To quantitatively access penumbra sharpening and scattering by adaptive aperture (AA) under various beam conditions and clinical cases for a Mevion S250i compact pencil beam scanning proton therapy system. Methods: First, in-air measurements were performed using a scintillation detector for single spot profile and lateral penumbra for five square field sizes (3 9 3 to 18 9 18 cm 2 ), three energies (33.04, 147.36, and 227.16 MeV), and three snout positions (5, 15, and 33.6 cm) with Open and AA field. Second, treatment plans were generated in RayStation treatment planning system (TPS) for various combination of target size (3-and 10-cm cube), target depth (5, 10, and 15 cm) and air gap (5-20 cm) for both Open and AA field. These plans were delivered to EDR2 films in the solid water and penumbra reduction by AA was quantified. Third, the effect of the AA scattered protons on the surface dose was studied at 5 mm depth by EDR2 film and the RayStation TPS computation. Finally, dosimetric advantage of AA over Open field was studied for five brain and five prostate cases using the TPS simulation. Results: The spot size changed dramatically from 3.8 mm at proton beam energy of 227.15 MeV to 29.4 mm at energy 33.04 MeV. In-air measurements showed that AA substantially reduced the lateral penumbra by 30% to 60%. The EDR2 film measurements in solid water presented the maximum penumbra reduction of 10 to 14 mm depending on the target size. The maximum increase of 25% in field edge dose at 5 mm depth as compared to central axis was observed. The substantial penumbra reduction by AA produced less dose to critical structures for all the prostate and brain cases. Conclusions: Adaptive aperture sharpens the penumbra by factor of two to three depending upon the beam condition. The absolute penumbra reduction with AA was more noticeable for shallower target, smaller target, and larger air gap. The AA-scattered protons contributed to increase in surface dose. Clinically, AA reduced the doses to critical structures.

Purpose The purpose of this work is to develop machine and deep learning‐based models to predict output and MU based on measured patient quality assurance (QA) data in uniform scanning proton therapy (USPT). Methods This study involves 4,231 patient QA measurements conducted over the last 6 years. In the current approach, output and MU are predicted by an empirical model (EM) based on patient treatment plan parameters. In this study, two MATLAB‐based machine and deep learning algorithms — Gaussian process regression (GPR) and shallow neural network (SNN) — were developed. The four parameters from patient QA (range, modulation, field size, and measured output factor) were used to train these algorithms. The data were randomized with a training set containing 90% and a testing set containing remaining 10% of the data. The model performance during training was accessed using root mean square error (RMSE) and R‐squared values. The trained model was used to predict output based on the three input parameters: range, modulation, and field size. The percent difference was calculated between the predicted and measured output factors. The number of data sets required to make prediction accuracy of GPR and SNN models' invariable was also evaluated. Results The prediction accuracy of machine and deep learning algorithms is higher than the EM. The output predictions with [GPR, SNN, and EM] within ± 2% and ± 3% difference were [97.16%, 97.64%, and 92.95%] and [99.76%, 99.29%, and 97.18%], respectively. The GPR model outperformed the SNN with a smaller number of training data sets. Conclusion The GPR and SNN models outperformed the EM in terms of prediction accuracy. Machine and deep learning algorithms predicted the output factor and MU for USPT with higher predictive accuracy than EM. In our clinic, these models have been adopted as a secondary check of MU or output factors.

A major contributing factor to proton range uncertainty is the conversion of computed tomography (CT) Hounsfield units (HU) to proton relative stopping power (RSP). This uncertainty is heightened in the presence of X-ray beamhardening artifact (BHA), which has two manifestations: cupping and streaking, especially in and near bone tissue. This uncertainty can affect the accuracy of proton RSP calculation for treatment planning in proton radiotherapy. Dualenergy CT (DECT) and iterative beam-hardening correction (iBHC) both show promise in mitigating CT BHA.This present work attempts to analyze the relative robustness of iBHC and DECT techniques on both manifestations of BHA. The stoichiometric method for HU to RSP conversion was used for single-energy CT (SECT) and DECT-based monochromatic techniques using a tissue substitute phantom. Cupping BHA was simulated by measuring the HU of a bone substitute plug in wax/3D-printed phantoms of increasing size. Streaking BHA was simulated by placing a solid water plug between two bone plugs in a wax phantom. Finally, the effect of varying calibration phantom size on RSP was calculated in an anthropomorphic head phantom. The RSP decreased −0.002 cm -1 as phantom size increased for SECT but remained largely constant when iBHC applied or with DECT techniques. The RSP varied a maximum of 2.60% in the presence of streaking BHA in SECT but was reduced to 1.40% with iBHC. For DECT techniques, the maximum difference was 2.40%, reduced to 0.6% with iBHC. Comparing calibration phantoms of 20-and 33-cm diameter, maximum voxel differences of 5 mm in the waterequivalent thickness were observed in the skull but reduced to 1.3 mm with iBHC. The DECT techniques excelled in mitigating cupping BHA, but streaking BHA still could be observed. The use of iBHC reduced RSP variation with BHA in both SECT and DECT techniques.