Shen, Jiajian

PurposeThe aim of this work was to develop an efficient daily quality assurance (QA) program with strict tolerance levels for pencil beam scanning (PBS) proton radiotherapy featuring simultaneous dosimetric testing on a single, nonuniform field.MethodsA nonuniform field measuring beam output, proton range, and spot position was designed for delivery onto a Sun Nuclear Daily‐QA 3 device. A custom acrylic block permitted simultaneous measurement of low‐ and high‐energy proton ranges in addition to beam output. Sensitivities to output, range, and spot position were evaluated to quantitate the device's response. Reproducibility tests were used to identify and control sources of measurement error as well as to assess the QA procedure's robustness. This procedure was implemented in each of our four treatment rooms independently; 4–6 months of daily QA measurements were collected.ResultsThe 1% output, 0.5 mm range, and 1.5 mm spot position tolerances derived from preliminary tests were tighter overall than tolerances found in the literature and equivalent to the limits used for proton system commissioning. The simplicity and automation of the procedure reduced the time required for daily QA to 10 min per treatment room, and competition for beam between multiple treatment rooms was minimized.ConclusionsAn efficient daily PBS QA procedure can be performed using a single, nonuniform field on a nondedicated QA device. A thorough quantitation of the device's response and careful control of measurement uncertainties allowed daily tolerances to match commissioning standards.

AbstractMonte Carlo (MC) simulation has been used to generate commissioning data for the beam modeling of treatment planning system (TPS). We have developed a method called radial projection (RP) for postprocessing of MC‐simulation‐generated data. We used the RP method to reduce the statistical uncertainty of the lateral profile of proton pencil beams with axial symmetry. The RP method takes advantage of the axial symmetry of dose distribution to use the mean value of multiple independent scores as the representative score. Using the mean as the representative value rather than any individual score results in substantial reduction in statistical uncertainty. Herein, we present the concept and step‐by‐step implementation of the RP method, as well as show the advantage of the RP method over conventional measurement methods for generating lateral profile. Lateral profiles generated by both methods were compared to demonstrate the uncertainty reduction qualitatively, and standard error comparison was performed to demonstrate the reduction quantitatively. The comparisons showed that statistical uncertainty was reduced substantially by the RP method. Using the RP method to postprocess MC data, the corresponding MC simulation time was reduced by a factor of 10 without quality reduction in the generated result from the MC data. We concluded that the RP method is an effective technique to increase MC simulation efficiency for generating lateral profiles for axially symmetric pencil beams.

PurposeTo accurately model the beam delivery time (BDT) for a synchrotron‐based proton spot scanning system using experimentally determined beam parameters.MethodsA model to simulate the proton spot delivery sequences was constructed, and BDT was calculated by summing times for layer switch, spot switch, and spot delivery. Test plans were designed to isolate and quantify the relevant beam parameters in the operation cycle of the proton beam therapy delivery system. These parameters included the layer switch time, magnet preparation and verification time, average beam scanning speeds in x‐ and y‐directions, proton spill rate, and maximum charge and maximum extraction time for each spill. The experimentally determined parameters, as well as the nominal values initially provided by the vendor, served as inputs to the model to predict BDTs for 602 clinical proton beam deliveries. The calculated BDTs (TBDT) were compared with the BDTs recorded in the treatment delivery log files (TLog): ∆t = TLog−TBDT.ResultsThe experimentally determined average layer switch time for all 97 energies was 1.91 s (ranging from 1.9 to 2.0 s for beam energies from 71.3 to 228.8 MeV), average magnet preparation and verification time was 1.93 ms, the average scanning speeds were 5.9 m/s in x‐direction and 19.3 m/s in y‐direction, the proton spill rate was 8.7 MU/s, and the maximum proton charge available for one acceleration is 2.0 ± 0.4 nC. Some of the measured parameters differed from the nominal values provided by the vendor. The calculated BDTs using experimentally determined parameters matched the recorded BDTs of 602 beam deliveries (∆t = −0.49 ± 1.44 s), which were significantly more accurate than BDTs calculated using nominal timing parameters (∆t = −7.48 ± 6.97 s).ConclusionsAn accurate model for BDT prediction was achieved by using the experimentally determined proton beam therapy delivery parameters, which may be useful in modeling the interplay effect and patient throughput. The model may provide guidance on how to effectively reduce BDT and may be used to identifying deteriorating machine performance.

