Fifty-seventh annual meeting of the American association of physicists in medicine
TH-CD-BRA-03: Experimental Validation of An Analytical Model for Prompt Gamma Imaging
A prompt gamma (PG) slit camera prototype recently demonstrated a 1–2 mm accuracy to detect proton range shifts at clinical beam currents by comparing an expected PG detection profile to a measured one. An analytical model has been recently developed to compute the expected profile at practical speed (< 1 s). We present here its benchmark against measurements in heterogeneous phantoms.
The analytical model is based on the computation of PG emission from Monte Carlo (MC) pre-generated PG emission profiles and the convolution of the emission profile with a transfer function that takes into account patient geometry and camera positioning. For the experimental benchmark, three phantom configurations were chosen, all built with slabs of tissue-equivalent plastics: 1) one homogeneous large slab (either adipose, bone or water); 2) slabs mimicking a typical succession of tissues in the brain (PMMA-adipose-bone-PMMA-water); 3) the same but for the lung with a geometry prone to range mixing effects (water-muscle followed by a mix of lung and muscle equivalent materials with the interface parallel to the beam). Several incident energies were selected, respectively: 1) 100, 150 and 200 MeV; 2) 115 and 145 MeV; 3) 110 MeV. The PG signal was measured by the PG camera placed at the position determined by the analytical model. Additional benchmark was performed using MC simulation with PENH.
For the analytical model versus measurements, agreement for range estimation was within 2.7 mm on average (1.4 mm standard deviation). Agreement between MC and experiments was similar. Finally, the analytical model reproduced MC predictions within 1 mm.
Overall, excellent agreement was achieved with MC simulations and promising agreement with measurements was observed. The analytical model can potentially be used to select some probe spots that would be good candidates to verify the range prior to treatment delivery.
Work supported by IBA