A Monte Carlo Study of the Relationship between the Time Structures of Prompt Gammas and in vivo Radiation Dose in Proton Therapy

TL;DR

This study introduces a new method for in vivo proton range verification in proton therapy by analyzing the time structure of prompt gammas generated during treatment, validated through Monte Carlo simulations and experimental comparisons.

Contribution

The paper presents a novel approach utilizing the time structure of prompt gammas with a range modulation wheel for accurate in vivo dose verification in proton therapy.

Findings

Monte Carlo simulations closely matched measured PDDs within 2.9%.

Time structure of prompt gammas correlated with proton dose rate within 0.39 ms.

The method enables accurate prediction of in vivo proton range.

Abstract

For the in vivo range verification in proton therapy, it has been tried to measure the spatial distribution of the prompt gammas generated by the proton-induced interactions with the close relationship with the proton dose distribution. However, the high energy of the prompt gammas and background gammas are still problematic in measuring the distribution. In this study, we suggested a new method determining the in vivo range by utilizing the time structure of the prompt gammas formed with the rotation of a range modulation wheel (RMW) in the passive scattering proton therapy. To validate the Monte Carlo code simulating the proton beam nozzle, axial percent depth doses (PDDs) were compared with the measured PDDs with the varying beam range of 4.73-24.01 cm. And the relationship between the proton dose rate and the time structure of the prompt gammas was assessed and compared in the water phantom. The results of the PDD showed accurate agreement within the relative errors of 1.1% in the distal range and 2.9% in the modulation width. Average dose difference in the modulation was assessed as less than 1.3% by comparing with the measurement. The time structure of prompt gammas was well-matched within 0.39 ms with the proton dose rate, and this could enable the accurate prediction of the in vivo range.