A method for quantitative evaluations of scanning‐proton dose distributions

Abstract

Purpose Patient‐Specific Quality Assurance (PSQA) measurement analysis depends on generating metrics representative of calculation and measurement agreement. Considering the heightened capability of discrete spot scanning protons to modulate individual dose voxels, a dose plane comparison approach that maintained all of the capabilities of the well‐established γ test, but that also provided a more intuitive error parameterization, was desired. Methods Analysis was performed for 300 dose planes compared by searching all calculated points within a fixed radius around each measured pixel to determine the dose deviation. Dose plane agreement is reported as the dose difference minimum (DDM) within an empirically established search radius: ΔDmin(r). This per‐pixel metric is aggregated into a histogram binned by dose deviation. Search‐radius criteria were based on a weighted‐beamlet 3σ spatial deviation from imaging isocenter. Equipment setup error was mitigated during analysis using tracked image registration, ensuring beamlet deviations to be the dominant source of spatial error. The percentage of comparison points with <3% dose difference determined pass rate. Results The mean beamlet radial deviation was 0.38mm from x‐ray isocenter, with a standard deviation of 0.19mm, such that 99.9% of relevant pencil beams were within 1 mm of nominal. The dose‐plane comparison data showed no change in passing rate between a 3%/1mm ΔDmin(r) analysis (97.6 +/‐ 3.6%) and a 3%/2mm γ test (97.7 +/‐ 3.2%). Conclusions PSQA dose‐comparison agreements corresponding to a search radius outside of machine performance limits are likely false positives. However, the elliptical shape of the γ test is too dose‐restrictive with a spatial‐error threshold set at 1 mm. This work introduces a cylindrical search shape, proposed herein as more relevant to plan quality, as part of the new DDM planar‐dose comparison algorithm. DDM accepts all pixels within a given dose threshold inside the search radius, and carries forward plan‐quality metrics in a straightforward manner for evaluation.

Beam mask and sliding window‐facilitated deep learning‐based accurate and efficient dose prediction for pencil beam scanning proton therapy

“…The dose predictions for all the testing cases were completed within 0.25 s. The improved predicted PBSPT doses using our proposed beam mask and sliding window methods met our institutional clinical requirement to have a 3D Gamma passing of 95% with a criterion of 3%/3 mm/10%. [80][81][82] In this study, we introduced the beam mask and sliding window methods to improve the performance of PBSPT dose prediction. For the beam mask method, we observed improvements in DVH index accuracy such as D2, D98, and Dmean, for both targets and OARs with bigger improvements in spinal cord Dmax.…”

“…for the testing prostate cases and 96.55 ± 0.34%, 95.09 ± 0.61%, and 92.79 ± 0.53% for the testing lung cases within target, OARs, and BODY, respectively. The dose predictions for all the testing cases were completed within 0.25 s. The improved predicted PBSPT doses using our proposed beam mask and sliding window methods met our institutional clinical requirement to have a 3D Gamma passing of 95% with a criterion of 3%/3 mm/10% 80–82 …”