# Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI

arXiv: https://arxiv.org/abs/1804.04366 · HTML: https://mlsanity.com/paper/1804.04366/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 10 of 10)

- [One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation](https://mlsanity.com/paper/2204.13738/) (2022)
- [Unified Multi-Modal Image Synthesis for Missing Modality Imputation](https://mlsanity.com/paper/2304.05340/) (2023)
- [Cross-Modality Neuroimage Synthesis: A Survey](https://mlsanity.com/paper/2202.06997/) (2022)
- [Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesis](https://mlsanity.com/paper/2105.02835/) (2021)
- [Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models](https://mlsanity.com/paper/2409.10089/) (2024)
- [MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer's Disease Imaging](https://mlsanity.com/paper/2512.15947/) (2025)
- [Exploring the Power of Generative Deep Learning for Image-to-Image Translation and MRI Reconstruction: A Cross-Domain Review](https://mlsanity.com/paper/2303.09012/) (2023)
- [Learned Local Attention Maps for Synthesising Vessel Segmentations](https://mlsanity.com/paper/2308.12861/) (2023)
- [Stroke Locus Net: Occluded Vessel Localization from MRI Modalities](https://mlsanity.com/paper/2510.10155/) (2025)
- [Towards Personalized Multi-Modal MRI Synthesis across Heterogeneous Datasets](https://mlsanity.com/paper/2602.19723/) (2026)

## Abstract (arXiv)

Magnetic Resonance Angiography (MRA) has become an essential MR contrast for imaging and evaluation of vascular anatomy and related diseases. MRA acquisitions are typically ordered for vascular interventions, whereas in typical scenarios, MRA sequences can be absent in the patient scans. This motivates the need for a technique that generates inexistent MRA from existing MR multi-contrast, which could be a valuable tool in retrospective subject evaluations and imaging studies. In this paper, we present a generative adversarial network (GAN) based technique to generate MRA from T1-weighted and T2-weighted MRI images, for the first time to our knowledge. To better model the representation of vessels which the MRA inherently highlights, we design a loss term dedicated to a faithful reproduction of vascularities. To that end, we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term. Extending the well- established generator-discriminator architecture based on the recent PatchGAN model with the addition of steerable filter loss, the proposed steerable GAN (sGAN) method is evaluated on the large public database IXI. Experimental results show that the sGAN outperforms the baseline GAN method in terms of an overlap score with similar PSNR values, while it leads to improved visual perceptual quality.