ArticleJournal of biomedical optics2020
Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions.
Article in Journal of biomedical optics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 62 citations in OpenAlex.
- Benchmarking deep learning-based reconstruction methods for photoacoustic computed tomography with clinically relevant synthetic datasets.Journal of biomedical optics · 2027Article
- Deep learning-driven quantitative spectroscopic photoacoustic imaging for segmentation and oxygen saturation estimation.Ultrasonics · 2026Article
- Application of a virtual imaging framework for investigating a deep learning-based reconstruction method for 3D quantitative photoacoustic computed tomography.Photoacoustics · 2026Article
- Towards bridging the synthetic-to-real gap in quantitative photoacoustic tomography via unsupervised domain adaptation.Photoacoustics · 2025Article
- Depth-dependent fluence compensation withoutJournal of biomedical optics · 2025Article
- Automatic photoacoustic monitoring of perinatal brain hypoxia with superior sagittal sinus detection.Journal of biomedical optics · 2025Article
- Bioactive hydrogel synergizes neuroprotection, macrophage polarization, and angiogenesis to improve repair of traumatic brain injury.Materials today. Bio · 2024Article
- Impact of skin tone on target size detectability in photoacoustic breast imaging.Biophotonics discovery · 2024Article
- Current trends in the characterization and monitoring of vascular response to cancer therapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Extractor-attention-predictor network for quantitative photoacoustic tomography.Photoacoustics · 2024Article
- Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry.Journal of biomedical optics · 2024Article
- Compensating unknown speed of sound in learned fast 3D limited-view photoacoustic tomography.Photoacoustics · 2024Article
- Oxygenation heterogeneity facilitates spatiotemporal flow pattern visualization inside human blood vessels using photoacoustic computed tomography.Biomedical optics express · 2024Article
- Photoacoustic imaging of squirrel monkey cortical responses induced by peripheral mechanical stimulation.Journal of biophotonics · 2024Article
- Quantitative photoacoustic tomography: modeling and inverse problems.Journal of biomedical optics · 2024Review
- Dual-modal Photoacoustic and Ultrasound Imaging: from preclinical to clinical applications.Frontiers in photonics · 2024Article
- Functional Photoacoustic Imaging for Placental Monitoring: A Mini Review.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Review
- Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
- Towards in vivo characterization of thyroid nodules suspicious for malignancy using multispectral optoacoustic tomography.European journal of nuclear medicine and molecular imaging · 2023Article
- A jointed feature fusion framework for photoacoustic image reconstruction.Photoacoustics · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
Abstract
significanceTwo-dimensional (2-D) fully convolutional neural networks have been shown capable of producing maps of sO2 from 2-D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as they are limited by the 2-D nature of the training data when the problem is inherently three-dimensional (3-D), and they have not been tested with realistic images.
aimTo demonstrate the capability of deep neural networks to process whole 3-D images and output 3-D maps of vascular sO2 from realistic tissue models/images. APPROACH: Two separate fully convolutional neural networks were trained to produce 3-D maps of vascular blood oxygen saturation and vessel positions from multiwavelength simulated images of tissue models.
resultsThe mean of the absolute difference between the true mean vessel sO2 and the network output for 40 examples was 4.4% and the standard deviation was 4.5%.
conclusions3-D fully convolutional networks were shown capable of producing accurate sO2 maps using the full extent of spatial information contained within 3-D images generated under conditions mimicking real imaging scenarios. We demonstrate that networks can cope with some of the confounding effects present in real images such as limited-view artifacts and have the potential to produce accurate estimates in vivo.
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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.