# Ultrasound Image Generation using Latent Diffusion Models

arXiv: https://arxiv.org/abs/2502.08580 · HTML: https://mlsanity.com/paper/2502.08580/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Placed in

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

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation](https://mlsanity.com/paper/2305.05424/) (2023)
- [Augmenting medical image classifiers with synthetic data from latent diffusion models](https://mlsanity.com/paper/2308.12453/) (2023)
- [Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research](https://mlsanity.com/paper/2311.09402/) (2023)
- [EchoNet-Synthetic: Privacy-preserving Video Generation for Safe Medical Data Sharing](https://mlsanity.com/paper/2406.00808/) (2024)
- [Dehazing Ultrasound using Diffusion Models](https://mlsanity.com/paper/2307.11204/) (2023)
- [Deep Ultrasound Denoising Using Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.07440/) (2023)
- [Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image Generation](https://mlsanity.com/paper/2407.05428/) (2024)
- [Vision Mamba for Classification of Breast Ultrasound Images](https://mlsanity.com/paper/2407.03552/) (2024)
- [Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models](https://mlsanity.com/paper/2408.10987/) (2024)
- [Ultrasound segmentation using U-Net: learning from simulated data and testing on real data](https://mlsanity.com/paper/1904.11031/) (2019)
- [UltraRay: Introducing Full-Path Ray Tracing in Physics-Based Ultrasound Simulation](https://mlsanity.com/paper/2501.05828/) (2025)
- [Active inference and deep generative modeling for cognitive ultrasound](https://mlsanity.com/paper/2410.13310/) (2024)
- [Cinepro: Robust Training of Foundation Models for Cancer Detection in Prostate Ultrasound Cineloops](https://mlsanity.com/paper/2501.12331/) (2025)
- [Ultrasound Image Synthesis Using Generative AI for Lung Ultrasound Detection](https://mlsanity.com/paper/2501.06356/) (2025)
- [Ultrasound Image Enhancement with the Variance of Diffusion Models](https://mlsanity.com/paper/2409.11380/) (2024)

## Built on by (most cited first, 7 of 7)

- [High Volume Rate 3D Ultrasound Reconstruction with Diffusion Models](https://mlsanity.com/paper/2505.22090/) (2025)
- [Breast Ultrasound Tumor Generation via Mask Generator and Text-Guided Network:A Clinically Controllable Framework with Downstream Evaluation](https://mlsanity.com/paper/2507.07721/) (2025)
- [AWDiff: An a trous wavelet diffusion model for lung ultrasound image synthesis](https://mlsanity.com/paper/2603.03125/) (2026)
- [Hybrid Diffusion Model for Breast Ultrasound Image Augmentation](https://mlsanity.com/paper/2603.26834/) (2026)
- [A Foundational EDM2-Based Generative Model for High-Resolution Synthetic Fetal Ultrasound Imaging from Open Datasets](https://mlsanity.com/paper/2608.05471/) (2026)
- [Physics-Guided Synthetic High-Frequency Ultrasound Generation for Skin Layer Segmentation](https://mlsanity.com/paper/2609.12735/) (2026)
- [Abdominal Ultrasound Simulation from Semantic Labels using Paired Label-to-Physics-Based Image Translation](https://mlsanity.com/paper/2610.08849/) (2026)

## Abstract (arXiv)

Diffusion models for image generation have been a subject of increasing interest due to their ability to generate diverse, high-quality images. Image generation has immense potential in medical imaging because open-source medical images are difficult to obtain compared to natural images, especially for rare conditions. The generated images can be used later to train classification and segmentation models. In this paper, we propose simulating realistic ultrasound (US) images by successive fine-tuning of large diffusion models on different publicly available databases. To do so, we fine-tuned Stable Diffusion, a state-of-the-art latent diffusion model, on BUSI (Breast US Images) an ultrasound breast image dataset. We successfully generated high-quality US images of the breast using simple prompts that specify the organ and pathology, which appeared realistic to three experienced US scientists and a US radiologist. Additionally, we provided user control by conditioning the model with segmentations through ControlNet. We will release the source code at http://code.sonography.ai/ to allow fast US image generation to the scientific community.