ArticleIEEE transactions on medical imaging2022
Deep Learning-Powered Bessel-Beam Multiparametric Photoacoustic Microscopy.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
- Optoacoustic brain imaging: A systematic review and field mapping.Photoacoustics · 2026Article
- Electrically switchable dual-focus metalens for depth-extended in vivo photoacoustic imaging of ophthalmic vascular disease.Light, science & applications · 2026Article
- A comprehensive review of high-performance photoacoustic microscopy systems.Photoacoustics · 2025Review
- All-fiber three-wavelength laser for functional photoacoustic microscopy.Photoacoustics · 2025Article
- Label-free optical microscopy with artificial intelligence: a new paradigm in pathology.Biophotonics discovery · 2025Review
- Axially multifocal metalens for 3D volumetric photoacoustic imaging of neuromelanin in live brain organoid.Science advances · 2025Article
- Quantitative classification of melasma with photoacoustic microscopy: a pilot study.Journal of biomedical optics · 2024Article
- Photoacoustic imaging plus X: a review.Journal of biomedical optics · 2024Review
- Sounding out the dynamics: a concise review of high-speed photoacoustic microscopy.Journal of biomedical optics · 2024Review
- Enhanced resolution and sensitivity acoustic-resolution photoacoustic microscopy with semi/unsupervised GANs.Scientific reports · 2023Article
- Homogeneous-resolution photoacoustic microscopy for ultrawide field-of-view neurovascular imaging in Alzheimer's disease.Photoacoustics · 2023Article
- Optical-resolution photoacoustic microscopy with a needle-shaped beam.Nature photonics · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Enabling simultaneous and high-resolution quantification of the total concentration of hemoglobin ( [Formula: see text]), oxygen saturation of hemoglobin (sO2), and cerebral blood flow (CBF), multi-parametric photoacoustic microscopy (PAM) has emerged as a promising tool for functional and metabolic imaging of the live mouse brain. However, due to the limited depth of focus imposed by the Gaussian-beam excitation, the quantitative measurements become inaccurate when the imaging object is out of focus. To address this problem, we have developed a hardware-software combined approach by integrating Bessel-beam excitation and conditional generative adversarial network (cGAN)-based deep learning. Side-by-side comparison of the new cGAN-powered Bessel-beam multi-parametric PAM against the conventional Gaussian-beam multi-parametric PAM shows that the new system enables high-resolution, quantitative imaging of [Formula: see text], sO2, and CBF over a depth range of [Formula: see text] in the live mouse brain, with errors 13-58 times lower than those of the conventional system. Better fulfilling the rigid requirement of light focusing for accurate hemodynamic measurements, the deep learning-powered Bessel-beam multi-parametric PAM may find applications in large-field functional recording across the uneven brain surface and beyond (e.g., tumor imaging).
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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.