# Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction

arXiv: https://arxiv.org/abs/2404.08748 · HTML: https://mlsanity.com/paper/2404.08748/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 55 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Diffusion Posterior Sampling for General Noisy Inverse Problems](https://mlsanity.com/paper/2209.14687/) (2022)
- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Regularising Inverse Problems with Generative Machine Learning Models](https://mlsanity.com/paper/2107.11191/) (2021)
- [Prior Image-Constrained Reconstruction using Style-Based Generative Models](https://mlsanity.com/paper/2102.12525/) (2021)
- [PatchVAE: Learning Local Latent Codes for Recognition](https://mlsanity.com/paper/2004.03623/) (2020)
- [Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary](https://mlsanity.com/paper/1801.01452/) (2017)
- [Systematic Review on Learning-based Spectral CT](https://mlsanity.com/paper/2304.07588/) (2023)
- [Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction](https://mlsanity.com/paper/2203.05968/) (2022)

## Abstract (arXiv)

This paper presents a novel approach for learned synergistic reconstruction of medical images using multibranch 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.