AAPM Task Group Report 307: Use of EPIDs for Patient-Specific IMRT and VMAT QA

Abstract

Purpose: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: EPID; EPID QA; IMRT/VMAT QA; electronic portal imaging device; patient-specific EPID QA.

Figure B1.

The formalism for back-projection of a transit dose measured with an EPID to determine the dose within the patient as used in EPIgray. Steps 2–4 can be used with any type of detector (reproduced from Francois, Boissard et al. 2011).

Figure B2.

Schematic diagram of a solid water phantom of thickness w irradiated by square open or wedged fields L × L in size. The EPID is positioned at the SID to measure the transit signals s_t(I,w,L) and s_t(I,w,L,d) at point s. (a) Reference configuration with an IC positioned in the midplane at SAD = 100 cm to determine the midplane dose D(I,w,L). (b) The midplane is positioned at the distance d below the SAD to determine the midplane dose D′(I,w,L). (c) A phantom with w = 22 cm is irradiated to obtain a reference transit signal s_r,t required for the determination of the calibration factor k_s (reproduced from Piermattei, Greco et al. 2012).

Figure B3.

Schematic presentation of the various steps involved in the reconstruction of the dose distribution inside a patient/phantom from an EPID measurement using the iViewDose back-projection model (reproduced from Mijnheer, Olaciregui-Ruiz et al. 2013).

Figure 1.

Main components of an a-Si EPID.

Figure 2.

Schematic representation of the various EPID-based PSQA techniques. (a) and (b) Forward methods compare measured 2D images or dose distributions with predicted images or dose distributions at the EPID level. Back-projection methods, both (c) non-transit and (d) transit, provide dose distributions in a phantom or patient. (Reproduced from Mijnheer 2017^b).

Figure 3.

Computer screen showing the SOFTDISO interface for IVD results. The screen is subdivided into four areas. From the left: (a) patient CT slice at isocenter level and the in-plane EPID profiles of two measurements; (b) the pixel area around the beam central axis consisting of 9 × 9 pixels (corresponding roughly to the TPS grid) and the cross-plane EPID profiles; (c) the results for R ratios, the ratios between the measured dose, and the dose calculated by the TPS in the pixel area around the beam central axis, obtained at different days; and (d) the daily EPID-integrated images obtained at two different days and their γ analysis. The red and blue data in the four figures have been obtained on two different days: December 5 and December 9, 2013, respectively. (Reproduced from Cilla, Meluccio et al. 2016).

Figure 4.

Steps involved in verification of the predicted dose.

Figure 5.

Graphical representation of the ability of EPID QA to detect failure modes for (a) pretreatment, (b) first fraction transit, and (c) all fraction transit dosimetry as reported by Bojechko et al (Bojechko, Phillips et al. 2015). Each graph contains points representing a specific failure mode from the radiotherapy process scored for probability of occurrence and detectability. For example, pretreatment EPID dosimetry has high detectability for errors such as physics calculation error or treatment machine error, but low detectability for a number of failure modes such as wrong isocenter info, wrong or faulty equipment used, or movement on table. Comparisons between graphs demonstrate the potential advantages of specific EPID QA implementation; for instance, first fraction transit dosimetry has higher detectability of many failure modes, including a cluster of high occurrence failure modes such as wrong isocenter info, setup error, and error in CT data, while the detectability of these modes is lower for pretreatment and all fraction transit dosimetry. Legend for abbreviated failure modes: A: wrong or faulty equipment used, B: record and verify system down, C: personnel could not be contacted, D: treatment machine error, E: scheduling error, F: movement on table, and G: error in field planning.

Figure 5.

Graphical representation of the ability of EPID QA to detect failure modes for (a) pretreatment, (b) first fraction transit, and (c) all fraction transit dosimetry as reported by Bojechko et al (Bojechko, Phillips et al. 2015). Each graph contains points representing a specific failure mode from the radiotherapy process scored for probability of occurrence and detectability. For example, pretreatment EPID dosimetry has high detectability for errors such as physics calculation error or treatment machine error, but low detectability for a number of failure modes such as wrong isocenter info, wrong or faulty equipment used, or movement on table. Comparisons between graphs demonstrate the potential advantages of specific EPID QA implementation; for instance, first fraction transit dosimetry has higher detectability of many failure modes, including a cluster of high occurrence failure modes such as wrong isocenter info, setup error, and error in CT data, while the detectability of these modes is lower for pretreatment and all fraction transit dosimetry. Legend for abbreviated failure modes: A: wrong or faulty equipment used, B: record and verify system down, C: personnel could not be contacted, D: treatment machine error, E: scheduling error, F: movement on table, and G: error in field planning.