ArticlePhotoacoustics2022
Improving needle visibility in LED-based photoacoustic imaging using deep learning with semi-synthetic datasets.
Article in Photoacoustics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Efficient denoising in LED-based optoacoustic tomography with squeeze-and-excitation deep convolutional networks.Journal of biomedical optics · 2026Article
- Joint segmentation and image reconstruction with error prediction in photoacoustic imaging using deep learning.Photoacoustics · 2024Article
- Learning-based sound speed estimation and aberration correction for linear-array photoacoustic imaging.Photoacoustics · 2024Article
- Development of non-contact foreign body imaging base on photoacoustic signal intensity measurement.Journal of applied clinical medical physics · 2024Article
- Photoacoustic imaging plus X: a review.Journal of biomedical optics · 2024Review
- Ultrasound-guided needle tracking with deep learning: A novel approach with photoacoustic ground truth.Photoacoustics · 2023Article
- Handheld interventional ultrasound/photoacoustic puncture needle navigation based on deep learning segmentation.Biomedical optics express · 2023Article
- Intraoperative laparoscopic photoacoustic image guidance system in the da Vinci surgical system.Biomedical optics express · 2023Article
- Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast.Photoacoustics · 2023Review
- Enhanced Photoacoustic Visualisation of Clinical Needles by Combining Interstitial and Extracorporeal Illumination of Elastomeric Nanocomposite Coatings.Sensors (Basel, Switzerland) · 2022Article
- Absolute Quantitative Photoacoustic Imaging for Contrast Agents Concentration Estimation Using a Spectral Decomposition Approach.Molecular imagingArticle
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 has shown great potential for guiding minimally invasive procedures by accurate identification of critical tissue targets and invasive medical devices (such as metallic needles). The use of light emitting diodes (LEDs) as the excitation light sources accelerates its clinical translation owing to its high affordability and portability. However, needle visibility in LED-based photoacoustic imaging is compromised primarily due to its low optical fluence. In this work, we propose a deep learning framework based on U-Net to improve the visibility of clinical metallic needles with a LED-based photoacoustic and ultrasound imaging system. To address the complexity of capturing ground truth for real data and the poor realism of purely simulated data, this framework included the generation of semi-synthetic training datasets combining both simulated data to represent features from the needles and
Indexed as
Identifiers
What OpenQuestion holds
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.