# Comparative Analysis of GAN and Diffusion for MRI-to-CT translation

arXiv: https://arxiv.org/abs/2509.22049 · HTML: https://mlsanity.com/paper/2509.22049/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Are GANs Created Equal? A Large-Scale Study](https://mlsanity.com/paper/1711.10337/) (2017)
- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [Medical Image Synthesis with Context-Aware Generative Adversarial Networks](https://mlsanity.com/paper/1612.05362/) (2016)
- [Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models](https://mlsanity.com/paper/2209.12104/) (2022)
- [DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding](https://mlsanity.com/paper/2303.15770/) (2023)
- [MRI to CT Translation with GANs](https://mlsanity.com/paper/1901.05259/) (2019)

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

- [A Systematic Benchmark of GAN Architectures for MRI-to-CT Synthesis](https://mlsanity.com/paper/2603.13520/) (2026)
- [Do We Really Need Diffusion? A Fast U-Net for Paired Medical Image Translation](https://mlsanity.com/paper/2606.17675/) (2026)

## Abstract (arXiv)

Computed tomography (CT) is essential for treatment and diagnostics; In case CT are missing or otherwise difficult to obtain, methods for generating synthetic CT (sCT) images from magnetic resonance imaging (MRI) images are sought after. Therefore, it is valuable to establish a reference for what strategies are most effective for MRI-to-CT translation. In this paper, we compare the performance of two frequently used architectures for MRI-to-CT translation: a conditional generative adversarial network (cGAN) and a conditional denoising diffusion probabilistic model (cDDPM). We chose well-established implementations to represent each architecture: Pix2Pix for cGAN, and Palette for cDDPM. We separate the classical 3D translation problem into a sequence of 2D translations on the transverse plane, to investigate the viability of a strategy that reduces the computational cost. We also investigate the impact of conditioning the generative process on a single MRI image/slice and on multiple MRI slices. The performance is assessed using a thorough evaluation protocol, including a novel slice-wise metric Similarity Of Slices (SIMOS), which measures the continuity between transverse slices when compiling the sCTs into 3D format. Our comparative analysis revealed that MRI-to-CT generative models benefit from multi-channel conditional input and using cDDPM as an architecture.