Localization of anatomical changes in patients during proton therapy with in‐beam PET monitoring: A voxel‐based morphometry approach exploiting Monte Carlo simulations
Authors: Kraan Aafke Christine , Berti Andrea , Retico Alessandra , Baroni Guido , Battistoni Giuseppe , Belcari Nicola , Cerello Piergiorgio , Ciocca Mario , De Simoni Micol , Del Sarto Damiano , Donetti Marco , Dong Yunsheng , Embriaco Alessia , Ferrero Veronica , Fiorina Elisa , Fischetti Marta , Franciosini Gaia , Giraudo Giuseppe , Laruina Francesco , Maestri Davide , Magi Marco , Magro Giuseppe , Mancini Terracciano Carlo , Marafini Michela , Mattei Ilaria , Mazzoni Enrico , Mereu Paolo , Mirabelli Riccardo , Mirandola Alfredo , Morrocchi Matteo , Muraro Silvia , Patera Alessandra , Patera Vincenzo , Pennazio Francesco , Rivetti Angelo , Da Rocha Rolo Manuel Dionisio , Rosso Valeria , Sarti Alessio , Schiavi Angelo , Sciubba Adalberto , Solfaroli Camillocci Elena , Sportelli Giancarlo , Tampellini Sara , Toppi Marco , Traini Giacomo , Valle Serena Marta , Valvo Francesca , Vischioni Barbara , Vitolo Viviana , Wheadon Richard , Bisogni Maria Giuseppina 
Published: November 2021

Purpose

In‐beam positron emission tomography (PET) is one of the modalities that can be used for in vivo noninvasive treatment monitoring in proton therapy. Although PET monitoring has been frequently applied for this purpose, there is still no straightforward method to translate the information obtained from the PET images into easy‐to‐interpret information for clinical personnel. The purpose of this work is to propose a statistical method for analyzing in‐beam PET monitoring images that can be used to locate, quantify, and visualize regions with possible morphological changes occurring over the course of treatment.

Methods

We selected a patient treated for squamous cell carcinoma (SCC) with proton therapy, to perform multiple Monte Carlo (MC) simulations of the expected PET signal at the start of treatment, and to study how the PET signal may change along the treatment course due to morphological changes. We performed voxel‐wise two‐tailed statistical tests of the simulated PET images, resembling the voxel‐based morphometry (VBM) method commonly used in neuroimaging data analysis, to locate regions with significant morphological changes and to quantify the change.

Results

The VBM resembling method has been successfully applied to the simulated in‐beam PET images, despite the fact that such images suffer from image artifacts and limited statistics. Three dimensional probability maps were obtained, that allowed to identify interfractional morphological changes and to visualize them superimposed on the computed tomography (CT) scan. In particular, the characteristic color patterns resulting from the two‐tailed statistical tests lend themselves to trigger alarms in case of morphological changes along the course of treatment.

Conclusions

The statistical method presented in this work is a promising method to apply to PET monitoring data to reveal interfractional morphological changes in patients, occurring over the course of treatment. Based on simulated in‐beam PET treatment monitoring images, we showed that with our method it was possible to correctly identify the regions that changed. Moreover we could quantify the changes, and visualize them superimposed on the CT scan. The proposed method can possibly help clinical personnel in the replanning procedure in adaptive proton therapy treatments.