Ultrasound Image Enhancement with the Variance of Diffusion Models

TL;DR

This paper presents a novel ultrasound image enhancement method combining adaptive beamforming with diffusion-based variance imaging, resulting in improved despeckling and image quality from single acquisitions.

Contribution

It introduces a new approach integrating EBMV beamforming with diffusion model variance analysis for ultrasound despeckling, a novel combination in this context.

Findings

Superior image reconstruction quality demonstrated on public dataset

Effective despeckling and noise reduction in ultrasound images

Outperforms existing methods in preserving image details

Abstract

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.