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First experimental time-of-flight-based proton radiography using low gain avalanche diodes.

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|March 12, 2024
PubMed
Summary

This study introduces Sandwich Time-of-Flight Ion Computed Tomography (TOF-iCT), a novel imaging method that eliminates the need for residual energy detectors. The first experimental proton radiograph was successfully generated using this technique, paving the way for more accessible iCT applications.

Keywords:
4D-trackingion computed tomographylow gain avalanche diodeproton therapysandwich TOF-iCTtime-of-flight

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Area of Science:

  • Medical Imaging
  • Particle Physics
  • Radiotherapy

Background:

  • Ion computed tomography (iCT) determines relative stopping power (RSP) distribution using tracking systems and residual energy detectors.
  • Existing iCT methods are complex and costly, limiting clinical feasibility.

Purpose of the Study:

  • To introduce and experimentally validate a novel iCT approach based on time-of-flight (TOF) measurements, termed Sandwich TOF-iCT.
  • To demonstrate that this new method can determine RSP without a residual energy detector.

Main Methods:

  • Developed a small-scale Sandwich TOF-iCT demonstrator utilizing low gain avalanche diodes (LGADs) for precise position and time-of-arrival measurements.
  • Calibrated TOF measurements against water equivalent thickness (WET) using homogeneous PMMA slabs.
  • Acquired proton radiographs (pRads) of an aluminium stair phantom using 83 MeV and 100.4 MeV protons.

Main Results:

  • Successfully recorded the first TOF-based proton radiograph using the Sandwich TOF-iCT demonstrator.
  • Demonstrated the suitability of LGADs as detectors for this novel iCT system.
  • Observed differences between measured and theoretical WET ranging from 37.09% to 51.12% due to simplified calibration models.

Conclusions:

  • Sandwich TOF-iCT is a viable novel approach for ion computed tomography.
  • LGADs are suitable detectors for this compact and potentially cost-efficient iCT system.
  • Further optimization of system parameters and WET estimation algorithms is needed for clinical translation, potentially enhancing treatment planning quality.