Abstract

Background

Pencil beam scanning proton therapy is a cutting-edge cancer treatment that uses small beams of protons instead of traditional x-rays. These protons stop at a specific depth in the patient, the “Bragg peak”, depending on their initial energy, delivering concentrated energy to tumors while minimizing damage to surrounding healthy tissue. However, we need extremely accurate imaging and detection to ensure the protons hit exactly where intended.

Silicon pixel detectors are semiconductor devices that can precisely track and measure particle interactions. Understanding exactly how these detectors respond to protons is critical for ensuring the accuracy of proton computed tomography. Further, their response to secondary electrons produced by photon interactions in the surrounding absorber material needs to be precisely studied.

Approach

This research investigates mathematical models that predict how charge (electrical signal) spreads through silicon detectors when hit by protons and secondary electrons. We created computer simulations to test different detector designs and configurations, then validated our predictions against real experimental data. Based on the simulated detector response, the performance of downstream tasks is evaluated to gain insights into the implications of using a suboptimal model for this step.

Results

We found that the performance of proton calorimetery is only weakly affected by the detector response model, enabling the usage of a simpler model for future applications. At the same time, the response for secondary electrons must be modeled more precisely to be able to reconstruct the cluster size distributions accurately. This simultaneously improves range verification capabilities in treatment mode.

Significance

Better detector understanding directly translates to safer, more precise cancer treatment. This work provides a foundation for other researchers and clinics using similar technology.

Key Contributions

  • Novel charge collection model for ALPIDE detectors optimized for medical physics applications
  • Validation studies demonstrating model accuracy with parallel Monte Carlo-simulated and experimental data
  • Application impact analysis showing differences between the models for proton computed tomography and range verification
  • Open methodology applicable to other silicon detector technologies

Technologies & Methods

  • Python with scientific computing stack (NumPy, SciPy, PyTorch)
  • GATE/GEANT4 Monte Carlo simulation for particle transport