ArticleJournal of biomedical optics2022
High frame rate (∼3 Hz) circular photoacoustic tomography using single-element ultrasound transducer aided with deep learning.
Article in Journal of biomedical optics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- From light to sound: Seeing and hearing the placenta in health and disease.Science advances · 2026Review
- PA OmniNet: A retraining-free, generalizable deep learning framework for robust photoacoustic image reconstruction.Photoacoustics · 2025Article
- Model-informed deep-learning photoacoustic reconstruction for low-element linear array.Photoacoustics · 2025Article
- MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data.Photoacoustics · 2025Article
- Deep learning on photoacoustic tomography to remove image distortion due to inaccurate measurement of the scanning radius.Biomedical optics express · 2023Article
- Full-view LED-based optoacoustic tomography.Photoacoustics · 2023Article
- Looking deep inside tissue with photoacoustic molecular probes: a review.Journal of biomedical optics · 2022Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significance: In circular scanning photoacoustic tomography (PAT), it takes several minutes to generate an image of acceptable quality, especially with a single-element ultrasound transducer (UST). The imaging speed can be enhanced by faster scanning (with high repetition rate light sources) and using multiple-USTs. However, artifacts arising from the sparse signal acquisition and low signal-to-noise ratio at higher scanning speeds limit the imaging speed. Thus, there is a need to improve the imaging speed of the PAT systems without hampering the quality of the PAT image. Aim: To improve the frame rate (or imaging speed) of the PAT system by using deep learning (DL). Approach: For improving the frame rate (or imaging speed) of the PAT system, we propose a novel U-Net-based DL framework to reconstruct PAT images from fast scanning data. Results: The efficiency of the network was evaluated on both single- and multiple-UST-based PAT systems. Both phantom and Conclusions: We proposed an innovative method to improve the frame rate (or imaging speed) by using DL and with this method, the fastest frame rate of
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