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Analytical Computation of Prompt Gamma Ray Emission and Detection for Proton Range Verification

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E Sterpin

E Sterpin1*, G Janssens2 , J Smeets2 , D Prieels2 , S Vynckier1 , (1) Universite catholique de Louvain, Brussels, Belgium, (2) Ion Beam Applications, Louvain-la-neuve, Belgium


TH-C-BRD-1 Thursday 10:15AM - 12:15PM Room: Ballroom D

Purpose: A prompt gamma (PG) slit camera prototype demonstrated that on-line range monitoring within 1-2 mm could be performed by comparing expected and measured PG detection profiles. Monte Carlo (MC) can simulate the expected PG profile but this would result in prohibitive computation time for a complete pencil beam treatment plan. We implemented a much faster method that is based on analytical processing of pre-computed MC data.

Methods: The formation of the PG detection signal can be separated into: 1) production of PGs and 2) detection by the camera detectors after PG transport in geometry. For proton energies from 40 to 230 MeV, PG productions in depth were pre-computed by MC (PENH) for 12C, 14N, 16O, 31P and 40Ca. The PG production was then modeled analytically by adding the PG production for each element according to local proton energy and tissue composition.
PG transport in the patient/camera geometries and the detector response were modeled by convolving the PG production profile with a transfer function. The latter is interpolated from a database of transfer functions fitted to pre-computed MC data (PENELOPE). The database was generated for a photon source in a cylindrical phantom with various radiuses and a camera placed at various positions.
As a benchmark, the analytical model was compared to PENH for a water phantom, a phantom with different slabs (adipose, muscle, lung) and a thoracic CT.

Results: Good agreement (within 5%) was observed between the analytical model and PENH for the PG production. Similar accuracy for detecting range shifts was also observed. Speed of around 250 ms per profile was achieved (single CPU) using a non-optimized MatLab implementation.

Conclusion: We devised a fast analytical model for generating PG detection profiles. In the test cases considered in this study, similar accuracy than MC was achieved for detecting range shifts.

Funding Support, Disclosures, and Conflict of Interest: This research is supported by IBA

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