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.

Localization of anatomical changes in patients during proton therapy with in-beam PET monitoring: A voxel-based morphometry approach exploiting Monte Carlo simulations

Kraan A. C.;Berti A.;Belcari N.;Cerello P.;Ciocca M.;Del Sarto D.;Donetti M.;Laruina F.;Mattei I.;Mirabelli R.;Morrocchi M.;Patera V.;Rosso V.;Schiavi A.;Sportelli G.;Bisogni M. G.
2022-01-01

Abstract

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.
2022
Kraan, A. C.; Berti, A.; Retico, A.; Baroni, G.; Battistoni, G.; Belcari, N.; Cerello, P.; Ciocca, M.; De Simoni, M.; Del Sarto, D.; Donetti, M.; Dong, Y.; Embriaco, A.; Ferrero, V.; Fiorina, E.; Fischetti, M.; Franciosini, G.; Giraudo, G.; Laruina, F.; Maestri, D.; Magi, M.; Magro, G.; Mancini Terracciano, C.; Marafini, M.; Mattei, I.; Mazzoni, E.; Mereu, P.; Mirabelli, R.; Mirandola, A.; Morrocchi, M.; Muraro, S.; Patera, A.; Patera, V.; Pennazio, F.; Rivetti, A.; Da Rocha Rolo, M. D.; Rosso, V.; Sarti, A.; Schiavi, A.; Sciubba, A.; Solfaroli Camillocci, E.; Sportelli, G.; Tampellini, S.; Toppi, M.; Traini, G.; Valle, S. M.; Valvo, F.; Vischioni, B.; Vitolo, V.; Wheadon, R.; Bisogni, M. G.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1132928
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