Synergistic PET/CT Reconstruction Using a Joint Generative Model

TL;DR

This paper introduces a joint generative model-based framework for PET/CT reconstruction that leverages shared inter-modal information to improve image quality and noise reduction.

Contribution

It presents a novel synergistic penalty function using a $eta$-VAE to promote likely PET/CT pairs, enhancing reconstruction quality over traditional methods.

Findings

Outperforms MLEM and WLS in PSNR

Utilizes shared information between PET and CT modalities

Reduces noise in reconstructed images

Abstract

We propose in this work a framework for synergistic positron emission tomography (PET)/computed tomography (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 $\beta$-variational autoencoder ($\beta$-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 inter-modal information can help reduce noise during reconstruction. Our result shows that our method was able to utilize the information between two modalities. The proposed method was able to outperform individually reconstructed images of PET (i.e., by maximum likelihood expectation maximization (MLEM)) and CT (i.e., by weighted least squares (WLS)) in terms of peak signal-to-noise ratio (PSNR). Future work will focus on optimizing the parameters of the $\beta$-VAE network and further exploration of other generative network models.