Quantitative Evaluation of Synthesized Brain PET Using a Variational Autoencoder

Alzheimer’s disease (AD) accounts for 50-70% of dementia cases, making it the most common type of dementia. Positron Emission Tomography (PET) has demonstrated the ability to diagnose dementia to an equivalent ability as a cognitive exam. Advances in computational power and deep learning have revolutionized quantitative analysis of medical images. A variational autoencoder (VAE) has previously been proposed for the identification of PET brain abnormalities by examining divergence of the reconstruction error from a normal dataset. Little work has examined the utility of training a multiclass VAE and its ability to faithfully reconstruct quantitatively acceptable images across the AD class range; in effect exploring the inclusiveness of the learned latent state representation across different disease states. We construct a VAE that is trained on a dataset containing scans of 94 patients with varying degrees / classes of neurode-generation. A ’leave one group out’ approach to model training allows the classes with the most pertinent features to be probed across the dataset. Metrics used to assess VAE performance were the peak signal-to-noise ratio (PSNR) and structured similarity index (SSIM) if the VAE reconstructed imags versus the ground truth. A decreased average PSNR, 0.82%, and SSIM, 3.97%, demonstrates poorer performance when the cognitively normal (CN) data is removed from the training dataset, suggesting the CN group feature representations aid in the faithful reconstruction of subsequent disease groups. Furthermore, it was observed that inclusion of the late mild cognitively impairment (LMCI) group reduces reconstructed quantitative accuracy across all classes. Removal of this class during training increases PSNR and SSIM across all groups, with increases of 0.19% and 0.42% respectively. This suggests that the LMCI group does not follow the posterior Gaussian distribution.