# Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN

arXiv: https://arxiv.org/abs/1809.04536 · HTML: https://mlsanity.com/paper/1809.04536/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2018.

## Built on by (most cited first, 30 of 31)

- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [Diffusion Deformable Model for 4D Temporal Medical Image Generation](https://mlsanity.com/paper/2206.13295/) (2022)
- [Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model](https://mlsanity.com/paper/2305.19467/) (2023)
- [Self-Consistent Recursive Diffusion Bridge for Medical Image Translation](https://mlsanity.com/paper/2405.06789/) (2024)
- [I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling](https://mlsanity.com/paper/2405.14022/) (2024)
- [Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation](https://mlsanity.com/paper/2307.16143/) (2023)
- [HACA3: A Unified Approach for Multi-site MR Image Harmonization](https://mlsanity.com/paper/2212.06065/) (2022)
- [Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images](https://mlsanity.com/paper/2404.01464/) (2024)
- [METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy](https://mlsanity.com/paper/2104.10993/) (2021)
- [This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation](https://mlsanity.com/paper/2302.02150/) (2023)
- [Structural Attention: Rethinking Transformer for Unpaired Medical Image Synthesis](https://mlsanity.com/paper/2406.18967/) (2024)
- [Quantitative Metrics for Benchmarking Medical Image Harmonization](https://mlsanity.com/paper/2402.04426/) (2024)
- [Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks](https://mlsanity.com/paper/2106.08557/) (2021)
- [Deep learning based domain adaptation for mitochondria segmentation on EM volumes](https://mlsanity.com/paper/2202.10773/) (2022)
- [Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks](https://mlsanity.com/paper/2207.04325/) (2022)
- [A Novel Deep Learning Technique for Morphology Preserved Fetal ECG Extraction from Mother ECG using 1D-CycleGAN](https://mlsanity.com/paper/2310.03759/) (2023)
- [Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET](https://mlsanity.com/paper/2406.12632/) (2024)
- [From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review](https://mlsanity.com/paper/2502.19123/) (2025)
- [Multiscale Vector-Quantized Variational Autoencoder for Endoscopic Image Synthesis](https://mlsanity.com/paper/2511.19578/) (2025)
- [A Deep Generative Model of Neonatal Cortical Surface Development](https://mlsanity.com/paper/2206.07542/) (2022)
- [Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network](https://mlsanity.com/paper/2208.04825/) (2022)
- [Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images](https://mlsanity.com/paper/2410.09210/) (2024)
- [Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention](https://mlsanity.com/paper/2411.06583/) (2024)
- [Coarse-to-Fine Joint Registration of MR and Ultrasound Images via Imaging Style Transfer](https://mlsanity.com/paper/2508.05240/) (2025)
- [Biology-driven assessment of deep learning super-resolution imaging of the porosity network in dentin](https://mlsanity.com/paper/2510.08407/) (2025)
- [MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer's Disease Imaging](https://mlsanity.com/paper/2512.15947/) (2025)
- [Exploring constraints on CycleGAN-based CBCT enhancement for adaptive radiotherapy](https://mlsanity.com/paper/2110.04659/) (2021)
- [Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI](https://mlsanity.com/paper/2308.11047/) (2023)
- [Constrained CycleGAN for Effective Generation of Ultrasound Sector Images of Improved Spatial Resolution](https://mlsanity.com/paper/2309.00995/) (2023)

## Abstract (arXiv)

The cycleGAN is becoming an influential method in medical image synthesis. However, due to a lack of direct constraints between input and synthetic images, the cycleGAN cannot guarantee structural consistency between these two images, and such consistency is of extreme importance in medical imaging. To overcome this, we propose a structure-constrained cycleGAN for brain MR-to-CT synthesis using unpaired data that defines an extra structure-consistency loss based on the modality independent neighborhood descriptor to constrain structural consistency. Additionally, we use a position-based selection strategy for selecting training images instead of a completely random selection scheme. Experimental results on synthesizing CT images from brain MR images demonstrate that our method is better than the conventional cycleGAN and approximates the cycleGAN trained with paired data.