# Three Dimensional MR Image Synthesis with Progressive Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2101.05218 · HTML: https://mlsanity.com/paper/2101.05218/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [3D Brain and Heart Volume Generative Models: A Survey](https://mlsanity.com/paper/2210.05952/) (2022)
- [TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model](https://mlsanity.com/paper/2607.13812/) (2026)

## Abstract (arXiv)

Mainstream deep models for three-dimensional MRI synthesis are either cross-sectional or volumetric depending on the input. Cross-sectional models can decrease the model complexity, but they may lead to discontinuity artifacts. On the other hand, volumetric models can alleviate the discontinuity artifacts, but they might suffer from loss of spatial resolution due to increased model complexity coupled with scarce training data. To mitigate the limitations of both approaches, we propose a novel model that progressively recovers the target volume via simpler synthesis tasks across individual orientations.