Unsupervised denoising of photoacoustic images based on the Noise2Noise network.

Biomed Opt Express

Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, New York, USA.

Published: August 2024

In this study, we implemented an unsupervised deep learning method, the Noise2Noise network, for the improvement of linear-array-based photoacoustic (PA) imaging. Unlike supervised learning, which requires a noise-free ground truth, the Noise2Noise network can learn noise patterns from a pair of noisy images. This is particularly important for in vivo PA imaging, where the ground truth is not available. In this study, we developed a method to generate noise pairs from a single set of PA images and verified our approach through simulation and experimental studies. Our results reveal that the method can effectively remove noise, improve signal-to-noise ratio, and enhance vascular structures at deeper depths. The denoised images show clear and detailed vascular structure at different depths, providing valuable insights for preclinical research and potential clinical applications.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11427216PMC
http://dx.doi.org/10.1364/BOE.529253DOI Listing

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