Joint PET/CT Reconstruction Using a Double Variational Autoencoder

We propose in this work a framework for synergistic PET/CT reconstruction using a joint generative model as a penalty. We use a synergistic penalty function that promotes PET/CT pairs that are likely to occur together. The synergistic penalty function is based on a generative model, namely β-VAE. The model generates a PET/CT image pair from the same latent variable which contains the information that is shared between the two modalities. This sharing of intermodal information between images can help reduce noise during reconstruction. Our result shows that our method was able to utilize the information between two modalities to reconstruct better images compared to individually reconstructed images in terms of PSNR.