ArticlePhotoacoustics2025
Structure and oxygen saturation recovery of sparse photoacoustic microscopy images by deep learning.
Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Acquisition-inspired motion artifact modeling and degradation-consistent diffusion restoration in photoacoustic microscopy.Photoacoustics · 2026Article
- Deep learning-driven quantitative spectroscopic photoacoustic imaging for segmentation and oxygen saturation estimation.Ultrasonics · 2026Article
- A Label-Free Ultraviolet Photoacoustic Microscopy Enables Nuclear Imaging and Toxicity Assessment in an Intact Brain Organoid.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Hybrid Fourier-Derivative Analysis: An accurate and fast method for blood flow quantification in photoacoustic microscopy.Photoacoustics · 2025Article
- Spectral-distortion-suppressed deep learning for fiber sensor photoacoustic microscopy.Photoacoustics · 2025Article
- A comprehensive review of high-performance photoacoustic microscopy systems.Photoacoustics · 2025Review
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8 authors.
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Abstract
Photoacoustic microscopy (PAM) leverages the photoacoustic effect to provide high-resolution structural and functional imaging. However, achieving high-speed imaging with high spatial resolution remains challenging. To address this, undersampling and deep learning have emerged as common techniques to enhance imaging speed. Yet, existing methods rarely achieve effective recovery of functional images. In this study, we propose Mask-enhanced U-net (MeU-net) for recovering sparsely sampled PAM structural and functional images. The model utilizes dual-channel input, processing photoacoustic data from 532 nm and 558 nm wavelengths. Additionally, we introduce an adaptive vascular attention mask module that focuses on vascular information recovery and design a vessel-specific loss function to enhance restoration accuracy. We simulate data from mouse brain and ear imaging under various levels of sparsity (4 ×, 8 ×, 12 ×) and conduct extensive experiments. The results demonstrate that MeU-net significantly outperforms traditional interpolation methods and other representative models in structural information and oxygen saturation recovery.
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