Implementation of EPID transit dosimetry based on a through-air dosimetry algorithm.
PURPOSE
A method to perform transit dosimetry with an electronic portal imaging device (EPID) by extending the commercial implementation of a published through-air portal dose image (PDI) prediction algorithm Van Esch et al. [Radiother. Oncol. 71, 223-234 (2004)] is proposed and validated. A detailed characterization of the attenuation, scattering, and EPID response behind objects in the beam path is used to convert through-air PDIs into transit PDIs.


METHODS
The EPID detector response beyond a range of water equivalent thicknesses (0-35 cm) and field sizes (3×3 to 22.2×29.6 cm(2)) was analyzed. A constant air gap between the phantom exit surface and the EPID was utilized. A model was constructed that accounts for the beam's attenuation along the central axis, the presence of phantom scattered radiation, the detector's energy dependent response, and the difference in EPID off-axis pixel response relative to the central pixel. The efficacy of the algorithm was verified by comparing predicted and measured PDIs for IMRT fields delivered through phantoms of increasing complexity.


RESULTS
The expression that converts a through-air PDI to a transit PDI is dependent on the object's thickness, the irradiated field size, and the EPID pixel position. Monte Carlo derived narrow-beam linear attenuation coefficients are used to model the decrease in primary fluence incident upon the EPID due to the object's presence in the beam. This term is multiplied by a factor that accounts for the broad beam scatter geometry of the linac-phantom-EPID system and the detector's response to the incident beam quality. A 2D Gaussian function that models the nonuniformity of pixel response across the EPID detector plane is developed. For algorithmic verification, 49 IMRT fields were repeatedly delivered to homogeneous slab phantoms in 5 cm increments. Over the entire set of measurements, the average area passing a 3%∕3mm gamma criteria slowly decreased from 98% for no material in the beam to 96.7% for 35 cm of material in the beam. The same 49 fields were delivered to a heterogeneous slab phantom and on average, 97.1% of the pixels passed the gamma criteria. Finally, a total of 33 IMRT fields were delivered to the anthropomorphic phantom and on average, 98.1% of the pixels passed. The likelihood of good matches was independent of anatomical site.


CONCLUSIONS
A prediction of the transit PDI behind a phantom or patient can be created for the purposes of treatment verification via an extension of the Van Esch through-air PDI algorithm. The results of the verification measurements through phantoms indicate that further investigation through patients during their treatments is warranted.