SUVR quantification using attention-based 3D CNN with longitudinal Florbetapir PET images in Alzheimer’s disease

Florbetapir PET images provide valuable information about the amount of amyloid deposition in the brain due to neurodegenerative diseases, which helps in the prognosis of patients. The purpose of this study is to develop a system that helps in the automatic amyloid quantification of the standard uptake value ratio so that drug treatments could be effectively determined. 2647 Florbetapir PET images obtained from multiple centres of Alzheimer Disease Neuroimaging Initiative (ADNI) and an external dataset of 1413 scans from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s study are used to design and test the proposed 3D CNN attention-based model to quantify the amyloid deposits. Only 80% of scans from the ADNI dataset are used to train the model. The remaining 20% of scans from ADNI and the external dataset are used for testing the trained model. The proposed model achieves a root mean square error of 0.0362 and a mean absolute error of 0.026 on separate hold-out test data from ADNI and a root mean square error of 0.058 and a mean absolute error of 0.044 Anti-Amyloid Treatment in Asymptomatic Alzheimer’s study dataset. A graphical user interface is developed for the proposed model which will display a slice of the Florbetapir PET volume and its predicted standard uptake value ratio. 3D CNN architecture with both spatial and channel attention provided better results when compared to models without attention. The proposed model proves to be an efficient tool in the automatic amyloid standard uptake value ratio quantification.