An unsupervised physics-informed neural deconvolution framework for ultrasound imaging

Vollständiger Abstract

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Abstract Objective . Point spread function (PSF) and clutter noise are primary causes of ultrasound quality degradation. Physical deconvolution can reduce PSF effects but is sensitive to high-frequency noise, whereas deep learning often requires large labeled datasets and may introduce non-physical artifacts. Therefore, this study aims to develop an unsupervised and physically interpretable framework for robust and effective ultrasound image reconstruction. Approach . We propose an unsupervised physics-informed deconvolution network framework (UPIDN) that bridges the strengths of physical modeling and deep learning. Specifically, the degradation model is embedded into the network, and physical deconvolution provides stable low-frequency inversion guidance. Meanwhile, the structural prior capability of the deep image prior is exploited to compensate for the ill-posedness of high-frequency detail recovery. Additionally, we perform a cepstral domain transformation on radio-frequency data to decouple the embedded PSF information, providing the network with a physically consistent and data-adaptive initialization strategy. Meanwhile, considering the ultrasound noise characteristics, a dual-wavelet noise preprocessing scheme is designed to guide the prediction to focus more on the correct generation direction. Main results . Experiments demonstrate that UPIDN outperforms other state-of-the-art methods, achieving superior contrast and resolution while clearly measuring vessel–muscle interface that is difficult to discern in input delay-and-sum results. Significance . UPIDN provides a reliable approach for high-quality ultrasound reconstruction without paired training data and offers a potential solution for ultrasound imaging scenarios where data availability and physical fidelity are critical.