Ultrasound Image Enhancement with the Variance of Diffusion Models
<|reference_start|>Ultrasound Image Enhancement with the Variance of Diffusion Models: 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.<|reference_end|>
@article{zhang2024ultrasound,
    title={Ultrasound Image Enhancement with the Variance of Diffusion Models},
    author={Yuxin Zhang, Cl'ement Huneau, J'er^ome Idier, Diana Mateus},
    journal={arXiv preprint arXiv:2409.11380},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11380},
    primaryClass={cs.CV}
}