  Alzheimer's disease (AD) is a progressive neurodegenerative disorder leading
to cognitive decline. [$^{18}$F]-Fluorodeoxyglucose positron emission
tomography ([$^{18}$F]-FDG PET) is used to monitor brain metabolism, aiding in
the diagnosis and assessment of AD over time. However, the feasibility of
multi-time point [$^{18}$F]-FDG PET scans for diagnosis is limited due to
radiation exposure, cost, and patient burden. To address this, we have
developed a predictive image-to-image translation (I2I) model to forecast
future [$^{18}$F]-FDG PET scans using baseline and year-one data. The proposed
model employs a convolutional neural network architecture with long-short term
memory and was trained on [$^{18}$F]-FDG PET data from 161 individuals from the
Alzheimer's Disease Neuroimaging Initiative. Our I2I network showed high
accuracy in predicting year-two [18F]-FDG PET scans, with a mean absolute error
of 0.031 and a structural similarity index of 0.961. Furthermore, the model
successfully predicted PET scans up to seven years post-baseline. Notably, the
predicted [$^{18}$F]-FDG PET signal in an AD-susceptible meta-region was highly
accurate for individuals with mild cognitive impairment across years. In
contrast, a linear model was sufficient for predicting brain metabolism in
cognitively normal and dementia subjects. In conclusion, both the I2I network
and the linear model could offer valuable prognostic insights, guiding early
intervention strategies to preemptively address anticipated declines in brain
metabolism and potentially to monitor treatment effects.
