TOF-ULET: In-beam Stopping Power Estimation using Prompt Gamma Timing towards Adaptive Charged Particle Therapy

2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)(2022)

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摘要
The precision of charged particle therapy dose deposition is its main advantage to conventional radiotherapy and its weakness when encountering range uncertainties in clinical practice. We offer a new perspective on treatment verification by introducing a technique to estimate electronic stopping power during the treatment from the measurement of time between particle target entry and prompt gamma detection (TOF-ULET). For the estimation of electronic stopping power, we developed a lightweight analytical model for axial particle motion inside the patient. We used Monte Carlo simulations of a homogenous PMMA phantom as a first test of our method, achieving ~ 6 % estimation errors for 170 MeV and 189 MeV protons. The in-beam estimation of electronic stopping power opens up new opportunities in treatment adaptation between fractions by not only indicating significant deviations from the treatment plan, but also offering a current estimate of the patients’ anatomy along the beam path and – using conversion models – the delivered dose.
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关键词
Charged Particles,Power Estimation,Stopping Power,Particle Therapy,Charged Particle Therapy,Prompt Gamma,Monte Carlo Simulation,Measurement Time,Treatment Plan,MeV Protons,Homogeneous Phantom,Center Of Mass,Kinetic Energy,Proton Beam,Proton Energy,Particle Path
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