Abstract

Proton range verification by ionoacoustic wave sensing is a technique under development for applications in adron therapy as an alternative to nuclear imaging. It provides an acoustic imaging of the proton energy deposition vs. depth using the acoustic wave Time of Flight (ToF). State-of-the-art (based on simulations and experimental results) points out that this detection technique achieves better spatial resolution (< 1 mm) of the proton range comparing with Positron-Emission-Tomography (PET) and prompt gamma ray techniques. This work presents a complete Geant4/k-Wave model that allows to understand several physical phenomena and to evaluate the key parameters that affect the acoustic field generated by the incident proton radiation.

Highlights

  • Oncological hadron therapy utilizes proton beams with energies ranging from 65 MeV to 250 MeV for tumor treatment

  • The model is composed by Geant4, k-Wave and Matlab

  • This model allows to obtain the amplitude of the pressure signal at the end of the acquisition chain and its Signal-to-Noise Ratio (SNR) at different

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Summary

Introduction

Oncological hadron therapy utilizes proton beams with energies ranging from 65 MeV to 250 MeV for tumor treatment. Protons release very little energy at the interface and most of their energy at the end of their range, in an area called the Bragg Peak (BP). Unlike traditional radiotherapy, this technique allows to deliver most of the dose at the tumor while minimizing damage to the surrounding healthy tissue. The model is composed by Geant (proton beam energy deposition), k-Wave (ionoacoustic wave propagation) and Matlab (ultrasound transducer and Analog Front-End model). This model allows to obtain the amplitude of the pressure signal at the end of the acquisition chain and its Signal-to-Noise Ratio (SNR) at different. A case of study with a 19.5 MeV proton beam is here presented, mimicking the setup described in [2], but these techniques can be used for higher and more clinically relevant energies

Geant4 Model
Analog Front-End Model
Results
Conclusions
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