Room: AAPM ePoster Library
Purpose: Proton radiography with a magnetic lens system could provide instantaneous imaging for proton therapy and generate a map of proton stopping power, significantly reducing position error and facilitating a more accurate dose calculation. Here, the 800-MeV proton radiography (pRad) system at the Los Alamos Neutron Science Center (LANSCE) is modeled using the TOol for PArticle Simulation (TOPAS). The resulting transport model is a deployable, open-source Monte Carlo package that can be used for simulations of proton and heavy ion therapy treatments and concurrent particle imaging.
Methods: A proton flux of 107 particles per simulation with a Gaussian beam spread of s = 0.85 cm strikes a tantalum diffuser foil. The beam is transported through three quadrupoles that provide the matching conditions that produce a Fourier plane (collimation point) within the downstream lens system. The imaging lens is constructed of four magnetic quadrupole fields. At the imaging plane, several phantoms designed to mimic those used in PET and SPECT quality assurance were used to assess the resolution of the system and amount of contrast agent needed. The detector is a 15 cm x 15 cm x 0.2 cm LYSO panel.
Results: Signal-to-noise ratios (SNRs) were calculated for each object in each phantom; the highest SNR was seen for the smallest objects and lowest-Z materials due to the increase in transmission (statistics) through those materials.
Conclusion: The TOPAS pRad model can simulate the performance of an instantaneous proton radiography system. As expected, the model shows that gold has the highest level of contrast and 68Gallium had an acceptable level of contrast for proton imaging. Results confirmed the poor tissue contrast seen in proton radiography using water as a tissue-equivalent material. Future work will focus on validating these results experimentally using a mouse model with 68Gallium-DOTATATE or gold nanoparticles as contrast agents
Funding Support, Disclosures, and Conflict of Interest: This project was supported in part by the Graduate Fellowship for STEM Diversity