Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising

• URL: http://arxiv.org/abs/2506.21499v1
• Date: Thu, 26 Jun 2025 17:28:32 GMT
• Title: Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising
• Authors: Hojat Asgariandehkordi, Mostafa Sharifzadeh, Hassan Rivaz,
• Abstract summary: Ultrasound Coherent Plane Wave Compounding (CPWC) enhances image contrast by combining echoes from multiple steered transmissions.<n>We propose a zero-shot denoising framework tailored for low-angle CPWC acquisitions.
• Score: 1.912429179274357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultrasound Coherent Plane Wave Compounding (CPWC) enhances image contrast by combining echoes from multiple steered transmissions. While increasing the number of angles generally improves image quality, it drastically reduces the frame rate and can introduce blurring artifacts in fast-moving targets. Moreover, compounded images remain susceptible to noise, particularly when acquired with a limited number of transmissions. We propose a zero-shot denoising framework tailored for low-angle CPWC acquisitions, which enhances contrast without relying on a separate training dataset. The method divides the available transmission angles into two disjoint subsets, each used to form compound images that include higher noise levels. The new compounded images are then used to train a deep model via a self-supervised residual learning scheme, enabling it to suppress incoherent noise while preserving anatomical structures. Because angle-dependent artifacts vary between the subsets while the underlying tissue response is similar, this physics-informed pairing allows the network to learn to disentangle the inconsistent artifacts from the consistent tissue signal. Unlike supervised methods, our model requires no domain-specific fine-tuning or paired data, making it adaptable across anatomical regions and acquisition setups. The entire pipeline supports efficient training with low computational cost due to the use of a lightweight architecture, which comprises only two convolutional layers. Evaluations on simulation, phantom, and in vivo data demonstrate superior contrast enhancement and structure preservation compared to both classical and deep learning-based denoising methods.

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