# Deep CT to MR Synthesis using Paired and Unpaired Data

arXiv: https://arxiv.org/abs/1805.10790 · HTML: https://mlsanity.com/paper/1805.10790/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 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)
- [Deep learning-based synthetic-CT generation in radiotherapy and PET: a review](https://mlsanity.com/paper/2102.02734/) (2021)
- [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)
- [Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation](https://mlsanity.com/paper/2103.03781/) (2021)
- [One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis](https://mlsanity.com/paper/2207.06509/) (2022)
- [MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2108.02160/) (2021)
- [Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model](https://mlsanity.com/paper/2407.05059/) (2024)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [Bridging the gap between paired and unpaired medical image translation](https://mlsanity.com/paper/2110.08407/) (2021)
- [Deformation equivariant cross-modality image synthesis with paired non-aligned training data](https://mlsanity.com/paper/2208.12491/) (2022)
- [Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP](https://mlsanity.com/paper/2401.01911/) (2024)
- [Frequency-Supervised MR-to-CT Image Synthesis](https://mlsanity.com/paper/2107.08962/) (2021)
- [Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges](https://mlsanity.com/paper/2211.14732/) (2022)
- [ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network](https://mlsanity.com/paper/2103.05116/) (2021)
- [CNNs and GANs in MRI-based cross-modality medical image estimation](https://mlsanity.com/paper/2106.02198/) (2021)
- [Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scans](https://mlsanity.com/paper/2106.04281/) (2021)
- [Deep learning based domain adaptation for mitochondria segmentation on EM volumes](https://mlsanity.com/paper/2202.10773/) (2022)
- [Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networks](https://mlsanity.com/paper/2211.12082/) (2022)
- [Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation](https://mlsanity.com/paper/2202.06142/) (2022)
- [sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs](https://mlsanity.com/paper/2208.04874/) (2022)
- [IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data](https://mlsanity.com/paper/2302.01788/) (2023)
- [MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks](https://mlsanity.com/paper/2401.09624/) (2024)
- [Lightweight CycleGAN Models for Cross-Modality Image Transformation and Experimental Quality Assessment in Fluorescence Microscopy](https://mlsanity.com/paper/2510.15579/) (2025)
- [Mitigating analytical variability in fMRI results with style transfer](https://mlsanity.com/paper/2404.03703/) (2024)
- [Leveraging Multimodal CycleGAN for the Generation of Anatomically Accurate Synthetic CT Scans from MRIs](https://mlsanity.com/paper/2407.10888/) (2024)
- [Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization](https://mlsanity.com/paper/2410.02628/) (2024)
- [Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining](https://mlsanity.com/paper/2411.11613/) (2024)
- [Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest](https://mlsanity.com/paper/2501.15572/) (2025)

## Abstract (arXiv)

MR imaging will play a very important role in radiotherapy treatment planning for segmentation of tumor volumes and organs. However, the use of MR-based radiotherapy is limited because of the high cost and the increased use of metal implants such as cardiac pacemakers and artificial joints in aging society. To improve the accuracy of CT-based radiotherapy planning, we propose a synthetic approach that translates a CT image into an MR image using paired and unpaired training data. In contrast to the current synthetic methods for medical images, which depend on sparse pairwise-aligned data or plentiful unpaired data, the proposed approach alleviates the rigid registration challenge of paired training and overcomes the context-misalignment problem of the unpaired training. A generative adversarial network was trained to transform 2D brain CT image slices into 2D brain MR image slices, combining adversarial loss, dual cycle-consistent loss, and voxel-wise loss. The experiments were analyzed using CT and MR images of 202 patients. Qualitative and quantitative comparisons against independent paired training and unpaired training methods demonstrate the superiority of our approach.