# A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images

arXiv: https://arxiv.org/abs/2403.04612 · HTML: https://mlsanity.com/paper/2403.04612/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 24 of its 37 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 24 of 24)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [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)
- [Improved Techniques for Training Score-Based Generative Models](https://mlsanity.com/paper/2006.09011/) (2020)
- [Maximum Likelihood Training of Score-Based Diffusion Models](https://mlsanity.com/paper/2101.09258/) (2021)
- [Tackling the Generative Learning Trilemma with Denoising Diffusion GANs](https://mlsanity.com/paper/2112.07804/) (2021)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [Image to Image Translation for Domain Adaptation](https://mlsanity.com/paper/1712.00479/) (2017)
- [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018)
- [An Introduction to Deep Generative Modeling](https://mlsanity.com/paper/2103.05180/) (2021)
- [GANs for Medical Image Synthesis: An Empirical Study](https://mlsanity.com/paper/2105.05318/) (2021)
- [Diffusion Deformable Model for 4D Temporal Medical Image Generation](https://mlsanity.com/paper/2206.13295/) (2022)
- [DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis](https://mlsanity.com/paper/1904.12894/) (2019)
- [Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation](https://mlsanity.com/paper/2012.03769/) (2020)
- [Memory-efficient GAN-based Domain Translation of High Resolution 3D Medical Images](https://mlsanity.com/paper/2010.03396/) (2020)
- [Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Model](https://mlsanity.com/paper/2007.07230/) (2020)
- [STAN-CT: Standardizing CT Image using Generative Adversarial Network](https://mlsanity.com/paper/2004.01307/) (2020)
- [4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model](https://mlsanity.com/paper/2002.07089/) (2020)

## Built on by (most cited first, 4 of 4)

- [Generative augmentations for improved cardiac ultrasound segmentation using diffusion models](https://mlsanity.com/paper/2502.20100/) (2025)
- [Cardiac valve event timing in echocardiography using deep learning and triplane recordings](https://mlsanity.com/paper/2403.10156/) (2024)
- [Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer's Disease Biomarkers](https://mlsanity.com/paper/2405.05462/) (2024)
- [A Generative Imputation Method for Multimodal Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2508.09271/) (2025)

## Abstract (arXiv)

Currently, medical image domain translation operations show a high demand from researchers and clinicians. Amongst other capabilities, this task allows the generation of new medical images with sufficiently high image quality, making them clinically relevant. Deep Learning (DL) architectures, most specifically deep generative models, are widely used to generate and translate images from one domain to another. The proposed framework relies on an adversarial Denoising Diffusion Model (DDM) to synthesize echocardiography images and perform domain translation. Contrary to Generative Adversarial Networks (GANs), DDMs are able to generate high quality image samples with a large diversity. If a DDM is combined with a GAN, this ability to generate new data is completed at an even faster sampling time. In this work we trained an adversarial DDM combined with a GAN to learn the reverse denoising process, relying on a guide image, making sure relevant anatomical structures of each echocardiography image were kept and represented on the generated image samples. For several domain translation operations, the results verified that such generative model was able to synthesize high quality image samples: MSE: 11.50 +/- 3.69, PSNR (dB): 30.48 +/- 0.09, SSIM: 0.47 +/- 0.03. The proposed method showed high generalization ability, introducing a framework to create echocardiography images suitable to be used for clinical research purposes.