Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models

• URL: http://arxiv.org/abs/2408.10987v1
• Date: Tue, 20 Aug 2024 16:31:31 GMT
• Title: Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models
• Authors: Hojat Asgariandehkordi, Sobhan Goudarzi, Mostafa Sharifzadeh, Adrian Basarab, Hassan Rivaz,
• Abstract summary: High frame-rate ultrasound imaging is a cutting-edge technique that enables high frame-rate imaging. One challenge associated with high frame-rate ultrasound imaging is the high noise associated with them, hindering their wider adoption. Our proposed solution aims to enhance plane wave image quality. Specifically, the method considers the distinction between low-angle and high-angle compounding plane waves as noise. In addition, our approach employs natural image segmentation masks as intensity maps for the generated images, resulting in accurate denoising for various anatomy shapes.
• Score: 3.3463490716514177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultrasound plane wave imaging is a cutting-edge technique that enables high frame-rate imaging. However, one challenge associated with high frame-rate ultrasound imaging is the high noise associated with them, hindering their wider adoption. Therefore, the development of a denoising method becomes imperative to augment the quality of plane wave images. Drawing inspiration from Denoising Diffusion Probabilistic Models (DDPMs), our proposed solution aims to enhance plane wave image quality. Specifically, the method considers the distinction between low-angle and high-angle compounding plane waves as noise and effectively eliminates it by adapting a DDPM to beamformed radiofrequency (RF) data. The method underwent training using only 400 simulated images. In addition, our approach employs natural image segmentation masks as intensity maps for the generated images, resulting in accurate denoising for various anatomy shapes. The proposed method was assessed across simulation, phantom, and in vivo images. The results of the evaluations indicate that our approach not only enhances image quality on simulated data but also demonstrates effectiveness on phantom and in vivo data in terms of image quality. Comparative analysis with other methods underscores the superiority of our proposed method across various evaluation metrics. The source code and trained model will be released along with the dataset at: http://code.sonography.ai

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