  This paper presents a novel approach for learned synergistic reconstruction
of medical images using multi-branch generative models. Leveraging variational
autoencoders (VAEs), our model learns from pairs of images simultaneously,
enabling effective denoising and reconstruction. Synergistic image
reconstruction is achieved by incorporating the trained models in a regularizer
that evaluates the distance between the images and the model. We demonstrate
the efficacy of our approach on both Modified National Institute of Standards
and Technology (MNIST) and positron emission tomography (PET)/computed
tomography (CT) datasets, showcasing improved image quality for low-dose
imaging. Despite challenges such as patch decomposition and model limitations,
our results underscore the potential of generative models for enhancing medical
imaging reconstruction.
