RESUMEN
PURPOSE: The International Atomic Energy Agency (IAEA) and the American Association of Physicists in Medicine (AAPM) have jointly published a new code of practice (CoP), TRS483, for the dosimetry of small static photon fields used in external beam radiotherapy. It gave recommendations on how to perform reference dosimetry in nonstandard machine-specific reference (msr) fields and measure field output factors in small fields. The purpose of this work was to perform a dosimetric evaluation of the recommendations given in this CoP. METHODS: All measurements were done in a Varian TrueBeam™ STx linear accelerator. Five ionization chambers were used for beam quality measurements, four Farmer type ionization chambers for performing reference dosimetry and two diodes for performing field output factor measurements. Field output factor measurements were done for fourteen field sizes (ranging from 0.5 cm × 0.5 cm to 10 cm × 10 cm). Beam energies used were: 6 MV WFF, 6 MV FFF, 10 MV WFF, and 10 MV FFF. Where appropriate, results from this study were compared with those obtained from the recommendations given in the IAEA TRS398 CoP, AAPM TG51 and TG51 Addendum protocols. RESULTS: Beam quality measurements show that for all beam energies and for equivalent square msr field sizes ranging from 4 cm × 4 cm to 10 cm × 10 cm, agreement between calculated and measured values of TPR20,10 (10) was within 0.6%. When %dd(10,10)X was used as beam quality specifier, the agreement was found to be within 0.8%. Absorbed dose to water per unit monitor unit at the depth of maximum dose zmax in a beam of quality Q, Dw,Qzmax/MU, was determined using both %dd(10,10)X and TPR20,10 (10) as beam quality specifiers. Measured ratios of Dw,Q (zmax )/MU, determined using the two approaches, ranged between 0.999 and 1.000 for all the beam energies investigated. Comparison with TRS398, TG51 and TG51 addendum protocols show that depending on beam energy, the mean values of the ratios TRS398/TRS483, TG51/TRS483, and TG51 Addendum/TRS483 of Dw,Q (zmax )/MU determined using both approaches show excellent agreement with TRS398 CoP (to within 0.05%); agreement with TG51 and TG51 addendum was to within 0.3% for all four beam energies investigated. Field output factors, determined using the two methods recommended in the TRS483 CoP, showed excellent agreement between the two methods. For the 1 cm collimator field size, the mean value of the field output factor obtained from an average of the two detectors investigated was found to be 2% lower than the mean value of the uncorrected ratio of readings. CONCLUSION: For beams with and without flattening filters, the values of Dw,Q (zmax )/MU obtained following the new CoP are found to be consistent with those obtained using TRS398, TG51 and TG51 addendum protocols to within 0.3%. Field output factors for small beams can be improved when the correction factors for different detectors included in TRS483 are appropriately incorporated into their dosimetry.
RESUMEN
PURPOSE: The accurate prediction of intrafraction lung tumor motion is required to compensate for system latency in image-guided adaptive radiotherapy systems. The goal of this study was to identify an optimal prediction model that has a short learning period so that prediction and adaptation can commence soon after treatment begins, and requires minimal reoptimization for individual patients. Specifically, the feasibility of predicting tumor position using a combination of a generalized (i.e., averaged) neural network, optimized using historical patient data (i.e., tumor trajectories) obtained offline, coupled with the use of real-time online tumor positions (obtained during treatment delivery) was examined. METHODS: A 3-layer perceptron neural network was implemented to predict tumor motion for a prediction horizon of 650 ms. A backpropagation algorithm and batch gradient descent approach were used to train the model. Twenty-seven 1-min lung tumor motion samples (selected from a CyberKnife patient dataset) were sampled at a rate of 7.5 Hz (0.133 s) to emulate the frame rate of an electronic portal imaging device (EPID). A sliding temporal window was used to sample the data for learning. The sliding window length was set to be equivalent to the first breathing cycle detected from each trajectory. Performing a parametric sweep, an averaged error surface of mean square errors (MSE) was obtained from the prediction responses of seven trajectories used for the training of the model (Group 1). An optimal input data size and number of hidden neurons were selected to represent the generalized model. To evaluate the prediction performance of the generalized model on unseen data, twenty tumor traces (Group 2) that were not involved in the training of the model were used for the leave-one-out cross-validation purposes. RESULTS: An input data size of 35 samples (4.6 s) and 20 hidden neurons were selected for the generalized neural network. An average sliding window length of 28 data samples was used. The average initial learning period prior to the availability of the first predicted tumor position was 8.53 ± 1.03 s. Average mean absolute error (MAE) of 0.59 ± 0.13 mm and 0.56 ± 0.18 mm were obtained from Groups 1 and 2, respectively, giving an overall MAE of 0.57 ± 0.17 mm. Average root-mean-square-error (RMSE) of 0.67 ± 0.36 for all the traces (0.76 ± 0.34 mm, Group 1 and 0.63 ± 0.36 mm, Group 2), is comparable to previously published results. Prediction errors are mainly due to the irregular periodicities between cycles. Since the errors from Groups 1 and 2 are within the same range, it demonstrates that this model can generalize and predict on unseen data. CONCLUSIONS: This is a first attempt to use an averaged MSE error surface (obtained from the prediction of different patients' tumor trajectories) to determine the parameters of a generalized neural network. This network could be deployed as a plug-and-play predictor for tumor trajectory during treatment delivery, eliminating the need for optimizing individual networks with pretreatment patient data.