ArticlePhotoacoustics2025
MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data.
Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Model-Based Joint Feature Fusion framework for limited-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- Recent advances in photoacoustic-ultrasound dual-modality fusion imaging for functional visualization of the superficial tumor microenvironment.Oncology reviews · 2026Review
- Phenothiazine-Based Fluorescence-Quenched Photoacoustic Contrast Agents for Molecular Imaging.Chemical & biomedical imaging · 2025Article
- Wavelet-enhanced residual optimal transport for Mamba-based image restoration in photoacoustic tomography.Photoacoustics · 2025Article
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3 authors.
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Abstract
Photoacoustic imaging (PAI) is an emerging hybrid imaging technology that combines the advantages of optical and ultrasound imaging. Despite its excellent imaging capabilities, PAI still faces numerous challenges in clinical applications, particularly sparse spatial sampling and limited view detection. These limitations often result in severe streak artifacts and blurring when using standard methods to reconstruct images from incomplete data. In this work, we propose an improved convolutional neural network (CNN) architecture, called multi-scale dense UNet (MSD-Net), to correct artifacts in 2D photoacoustic tomography (PAT). MSD-Net exploits the advantages of multi-scale information fusion and dense connections to improve the performance of CNN. Experimental validation with both simulated and
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