Zeitschrift für Medizinische Physik (Nov 2023)

Development and validation of an optimal GATE model for proton pencil-beam scanning delivery

  • Ali Asadi,
  • Azadeh Akhavanallaf,
  • Seyed Abolfazl Hosseini,
  • Naser Vosoughi,
  • Habib Zaidi

Journal volume & issue
Vol. 33, no. 4
pp. 591 – 600

Abstract

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Objective: To develop and validate a versatile Monte Carlo (MC)-based dose calculation engine to support MC-based dose verification of treatment planning systems (TPSs) and quality assurance (QA) workflows in proton therapy. Methods: The GATE MC toolkit was used to simulate a fixed horizontal active scan-based proton beam delivery (SIEMENS IONTRIS). Within the nozzle, two primary and secondary dose monitors have been designed to enable the comparison of the accuracy of dose estimation from MC simulations with respect to physical QA measurements. The developed beam model was validated against a series of commissioning measurements using pinpoint chambers and 2D array ionization chambers (IC) in terms of lateral profiles and depth dose distributions. Furthermore, beam delivery module and treatment planning has been validated against the literature deploying various clinical test cases of the AAPM TG‐119 (c-shape phantom) and a prostate patient. Results: MC simulations showed excellent agreement with measurements in the lateral depth-dose parameters and spread-out Bragg peak (SOBP) characteristics within a maximum relative error of 0.95 mm in range, 1.83% in entrance to peak ratio, 0.27% in mean point-to-point dose difference, and 0.32% in peak location. The mean relative absolute difference between MC simulations and measurements in terms of absorbed dose in the SOBP region was 0.93% ± 0.88%. Clinical phantom studies showed a good agreement compared to research TPS (relative error for TG-119 planning target volume PTV-D95 ∼ 1.8%; and for prostate PTV-D95 ∼ −0.6%). Conclusion: We successfully developed a MC model for the pencil beam scanning system, which appears reliable for dose verification of the TPS in combination with QA information, prior to patient treatment.

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