A model-based 3D patient-specific pre-treatment QA method for VMAT using the EPID.

This study reports the development and validation of a model-based, 3D patient dose reconstruction method for pre-treatment quality assurance using EPID images. The method is also investigated for sensitivity to potential MLC delivery errors. Each cine-mode EPID image acquired during plan delivery was processed using a previously developed back-projection dose reconstruction model providing a 3D dose estimate on the CT simulation data. Validation was carried out using 24 SBRT-VMAT patient plans by comparing: (1) ion chamber point dose measurements in a solid water phantom, (2) the treatment planning system (TPS) predicted 3D dose to the EPID reconstructed 3D dose in a solid water phantom, and (3) the TPS predicted 3D dose to the EPID and our forward predicted reconstructed 3D dose in the patient (CT data). AAA and AcurosXB were used for TPS predictions. Dose distributions were compared using 3%/3 mm (95% tolerance) and 2%/2 mm (90% tolerance) γ-tests in the planning target volume (PTV) and 20% dose volumes. The average percentage point dose differences between the ion chamber and the EPID, AcurosXB, and AAA were 0.73 ± 1.25%, 0.38 ± 0.96% and 1.06 ± 1.34% respectively. For the patient (CT) dose comparisons, seven (3%/3 mm) and nine (2%/2 mm) plans failed the EPID versus AAA. All plans passed the EPID versus Acuros XB and the EPID versus forward model γ-comparisons. Four types of MLC sensitive errors (opening, shifting, stuck, and retracting), of varying magnitude (0.2, 0.5, 1.0, 2.0 mm), were introduced into six different SBRT-VMAT plans. γ-comparisons of the erroneous EPID dose and original predicted dose were carried out using the same criteria as above. For all plans, the sensitivity testing using a 3%/3 mm γ-test in the PTV successfully determined MLC errors on the order of 1.0 mm, except for the single leaf retraction-type error. A 2%/2 mm criteria produced similar results with two more additional detected errors.

Frame average optimization of cine-mode EPID images used for routine clinical in vivo patient dose verification of VMAT deliveries.

PURPOSE: The in vivo 3D dose delivered to a patient during volumetric modulated arc therapy (VMAT) delivery can be calculated using electronic portal imaging device (EPID) images. These images must be acquired in cine-mode (i.e., "movie" mode) in order to capture the time-dependent delivery information. The angle subtended by each cine-mode EPID image during an arc can be changed via the frame averaging number selected within the image acquisition software. A large frame average number will decrease the EPID's angular resolution and will result in a decrease in the accuracy of the dose information contained within each image. Alternatively, less EPID images acquired per delivery will decrease the overall 3D patient dose calculation time, which is appealing for large-scale clinical implementation. Therefore, the purpose of this study was to determine the optimal frame average value per EPID image, defined as the highest frame averaging that can be used without an appreciable loss in 3D dose reconstruction accuracy for VMAT treatments. METHODS: Six different VMAT plans and six different SBRT-VMAT plans were delivered to an anthropomorphic phantom. Delivery was carried out on a Varian 2300ix model linear accelerator (Linac) equipped with an aS1000 EPID running at a frame acquisition rate of 7.5 Hz. An additional PC was set up at the Linac console area, equipped with specialized frame-grabber hardware and software packages allowing continuous acquisition of all EPID frames during delivery. Frames were averaged into "frame-averaged" EPID images using matlab. Each frame-averaged data set was used to calculate the in vivo dose to the patient and then compared to the single EPID frame in vivo dose calculation (the single frame calculation represents the highest possible angular resolution per EPID image). A mean percentage dose difference of low dose (<20% prescription dose) and high dose regions (>80% prescription dose) was calculated for each frame averaged scenario for each plan. The authors defined their unacceptable loss of accuracy as no more than a ±1% mean dose difference in the high dose region. Optimal frame average numbers were then determined as a function of the Linac's average gantry speed and the dose per fraction. RESULTS: The authors found that 9 and 11 frame averages were suitable for all VMAT and SBRT-VMAT treatments, respectively. This resulted in no more than a 1% loss to any of the dose region's mean percentage difference when compared to the single frame reconstruction. The optimized number was dependent on the treatment's dose per fraction and was determined to be as high as 14 for 12 Gy/fraction (fx), 15 for 8 Gy/fx, 11 for 6 Gy/fx, and 9 for 2 Gy/fx. CONCLUSIONS: The authors have determined an optimal EPID frame averaging number for multiple VMAT-type treatments. These are given as a function of the dose per fraction and average gantry speed. These optimized values are now used in the authors' clinical, 3D, in vivo patient dosimetry program. This provides a reduction in calculation time while maintaining the authors' required level of accuracy in the dose reconstruction.

An in vivo dose verification method for SBRT-VMAT delivery using the EPID.

PURPOSE: Radiation treatments have become increasingly more complex with the development of volumetric modulated arc therapy (VMAT) and the use of stereotactic body radiation therapy (SBRT). SBRT involves the delivery of substantially larger doses over fewer fractions than conventional therapy. SBRT-VMAT treatments will strongly benefit from in vivo patient dose verification, as any errors in delivery can be more detrimental to the radiobiology of the patient as compared to conventional therapy. Electronic portal imaging devices (EPIDs) are available on most commercial linear accelerators (Linacs) and their documented use for dosimetry makes them valuable tools for patient dose verification. In this work, the authors customize and validate a physics-based model which utilizes on-treatment EPID images to reconstruct the 3D dose delivered to the patient during SBRT-VMAT delivery. METHODS: The SBRT Linac head, including jaws, multileaf collimators, and flattening filter, were modeled using Monte Carlo methods and verified with measured data. The simulation provides energy spectrum data that are used by their "forward" model to then accurately predict fluence generated by a SBRT beam at a plane above the patient. This fluence is then transported through the patient and then the dose to the phosphor layer in the EPID is calculated. Their "inverse" model back-projects the EPID measured focal fluence to a plane upstream of the patient and recombines it with the extra-focal fluence predicted by the forward model. This estimate of total delivered fluence is then forward projected onto the patient's density matrix and a collapsed cone convolution algorithm calculates the dose delivered to the patient. The model was tested by reconstructing the dose for two prostate, three lung, and two spine SBRT-VMAT treatment fractions delivered to an anthropomorphic phantom. It was further validated against actual patient data for a lung and spine SBRT-VMAT plan. The results were verified with the treatment planning system (TPS) (ECLIPSE AAA) dose calculation. RESULTS: The SBRT-VMAT reconstruction model performed very well when compared to the TPS. A stringent 2%/2 mm χ-comparison calculation gave pass rates better than 91% for the prostate plans, 88% for the lung plans, and 86% for the spine plans for voxels containing 80% or more of the prescribed dose. Patient data were 86% for the lung and 95% for the spine. A 3%/3 mm χ-comparison was also performed and gave pass rates better than 93% for all plan types. CONCLUSIONS: The authors have customized and validated a robust, physics-based model that calculates the delivered dose to a patient for SBRT-VMAT delivery using on-treatment EPID images. The accuracy of the results indicates that this approach is suitable for clinical implementation. Future work will incorporate this model into both offline and real-time clinical adaptive radiotherapy.