ArticleScientific reports2020
Limited-View and Sparse Photoacoustic Tomography for Neuroimaging with Deep Learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed, 90 citations in OpenAlex.
- A Model-Based Joint Feature Fusion framework for limited-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- Ultrasound-Based Tomographic Imaging Reconstruction and Synthesis Methods: a Scoping Review.Journal of imaging informatics in medicine · 2026Article
- A generative adversarial network with multi-scale structural features for sparse-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- Progressive dual-branch adversarial diffusion for sparse-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- Physics-driven deep learning photoacoustic tomography.Fundamental research · 2026Article
- Attention-driven complementary information fusion network for sparse photoacoustic image reconstruction.Photoacoustics · 2026Article
- A Physics-Informed Neural Network forResearch square · 2026Article
- A Deep Transfer Learning Framework for Speed-of-Sound Aberration Correction in Full-Ring Photoacoustic Tomography.Sensors (Basel, Switzerland) · 2026Article
- TT-PADM: A Time-Driven Transformer Diffusion Model for Robust Sparse-View and Limited-View Photoacoustic Tomography.BME frontiers · 2026Article
- Reduction of photobleaching effects in photoacoustic imaging using noise agnostic, platform-flexible deep-learning methods.Journal of biomedical optics · 2025Article
- A PINN-driven game-theoretic framework in limited data photoacoustic tomography.Inverse problems · 2025Article
- Artifacts in photoacoustic imaging: Origins and mitigations.Photoacoustics · 2025Article
- Wavelet-enhanced residual optimal transport for Mamba-based image restoration in photoacoustic tomography.Photoacoustics · 2025Article
- Model-informed deep-learning photoacoustic reconstruction for low-element linear array.Photoacoustics · 2025Article
- Limited-view photoacoustic imaging reconstruction via high-quality self-supervised neural representation.Photoacoustics · 2025Article
- Lightweight sparse optoacoustic image reconstruction via an attention-driven multi-scale wavelet network.Photoacoustics · 2025Article
- MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data.Photoacoustics · 2025Article
- Enhancing signal-to-noise ratio in real-time LED-based photoacoustic imaging: A comparative study of CNN-based deep learning architectures.Photoacoustics · 2025Article
- Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced score-based diffusion model.Photoacoustics · 2025Article
- Multiple diffusion models-enhanced extremely limited-view reconstruction strategy for photoacoustic tomography boosted by multi-scale priors.Photoacoustics · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Photoacoustic tomography (PAT) is a non-ionizing imaging modality capable of acquiring high contrast and resolution images of optical absorption at depths greater than traditional optical imaging techniques. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their "view" of the imaging target, which result in image reconstruction artifacts degrading image quality. Iterative reconstruction methods can be used to reduce artifacts but are computationally expensive. In this work, we propose a novel deep learning approach termed pixel-wise deep learning (Pixel-DL) that first employs pixel-wise interpolation governed by the physics of photoacoustic wave propagation and then uses a convolution neural network to reconstruct an image. Simulated photoacoustic data from synthetic, mouse-brain, lung, and fundus vasculature phantoms were used for training and testing. Results demonstrated that Pixel-DL achieved comparable or better performance to iterative methods and consistently outperformed other CNN-based approaches for correcting artifacts. Pixel-DL is a computationally efficient approach that enables for real-time PAT rendering and improved image reconstruction quality for limited-view and sparse PAT.
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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.