# Ultrasound Image Enhancement with the Variance of Diffusion Models

arXiv: https://arxiv.org/abs/2409.11380 · HTML: https://mlsanity.com/paper/2409.11380/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Placed in

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

## Builds on (most cited first, 18 of 18)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2201.11793/) (2022)
- [Solving Inverse Problems in Medical Imaging with Score-Based Generative Models](https://mlsanity.com/paper/2111.08005/) (2021)
- [Score-based diffusion models for accelerated MRI](https://mlsanity.com/paper/2110.05243/) (2021)
- [SNIPS: Solving Noisy Inverse Problems Stochastically](https://mlsanity.com/paper/2105.14951/) (2021)
- [Medical image denoising using convolutional denoising autoencoders](https://mlsanity.com/paper/1608.04667/) (2016)
- [Deep learning in ultrasound imaging](https://mlsanity.com/paper/1907.02994/) (2019)
- [Adaptive Ultrasound Beamforming using Deep Learning](https://mlsanity.com/paper/1909.10342/) (2019)
- [Dehazing Ultrasound using Diffusion Models](https://mlsanity.com/paper/2307.11204/) (2023)
- [So you think you can DAS? A viewpoint on delay-and-sum beamforming](https://mlsanity.com/paper/2007.11960/) (2020)
- [Deep Ultrasound Denoising Using Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.07440/) (2023)
- [Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2307.15990/) (2023)
- [CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging](https://mlsanity.com/paper/2008.12750/) (2020)
- [Inverse Problem of Ultrasound Beamforming with Denoising-Based Regularized Solutions](https://mlsanity.com/paper/2206.07926/) (2022)
- [Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models](https://mlsanity.com/paper/2408.10987/) (2024)
- [Fast Sampling generative model for Ultrasound image reconstruction](https://mlsanity.com/paper/2312.09510/) (2023)
- [Ultrasound Imaging based on the Variance of a Diffusion Restoration Model](https://mlsanity.com/paper/2403.15316/) (2024)

## Built on by (most cited first, 1 of 1)

- [Ultrasound Image Generation using Latent Diffusion Models](https://mlsanity.com/paper/2502.08580/) (2025)

## Abstract (arXiv)

Ultrasound imaging, despite its widespread use in medicine, often suffers from various sources of noise and artifacts that impact the signal-to-noise ratio and overall image quality. Enhancing ultrasound images requires a delicate balance between contrast, resolution, and speckle preservation. This paper introduces a novel approach that integrates adaptive beamforming with denoising diffusion-based variance imaging to address this challenge. By applying Eigenspace-Based Minimum Variance (EBMV) beamforming and employing a denoising diffusion model fine-tuned on ultrasound data, our method computes the variance across multiple diffusion-denoised samples to produce high-quality despeckled images. This approach leverages both the inherent multiplicative noise of ultrasound and the stochastic nature of diffusion models. Experimental results on a publicly available dataset demonstrate the effectiveness of our method in achieving superior image reconstructions from single plane-wave acquisitions. The code is available at: https://github.com/Yuxin-Zhang-Jasmine/IUS2024_Diffusion.