Deriving Physiological Information from PET Images Using Machine Learning

Abstract

AbstractMachine learning (ML) algorithms have become popular in recent years and have found increasing utility in the field of medical imaging, specifically in positron emission tomography (PET) imaging. The interest in ML in PET imaging for the study of neurodegenerative diseases stems from the potential of these techniques to analyze and predict the physiological parameters of biomarkers such as the total volume of distribution (V$$_{\text {t}}$$ t ) in the organ or a structure of the organ to be explored. In this paper, we investigated whether the V$$_{\text {t}}$$ t of [$$^{18}$$ 18 F]-FEPPA radiotracer, an indicator of neuroinflammation, could be estimated directly in a non-invasive way, given the activity of the radiotracer in brain tissue. The study used several regression models to predict the [$$^{18}$$ 18 F]-FEPPA V$$_{\text {t}}$$ t in different brain regions where 31 regions of interest were defined for each of 24 patients with Parkinson disease and 20 healthy subjects, and were used to train four tree-based regression models. The predicted and reference values were compared by Bland-Altman analysis and regression model’s performance was evaluated by the mean absolute error (MAE). The best result was obtained by the XGBoost model with a MAE of 2.6. Bland-Altman analysis results indicate that predicted V$$_{\text {t}}$$ t are in average very close to the reference with a bias of 0.23 "Image missing" 2.82. Significant main effect of genotype on [$$^{18}$$ 18 F]-FEPPA in both caudate and putamen have been preserved by predicted Vt values (p < 0.05). The results of paired t-test indicate that the difference between predicted and reference V$$_{\text {t}}$$ t is not statistically significant in 6 out of 8 groups. The proposed algorithms provide a non-invasive and efficient tool to predict [$$^{18}$$ 18 F]-FEPPA V$$_{\text {t}}$$ t values, a hallmark of neuroinflammation that is believed to be a potential trigger for Parkinson’s disease development.