ArticlePhotoacoustics2021
Deep learning enabled real-time photoacoustic tomography system via single data acquisition channel.
Article in Photoacoustics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Ultrasound-Based Tomographic Imaging Reconstruction and Synthesis Methods: a Scoping Review.Journal of imaging informatics in medicine · 2026Article
- Nanotechnology-Driven Ultrasound/Photoacoustic Theranostics for Real-Time Imaging and Therapeutics.Nanotheranostics · 2026Review
- Joint segmentation and image reconstruction with error prediction in photoacoustic imaging using deep learning.Photoacoustics · 2024Article
- Photoacoustic imaging plus X: a review.Journal of biomedical optics · 2024Review
- Compressed single-shot 3D photoacoustic imaging with a single-element transducer.Photoacoustics · 2023Article
- Photoacoustic imaging with limited sampling: a review of machine learning approaches.Biomedical optics express · 2023Review
- Single-shot photoacoustic imaging with single-element transducer through a spatiotemporal encoder.Journal of biomedical optics · 2023Article
- Improving needle visibility in LED-based photoacoustic imaging using deep learning with semi-synthetic datasets.Photoacoustics · 2022Article
- Photoacoustic imaging aided with deep learning: a review.Biomedical engineering letters · 2022Review
- Deep learning in photoacoustic imaging: a review.Journal of biomedical optics · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Photoacoustic computed tomography (PACT) combines the optical contrast of optical imaging and the penetrability of sonography. In this work, we develop a novel PACT system to provide real-time imaging, which is achieved by a 120-elements ultrasound array only using a single data acquisition (DAQ) channel. To reduce the channel number of DAQ, we superimpose 30 nearby channels' signals together in the analog domain, and shrinking to 4 channels of data (120/30 = 4). Furthermore, a four-to-one delay-line module is designed to combine these four channels' data into one channel before entering the single-channel DAQ, followed by decoupling the signals after data acquisition. To reconstruct the image from four superimposed 30-channels' PA signals, we train a dedicated deep learning model to reconstruct the final PA image. In this paper, we present the preliminary results of phantom and in-vivo experiments, which manifests its robust real-time imaging performance. The significance of this novel PACT system is that it dramatically reduces the cost of multi-channel DAQ module (from 120 channels to 1 channel), paving the way to a portable, low-cost and real-time PACT system.
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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.