ArticleBiomedical optics express2022
Learned regularization for image reconstruction in sparse-view photoacoustic tomography.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Advanced methods of wearable ultrasound and photoacoustic imaging devices: A review.Photoacoustics · 2026Article
- A Model-Based Joint Feature Fusion framework for limited-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- A generative adversarial network with multi-scale structural features for sparse-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- Multi-frame pixel intensity consistency-based artifact reduction for photoacoustic computed tomography.Biomedical optics express · 2026Article
- TT-PADM: A Time-Driven Transformer Diffusion Model for Robust Sparse-View and Limited-View Photoacoustic Tomography.BME frontiers · 2026Article
- Model-informed deep-learning photoacoustic reconstruction for low-element linear array.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
- Score-based generative model-assisted information compensation for high-quality limited-view reconstruction in photoacoustic tomography.Photoacoustics · 2024Article
- Unveiling precision: a data-driven approach to enhance photoacoustic imaging with sparse data.Biomedical optics express · 2024Article
- Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration.Photoacoustics · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Constrained data acquisitions, such as sparse view measurements, are sometimes used in photoacoustic computed tomography (PACT) to accelerate data acquisition. However, it is challenging to reconstruct high-quality images under such scenarios. Iterative image reconstruction with regularization is a typical choice to solve this problem but it suffers from image artifacts. In this paper, we present a learned regularization method to suppress image artifacts in model-based iterative reconstruction in sparse view PACT. A lightweight dual-path network is designed to learn regularization features from both the data and the image domains. The network is trained and tested on both simulation and
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Registered trials
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