ArticlePhotoacoustics2020
Y-Net: Hybrid deep learning image reconstruction for photoacoustic tomography in vivo.
Article in Photoacoustics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Image Quality Improvement Techniques and Assessment Adequacy in Clinical Optoacoustic Imaging: A Systematic Review.Biosensors · 2022Pooled it
- DPAT-Net: A sequential dual-domain network with axial attention for limited-view photoacoustic image reconstruction.Photoacoustics · 2026Article
- Real-time deep learning-based vascular recognition in clinical breast photoacoustic imaging.Photoacoustics · 2026Article
- Deep learning-driven quantitative spectroscopic photoacoustic imaging for segmentation and oxygen saturation estimation.Ultrasonics · 2026Article
- Ultrasound-Based Tomographic Imaging Reconstruction and Synthesis Methods: a Scoping Review.Journal of imaging informatics in medicine · 2026Article
- [Research progress of photoacoustic imaging in diagnosis of limb lymphedema].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2026Review
- Progressive dual-branch adversarial diffusion for sparse-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- Physics-driven deep learning photoacoustic tomography.Fundamental research · 2026Article
- Photoacoustic Imaging for Women's Gynecological Health: Advances and Clinical Prospects.Bioengineering (Basel, Switzerland) · 2026Review
- Tropical dry forest land use/land cover change detection using semi-supervised deep learning algorithms and remote sensing.Environmental monitoring and assessment · 2026Article
- Attention-driven complementary information fusion network for sparse photoacoustic image reconstruction.Photoacoustics · 2026Article
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- Model-informed deep-learning photoacoustic reconstruction for low-element linear array.Photoacoustics · 2025Article
- Signal-domain speed-of-sound correction for ring-array-based photoacoustic tomography.Photoacoustics · 2025Article
- WALINET: A water and lipid identification convolutional neural network for nuisance signal removal inMagnetic resonance in medicine · 2025Article
- Lightweight sparse optoacoustic image reconstruction via an attention-driven multi-scale wavelet network.Photoacoustics · 2025Article
- Structure and oxygen saturation recovery of sparse photoacoustic microscopy images by deep learning.Photoacoustics · 2025Article
- Multiscale local sparsity and prior learning algorithm for Cherenkov-excited luminescence scanned tomography reconstruction.Applied optics · 2025Article
- Joint segmentation and image reconstruction with error prediction in photoacoustic imaging using deep learning.Photoacoustics · 2024Article
- Review on Photoacoustic Monitoring after Drug Delivery: From Label-Free Biomarkers to Pharmacokinetics Agents.Pharmaceutics · 2024Review
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Conventional reconstruction algorithms (e.g., delay-and-sum) used in photoacoustic imaging (PAI) provide a fast solution while many artifacts remain, especially for limited-view with ill-posed problem. In this paper, we propose a new convolutional neural network (CNN) framework Y-Net: a CNN architecture to reconstruct the initial PA pressure distribution by optimizing both raw data and beamformed images once. The network combines two encoders with one decoder path, which optimally utilizes more information from raw data and beamformed image. We compared our result with some ablation studies, and the results of the test set show better performance compared with conventional reconstruction algorithms and other deep learning method (U-Net). Both
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