%, 8.5% and 9.0%, and 16.6% and 6.2%, respectively. Respective coefficients of variation were 10.8% and 4.3%, 7.3% and 6.7%, and 6.7% and 5.3%. Poststenotic flow biases were 1.8% and 31.2%, 5.7% and -3.1%, and -18.3% and -18.2%, respectively. Conclusion Interlaboratory bias and variation of US-derived quantitative blood flow indicated its potential to become a clinical biomarker for the blood supply to end organs. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Forsberg in this issue.

Routine quality assurance for linear accelerators (linacs) usually involves verification of beam steering with a water scanning system. We established a beam steering procedure that uses a 2D ionization chamber array (ICA) and verified the equivalence of beam symmetry between the ICA and a water scanning system. The ICA calibration accuracy, reproducibility and stability were evaluated and the uncertainty in the measurement of beam symmetry due to the array calibration was examined. Forty‐five photon beams and 80 electron beams across 7 Varian C‐series and 4 TrueBeam linacs were steered in the radial and transverse directions using an ICA. After beam steering, profiles were re‐measured using the ICA and in‐water using a 3D Scanner (3DS). Beam symmetries measured with the ICA and 3DS were compared by (a) calculating the difference in point‐by‐point symmetry, (b) plotting the histogram distribution of the symmetry differences, and (c) comparing ICA and 3DS differences with their respective Varian symmetry protocol analysis. Array calibrations from five different occurrences (2012 to 2016) over six different beams reproduced within 0.5%. The uncertainty in beam symmetry was less than 0.5% due to the uncertainties in the array calibration. After all beams were steered using the ICA, the point‐by‐point symmetry differences between ICA and 3DS at the off‐axis positions of 20% and 80% of field size for all beam profiles indicated that 95% of point‐by‐point symmetry comparisons agreed within 0.7%, and 100% agreed within 1.0%; after steering with the ICA 97.8% of photon beam profiles (88 of 90) and 97.5% of electron beam profiles (156 of 160) had symmetry within 1% when measured with the 3DS. All photon and electron beam profiles had symmetry within 1.1% and 1.2%, respectively, for profiles measured with the 3DS. Our data demonstrate that a calibrated ICA can be used to steer photon and electron beams achieving beam symmetry within 1% when re‐measured with a 3D water scanning system.

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. The performance of the dose difference/distance-to-agreement (DTA) and γ 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 γ verification algorithm employing benchmark cases are presented. Operational shortcomings that can reduce the γ 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. Recommendations on delivery methods, data interpretation, dose normalization, the use of γ 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.