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Purpose: In real‐time electronic portal imaging device (EPID) dosimetry applications where on‐treatment measured transmission images are compared to an ideal predicted image, ideally a tight tolerance should be set on the quantitative image comparison in order to detect a wide variety of possible delivery errors. However, this is currently not possible due to the appearance of banding artifacts in individual frames of the measured EPID image sequences. The purpose of this work was to investigate simulating banding artifacts in our cine‐EPID predicted image sequences to improve matching of individual image frames to the acquired image sequence. Increased sensitivity of this method to potential treatment delivery errors would represent an improvement in patient safety and treatment accuracy. Methods: A circuit board was designed and built to capture the target current (TARG‐I) and forward power signals produced by the linac to help model the discrete beam‐formation process of the linac. To simulate the temporal–spatial nature of the EPID readout, a moving read out mask was applied with the timing of the application of the readout mask synchronized to the TARG‐I pulses. Since identifying the timing of the first TARG‐I pulse affected the location of the banding artifacts throughout the image sequence, and furthermore the first several TARG‐I pulses at the beginning of "beam on" are not at full height yet (i.e., dose rate is ramping up), the forward‐power signal was also used to assist in reliable detection of the first radiation pulse of the beam delivery. The predicted EPID cine‐image sequence obtained using a comprehensive physics‐based model was modified to incorporate the discrete nature of the EPID frame readout. This modified banding predicted EPID (MBP‐EPID) image sequence was then compared to its corresponding measured EPID cine‐image sequence on a frame‐by‐frame basis. The EPID was mounted on a Clinac 2100ix linac (Varian Medical Systems, Palo Alto, CA). The field size was set to 21.4 × 28.6 cm2 with no MLC modulation, beam energy of 6 MV, dose rate of 600 MU/min, and 700 MU were delivered for each clockwise (CW) and counter‐clockwise (CCW) arc. No phantoms were placed in the beam. Results: The dose rate ramp up effect was observed at the beginning irradiations, and the identification and timing of the radiation pulses, even during the dose rate ramp up, were able to be quantified using the TARG‐I and forward power signals. The approach of capturing individual dose pulses and synchronizing with the mask image applied to the original predicted EPID image sequence was demonstrated to model the actual EPID readout. The MBP‐EPID image sequences closely reproduced the location and magnitude of the banding features observed in the acquired (i.e., measured) image sequence, for all test irradiations examined here. Conclusions: The banding artifacts observed in the measured EPID cine‐frame sequences were reproduced in the predicted EPID cine‐frames by simulating the discrete temporal–spatial nature of the EPID read out. The MBP‐EPID images showed good agreement qualitatively to the corresponding measured EPID frame sequence of a simple square test field, without any phantom in the beam. This approach will lead to improved image comparison tolerances for real‐time patient dosimetry applications. [ABSTRACT FROM AUTHOR]

Purpose: Radiation treatments are trending toward delivering higher doses per fraction under stereotactic radiosurgery and hypofractionated treatment regimens. There is a need for accurate 3D in vivo patient dose verification using electronic portal imaging device (EPID) measurements. This work presents a model-based technique to compute full three-dimensional patient dose reconstructed from on-treatment EPID portal images (i.e., transmission images). Methods: EPID dose is converted to incident fluence entering the patient using a series of steps which include converting measured EPID dose to fluence at the detector plane and then back-projecting the primary source component of the EPID fluence upstream of the patient. Incident fluence is then recombined with predicted extra-focal fluence and used to calculate 3D patient dose via a collapsed-cone convolution method. This method is implemented in an iterative manner, although in practice it provides accurate results in a single iteration. The robustness of the dose reconstruction technique is demonstrated with several simple slab phantom and nine anthropomorphic phantom cases. Prostate, head and neck, and lung treatments are all included as well as a range of delivery techniques including VMAT and dynamic intensity modulated radiation therapy (IMRT). Results: Results indicate that the patient dose reconstruction algorithm compares well with treatment planning system computed doses for controlled test situations. For simple phantom and square field tests, agreement was excellent with a 2%/2 mm 3D chi pass rate =98.9%. On anthropomorphic phantoms, the 2%/2 mm 3D chi pass rates ranged from 79.9% to 99.9% in the planning target volume (PTV) region and 96.5% to 100% in the low dose region (>20% of prescription, excluding PTV and skin build-up region). Conclusions: An algorithm to reconstruct delivered patient 3D doses from EPID exit dosimetry measurements was presented. The method was applied to phantom and patient data sets, as well as for dynamic IMRT and VMAT delivery techniques. Results indicate that the EPID dose reconstruction algorithm presented in this work is suitable for clinical implementation. [ABSTRACT FROM AUTHOR]