# Multi-Modality Generative Adversarial Networks with Tumor Consistency Loss for Brain MR Image Synthesis

arXiv: https://arxiv.org/abs/2005.00925 · HTML: https://mlsanity.com/paper/2005.00925/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 12 of 12)

- [A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation](https://mlsanity.com/paper/2101.05434/) (2021)
- [TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation](https://mlsanity.com/paper/2105.08993/) (2021)
- [METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy](https://mlsanity.com/paper/2104.10993/) (2021)
- [Cross-Modality Neuroimage Synthesis: A Survey](https://mlsanity.com/paper/2202.06997/) (2022)
- [Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion](https://mlsanity.com/paper/2203.04586/) (2022)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [What can we learn about a generated image corrupting its latent representation?](https://mlsanity.com/paper/2210.06257/) (2022)
- [Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser](https://mlsanity.com/paper/2402.00375/) (2024)
- [Unsupervised Tumor-Aware Distillation for Multi-Modal Brain Image Translation](https://mlsanity.com/paper/2403.20168/) (2024)
- [Principled Feature Disentanglement for High-Fidelity Unified Brain MRI Synthesis](https://mlsanity.com/paper/2406.14954/) (2024)
- [Brain MR Image Synthesis with 3D Multi-Contrast Self-Attention GAN](https://mlsanity.com/paper/2604.00070/) (2026)
- [Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesis](https://mlsanity.com/paper/2606.25390/) (2026)

## Abstract (arXiv)

Magnetic Resonance (MR) images of different modalities can provide complementary information for clinical diagnosis, but whole modalities are often costly to access. Most existing methods only focus on synthesizing missing images between two modalities, which limits their robustness and efficiency when multiple modalities are missing. To address this problem, we propose a multi-modality generative adversarial network (MGAN) to synthesize three high-quality MR modalities (FLAIR, T1 and T1ce) from one MR modality T2 simultaneously. The experimental results show that the quality of the synthesized images by our proposed methods is better than the one synthesized by the baseline model, pix2pix. Besides, for MR brain image synthesis, it is important to preserve the critical tumor information in the generated modalities, so we further introduce a multi-modality tumor consistency loss to MGAN, called TC-MGAN. We use the synthesized modalities by TC-MGAN to boost the tumor segmentation accuracy, and the results demonstrate its effectiveness.