ArticlePhotoacoustics2024
Unsupervised disentanglement strategy for mitigating artifact in photoacoustic tomography under extremely sparse view.
Article in Photoacoustics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Deep Learning-Based Correction for Scanning Radius Errors in Circular-Scan Photoacoustic Tomography.Journal of imaging · 2026Article
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- Edge-Aware Short-Chain Diffusion Enables High-Fidelity Sparse-Sampling Optoacoustic Tomography.BME frontiers · 2026Article
- Wavelet-enhanced residual optimal transport for Mamba-based image restoration in photoacoustic tomography.Photoacoustics · 2025Article
- Sparse scanning encoding and neural network decoding for compressed photoacoustic microscopy.Photoacoustics · 2025Article
- Residual-conditioned sparse transformer for photoacoustic image artifact reduction.Photoacoustics · 2025Article
- Zero-Shot Artifact2Artifact: Self-incentive artifact removal for photoacoustic imaging.Photoacoustics · 2025Article
- Intranasal delivery of engineered extracellular vesicles promotes neurofunctional recovery in traumatic brain injury.Journal of nanobiotechnology · 2025Article
- [A sparse-view cone-beam CT reconstruction algorithm based on bidirectional flow field- guided projection completion].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
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Authors and funding
8 authors.
Funding
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Abstract
Traditional methods under sparse view for reconstruction of photoacoustic tomography (PAT) often result in significant artifacts. Here, a novel image to image transformation method based on unsupervised learning artifact disentanglement network (ADN), named PAT-ADN, was proposed to address the issue. This network is equipped with specialized encoders and decoders that are responsible for encoding and decoding the artifacts and content components of unpaired images, respectively. The performance of the proposed PAT-ADN was evaluated using circular phantom data and the animal
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