ReviewBiomedical optics express2023
Photoacoustic imaging with limited sampling: a review of machine learning approaches.
Review in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed.
- Differential aperture modulation enables high-fidelity 3D photoacoustic imaging.Photoacoustics · 2026Article
- Mapping blood vessels and oxygenation throughout the whole mouse brain with dual-frequency fiber-array photoacoustic computed tomography.Light, science & applications · 2026Article
- Assessing image quality in photoacoustic imaging: A metric-based and deep learning-based evaluation.Photoacoustics · 2026Article
- Geometry-dependent reachability, homogenization, and redundancy in multi-view photoacoustic illumination.Biomedical optics express · 2026Article
- Efficient denoising in LED-based optoacoustic tomography with squeeze-and-excitation deep convolutional networks.Journal of biomedical optics · 2026Article
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- Sub-Diffraction Photoacoustic Microscopy Enabled by a Novel Phase-Shifted Excitation Strategy: A Numerical Study.Sensors (Basel, Switzerland) · 2026Article
- A PINN-driven game-theoretic framework in limited data photoacoustic tomography.Inverse problems · 2025Article
- Sparse scanning encoding and neural network decoding for compressed photoacoustic microscopy.Photoacoustics · 2025Article
- A Comparison of Image Reconstruction Methods for Ring-Array Photoacoustic Computed Tomography.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2025Article
- Photoacoustic-Integrated Multimodal Approach for Colorectal Cancer Diagnosis.ACS biomaterials science & engineering · 2025Review
- Limited-view photoacoustic imaging reconstruction via high-quality self-supervised neural representation.Photoacoustics · 2025Article
- Structure and oxygen saturation recovery of sparse photoacoustic microscopy images by deep learning.Photoacoustics · 2025Article
- Article
- Noise insensitive volumetric fusion method for enhanced photoacoustic microscopy.Journal of biomedical optics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Photoacoustic imaging combines high optical absorption contrast and deep acoustic penetration, and can reveal structural, molecular, and functional information about biological tissue non-invasively. Due to practical restrictions, photoacoustic imaging systems often face various challenges, such as complex system configuration, long imaging time, and/or less-than-ideal image quality, which collectively hinder their clinical application. Machine learning has been applied to improve photoacoustic imaging and mitigate the otherwise strict requirements in system setup and data acquisition. In contrast to the previous reviews of learned methods in photoacoustic computed tomography (PACT), this review focuses on the application of machine learning approaches to address the limited spatial sampling problems in photoacoustic imaging, specifically the limited view and undersampling issues. We summarize the relevant PACT works based on their training data, workflow, and model architecture. Notably, we also introduce the recent limited sampling works on the other major implementation of photoacoustic imaging, i.e., photoacoustic microscopy (PAM). With machine learning-based processing, photoacoustic imaging can achieve improved image quality with modest spatial sampling, presenting great potential for low-cost and user-friendly clinical applications.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.