Purpose Intensity‐modulated radiation therapy (IMRT) utilizes many small fields for producing a uniform dose distribution. Therefore, there are many field junctions in the target region, and resulting dose uncertainties are accumulated. However, such accumulation of the dose uncertainty has not been implemented in the current practice of IMRT dose verification. The purpose of this study is to develop a method to predict the gamma passing rate (GPR) using a dose uncertainty accumulation model. Methods Thirty‐three intensity‐modulated (IM) beams for head‐and‐neck cases with step‐and‐shoot techniques were used in this study. The treatment plan was created using the XiO treatment planning system (TPS). The IM beam was produced by the ONCOR Impression Plus linear accelerator. MapCHECK was used to measure the dose distribution. The distribution of a dose uncertainty potential (DUP) was generated by in‐house software that accumulated field shapes weighted by a segmental monitor unit, followed by Gaussian folding. The width of the Gaussian was determined from the width of the lateral penumbra. The dose difference between the calculated and measured doses was compared with the estimated DUP at each point. The GPR of each beam was predicted for 2%/2‐mm, 3%/2‐mm, and 3%/3‐mm tolerances by its own DUP histogram and a GPR‐vs‐DUP correlation of other beams using the leave‐one‐out cross‐validation method. The predicted GPR was compared with the measured GPR to evaluate the performance of this prediction method. The criteria for the predicted GPR corresponding to a measured GPR ≥ 90% were estimated to examine the feasibility of estimating the measured GPR by this GPR prediction method. Results The DUP was confirmed to have proportionality to the standard deviation (SD) of the dose difference. The SDs of the difference between the measured and predicted GPRs were 3.1, 1.7, and 1.4% for 2%/2‐mm, 3%/2‐mm, and 3%/3‐mm tolerances, respectively. The criteria of the predicted GPR corresponding to the measured GPR ≥ 90% were 94.1 and 95.0% with confidence levels of 99 and 99.9%, respectively. Conclusion In this study, we confirmed the good proportionality between the dose difference and the estimated DUP. The results showed a feasibility to predict the dose difference from DUP as estimated by a DUP accumulation model. The predicted GPR developed in this study showed good accuracy for planar dose distributions of head and neck IMRT. The prediction method developed in this study is considered to be feasible as a substitute for the current practice of measurement‐based verification of the dose distribution with gamma analysis.
Purpose We aim to develop a method to predict the gamma passing rate (GPR) of a three‐dimensional (3D) dose distribution measured by the Delta4 detector system using the dose uncertainty potential (DUP) accumulation model. Methods Sixty head‐and‐neck intensity‐modulated radiation therapy (IMRT) treatment plans were created in the XiO treatment planning system. All plans were created using nine step‐and‐shoot beams of the ONCOR linear accelerator. Verification plans were created and measured by the Delta4 system. The planar DUP (pDUP) manifesting on a field edge was generated from the segmental aperture shape with a Gaussian folding on the beam's‐eye view. The DUP at each voxel (u) was calculated by projecting the pDUP on the Delta4 phantom with its attenuation considered. The learning model (LM), an average GPR as a function of the DUP, was approximated by an exponential function aGPRu=e-qu to compensate for the low statistics of the learning data due to a finite number of the detectors. The coefficient q was optimized to ensure that the difference between the measured and predicted GPRs (dGPR) was minimized. The standard deviation (SD) of the dGPR was evaluated for the optimized LM. Results It was confirmed that the coefficient q was larger for tighter tolerance. This result corresponds to the expectation that the attenuation of the aGPRu will be large for tighter tolerance. The pGPR and mGPR were observed to be proportional for all tolerances investigated. The SD of dGPR was 2.3, 4.1, and 6.7% for tolerances of 3%/3 mm, 3%/2 mm, 2%/2 mm, respectively. Conclusion The DUP‐based predicting method of the GPR was extended to 3D by introducing DUP attenuation and an optimized analytical LM to compensate for the low statistics of the learning data due to a finite number of detector elements. The precision of the predicted GPR is expected to be improved by improving the LM and by involving other metrics.
We assessed the usefulness of PET/CT images to determine the target volume in radiotherapy planning by evaluating the standardized uptake value (SUV). We evaluated the imaging conditions and image-reconstruction conditions of PET/CT useful for treatment planning by collecting (18)F-FDG images of acrylic spheres (10-48 mm in diameter) in a phantom. The (18)F-FDG concentration in the spheres was 10-fold higher than that of the phantom. The contours were delineated according to the SUV by the threshold and regions of interest (ROI) methods. Comparisons of two- and three-dimensional (2D and 3D) acquisition images indicated that the sharpness and quantitative qualities of the sphere boundaries were better in the former than in the latter. In the extraction of outlines using the SUV, outlines obtained at an SUV of 40-50% of the maximum agreed well with the actual acrylic sphere size. 2D acquisition images are more suitable for delineating target volume contours, although 3D acquisition images are more popular in diagnostic imaging. An SUV of 40-50% of the maximum is suggested to be appropriate for GTV contouring of sphere tumors with homogenously distributed (18)F-FDG.
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