In-beam pet monitoring for adaptive proton therapy: A voxel-based morphometry approach based on FLUKA Monte Carlo simulations.
Berti A., Kraan A., Retico A., Battistoni G., Del Sarto D., Ferrero V., Fiorina E., Laruina F., Mazzoni E., Morrocchi M., Pennazio F., Rosso V., Sportelli G., Vitolo V., Bisogni G.
One of the modalities for $in vivo$, non-invasive treatment monitoring in proton therapy is in-beam PET. Yet, no straightforward method exists to translate the information from PET images into easy to interpret information for physicians. We propose a new approach for analyzing in-beam PET monitoring images to locate, quantify and visualize morphological changes occurring over the treatment course. We thus selected a patient treated with proton therapy whose sinonasal cavity emptied over the treatment course. We generated a series of artificially modified CT scans to mimic this gradual emptying. Using a dual-head PET system geometry, we performed Monte Carlo simulations of the treatment to investigate monitoring-PET signal changes. Next, we performed two-tailed voxel-wise statistical tests to locate and quantify regions with significant morphological changes. As a result, we successfully applied our method to simulated in-beam PET images, generating three-dimensional probability maps to visualize morphological changes in the CT. Their characteristic color patterns are valuable to trigger an alarm in case of morphological changes over the treatment course.