PurposeTo implement and evaluate a novel and fast method for proton range verification by using a planar scintillator and step wedge.MethodsA homogenous proton pencil beam plan with 35 energies was designed and delivered to a 2D flat scintillator with a step wedge. The measurement was repeated 15 times (3 different days, 5 times per day). The scintillator image was smoothed, the Bragg peak and distal fall off regions were fitted by an analytical equation, and the proton range was calculated using simple trigonometry. The accuracy of this method was verified by comparing the measured ranges to those obtained using an ionization chamber and a scanning water tank, the gold standard. The reproducibility was evaluated by comparing the ranges over 15 repeated measurements. The sensitivity was evaluated by delivering to same beam to the system with a film inserted under the wedge.ResultsThe range accuracy of all 35 proton energies measured over 3 days was within 0.2 mm. The reproducibility in 15 repeated measurements for all 35 proton ranges was ±0.045 mm. The sensitivity to range variation is 0.1 mm for the worst case. This efficient procedure permits measurement of 35 proton ranges in less than 3 min. The automated data processing produces results immediately. The setup of this system took less than 5 min. The time saving by this new method is about two orders of magnitude when compared with the time for water tank range measurements.ConclusionsA novel method using a scintillator with a step wedge to measure the proton range was implemented and evaluated. This novel method is fast and sensitive, and the proton range measured by this method was accurate and highly reproducible.

AbstractThis work is to show which is more relevant to cause local failures (LFs) due to patient setup uncertainty between the planning target volume (PTV) underdosage and the potential target underdosage subject to patient setup uncertainties in head and neck (H&N) cancer treated with volumetric‐modulated arc therapy (VMAT). Thirteen LFs in 10 H&N patients treated by VMAT were analyzed. Measures have been taken to minimize the chances of insufficient target delineation for these patients and the patients were clinically determined to have LF based on the PET/CT scan results by an experienced radiologist and then reviewed by a second experienced radiation oncologist. Two methods were used to identify the possible locations of LF due to underdosage: (a) examining the standard VMAT plan, in which the underdosed volume in the nominal dose distribution (UVN) was generated by subtracting the volumes receiving the prescription doses from PTVs, and (b) plan robustness analysis, in which in addition to the nominal dose distribution, six perturbed dose distributions were created by translating the CT iso‐center in three cardinal directions by the PTV margin. The coldest dose distribution was represented by the minimum of the seven doses in each voxel. The underdosed volume in the coldest dose distribution (UVC) was generated by subtracting the volumes receiving the prescription doses in the coldest dose distribution from the volumes receiving the prescription doses in the nominal dose distribution. UVN and UVC were subsequently examined for spatial association with the locations of LF. The association was tested using the binominal distribution and the Fisher's exact test of independence. We found that of 13 LFs, 11 were associated with UVCs (P = 0.011), while three were associated with UVNs (P = 0.99). We concluded that the possible target underdosage due to patient setup uncertainties appeared to be a more relevant factor associated with LF in VMAT for H&N cancer than the compromised PTV coverage at least for the patients included in this study.

Because treatment planning systems (TPSs) generally do not provide monitor units (MUs) for double‐scattered proton plans, models to predict MUs as a function of the range and the nominal modulation width requested of the beam delivery system, such as the one developed by the MGH group, have been proposed. For a given nominal modulation width, however, the measured modulation width depends on the accuracy of the vendor's calibration process and may differ from this nominal value, and also from one beamline to the next. Although such a difference can be replicated in our TPS, the output dependence on range and modulation width for each beam option or suboption has to be modeled separately for each beamline in order to achieve maximal 3% inaccuracy. As a consequence, the MGH output model may not be directly transferable. This work, therefore, serves to extend the model to more general clinic situations. In this paper, a parameterized linear‐quadratic transformation is introduced to convert the nominal modulation width to the measured modulation width for each beam option or suboption on a per‐beamline basis. Fit parameters are derived for each beamline from measurements of 60 reference beams spanning the minimum and maximum ranges, and modulation widths from 2 cm to full range per option or suboption. Using the modeled modulation width, we extract the MGH parameters for the output dependence on range and modulation width. Our method has been tested with 1784 patient‐specific fields delivered across three different beamlines at our facility. For these fields, all measured outputs fall within 3%, and 64.4% fall within 1%, of our model. Using a parameterized linear‐quadratic modulation width, MU calculation models can be established on a per‐beamline basis for each double scattering beam option or suboption.PACS number: 87.53.Qc