ArticleBiomedical optics express2021
Artifact removal in photoacoustic tomography with an unsupervised method.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 33 citations in OpenAlex.
- 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
- 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
- Residual-conditioned sparse transformer for photoacoustic image artifact reduction.Photoacoustics · 2025Article
- Zero-Shot Artifact2Artifact: Self-incentive artifact removal for photoacoustic imaging.Photoacoustics · 2025Article
- Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced score-based diffusion model.Photoacoustics · 2025Article
- MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data.Photoacoustics · 2025Article
- Multiple diffusion models-enhanced extremely limited-view reconstruction strategy for photoacoustic tomography boosted by multi-scale priors.Photoacoustics · 2024Article
- Score-based generative model-assisted information compensation for high-quality limited-view reconstruction in photoacoustic tomography.Photoacoustics · 2024Article
- Unsupervised disentanglement strategy for mitigating artifact in photoacoustic tomography under extremely sparse view.Photoacoustics · 2024Article
- Photoacoustic imaging plus X: a review.Journal of biomedical optics · 2024Review
- Learning spatially variant degradation for unsupervised blind photoacoustic tomography image restoration.Photoacoustics · 2023Article
- Photoacoustic imaging with limited sampling: a review of machine learning approaches.Biomedical optics express · 2023Review
- Photoacoustic image synthesis with generative adversarial networks.Photoacoustics · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Photoacoustic tomography (PAT) is an emerging biomedical imaging technology that can realize high contrast imaging with a penetration depth of the acoustic. Recently, deep learning (DL) methods have also been successfully applied to PAT for improving the image reconstruction quality. However, the current DL-based PAT methods are implemented by the supervised learning strategy, and the imaging performance is dependent on the available ground-truth data. To overcome the limitation, this work introduces a new image domain transformation method based on cyclic generative adversarial network (CycleGAN), termed as PA-GAN, which is used to remove artifacts in PAT images caused by the use of the limited-view measurement data in an unsupervised learning way. A series of data from phantom and
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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.