# Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks

arXiv: https://arxiv.org/abs/2001.10830 · HTML: https://mlsanity.com/paper/2001.10830/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [ADIR: Adaptive Diffusion for Image Reconstruction](https://mlsanity.com/paper/2212.03221/) (2022)
- [Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks](https://mlsanity.com/paper/2106.14324/) (2021)
- [Assessing the ability of generative adversarial networks to learn canonical medical image statistics](https://mlsanity.com/paper/2204.12007/) (2022)
- [Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation](https://mlsanity.com/paper/2211.15717/) (2022)
- [High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models](https://mlsanity.com/paper/2306.08630/) (2023)
- [Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems](https://mlsanity.com/paper/2202.05311/) (2022)
- [Diverse Image Generation with Diffusion Models and Cross Class Label Learning for Polyp Classification](https://mlsanity.com/paper/2502.05444/) (2025)
- [Advancing the AmbientGAN for learning stochastic object models](https://mlsanity.com/paper/2102.00281/) (2021)
- [Generative models and Bayesian inversion using Laplace approximation](https://mlsanity.com/paper/2203.07755/) (2022)
- [Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey](https://mlsanity.com/paper/2509.15363/) (2025)
- [Physics-Guided Conditional Diffusion Networks for Microwave Image Reconstruction](https://mlsanity.com/paper/2510.25729/) (2025)

## Abstract (arXiv)

Medical image reconstruction is typically an ill-posed inverse problem. In order to address such ill-posed problems, the prior distribution of the sought after object property is usually incorporated by means of some sparsity-promoting regularization. Recently, prior distributions for images estimated using generative adversarial networks (GANs) have shown great promise in regularizing some of these image reconstruction problems. In this work, we apply an image-adaptive GAN-based reconstruction method (IAGAN) to reconstruct high fidelity images from incomplete medical imaging data. It is observed that the IAGAN method can potentially recover fine structures in the object that are relevant for medical diagnosis but may be oversmoothed in reconstructions with traditional sparsity-promoting regularization.