A C Kraan¹*, A Berti², A Retico¹, G Battistoni³, P Cerello⁴, D Del Sarto², V Ferrero⁴, E Fiorina^4,5, F Laruina², E Mazzoni¹, M Morrocchi^2,1, F Pennazio⁴, V Rosso^2,1, G Sportelli^2,1, V Vitolo⁵, G Bisogni^2,1, (1) Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy, (2) University Of Pisa, Pisa, Italy,(3) Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Milano, Italy (4) Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy (5) Centro Nazionale di Adroterapia Oncologica, Pavia, Italy

PO-GePV-M-97 (Sunday, 7/25/2021)   [Eastern Time (GMT-4)]

Purpose: In-beam Positron Emission Tomography (PET) can be used for in-vivo non-invasive treatment monitoring in proton therapy. Although this modality has been frequently applied, no straightforward method exists to translate the PET images into easy to interpret information for clinical personnel. The purpose of this work is to propose an innovative statistical method for analyzing in-beam PET monitoring images to locate possible morphological changes occurring over the course of treatment, and to quantify and easily visualize them.

Methods: We investigated the impact of anatomical changes on the in-beam PET signal of a patient with Squamous Cell Carcinoma, that presented sinonasal cavity emptying during the course of treatment. For this purpose, we artificially created a set of CT scans with various volume changes of the cavity region and performed a vast amount of Monte Carlo simulations of a proton therapy treatment to model PET signal, using the characteristics of a dual head in-beam PET system. We performed voxel-wise two-tailed statistical tests, resembling the Voxel-Based Morphometry (VBM) method commonly used in neuroimaging data analyses, to locate regions with significant morphological changes.

Results: The VBM resembling method could be applied to the simulated in-beam PET images, despite the image artifacts and limited statistics. Three-dimensional probability maps were obtained, that allowed to locate and quantify interfractional morphological changes. The characteristic color patterns resulting from the method is appropriate to trigger alarms if morphological changes occur along the course of treatment, resulting in an under- and/or overdose exposure.

Conclusion: Our method is promising to apply to in-beam PET monitoring data. Using simulated PET monitoring images, we showed that the method allowed to correctly identify the regions that changed, and to quantify and visualize them in a straightforward manner. This new approach is promising and possibly brings us a step closer to dose reconstruction.

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