ArticlePhotoacoustics2025
Enhancing signal-to-noise ratio in real-time LED-based photoacoustic imaging: A comparative study of CNN-based deep learning architectures.
Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- FM-Adapt: Foundation model adaptation with photoacoustic-supervised learning for interventional ultrasound.Photoacoustics · 2026Article
- Atherosclerotic Plaque Characterization Magnetic Resonance Imaging In Vitro at 1.5 Tesla for the Assessment of Coronary Artery Disease.Journal of clinical medicine · 2026Article
- Optoacoustically augmented magnetic guidewire for radiation-free minimally invasive therapies.Science advances · 2026Article
- Identification of Cholesterol in Plaques of Atherosclerotic Using Magnetic Resonance Spectroscopy and 1D U-Net Architecture.Molecules (Basel, Switzerland) · 2026Article
- Model-informed deep-learning photoacoustic reconstruction for low-element linear array.Photoacoustics · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Recent advances in Light Emitting Diode (LED) technology have enabled a more affordable high frame rate photoacoustic imaging (PA) alternative to traditional laser-based PA systems that are costly and have slow pulse repetition rate. However, a major disadvantage with LEDs is the low energy outputs that do not produce high signal-to-noise ratio (SNR) PA images. There have been recent advancements in integrating deep learning methodologies aimed to address the challenge of improving SNR in LED-PA images, yet comprehensive evaluations across varied datasets and architectures are lacking. In this study, we systematically assess the efficacy of various Encoder-Decoder-based CNN architectures for enhancing SNR in real-time LED-based PA imaging. Through experimentation with
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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.