ArticlePhotoacoustics2021
Comparing Deep Learning Frameworks for Photoacoustic Tomography Image Reconstruction.
Article in Photoacoustics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction.Tomography (Ann Arbor, Mich.) · 2023Pooled it
- Ultrasound-Based Tomographic Imaging Reconstruction and Synthesis Methods: a Scoping Review.Journal of imaging informatics in medicine · 2026Article
- sCMOS-based fNIRS system: validation via optical performance and cortical response.Translational psychiatry · 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
- MSD-Net: Multi-scale dense convolutional neural network for photoacoustic image reconstruction with sparse data.Photoacoustics · 2025Article
- Extractor-attention-predictor network for quantitative photoacoustic tomography.Photoacoustics · 2024Article
- Unsupervised denoising of photoacoustic images based on the Noise2Noise network.Biomedical optics express · 2024Article
- Dual-modal Photoacoustic and Ultrasound Imaging: from preclinical to clinical applications.Frontiers in photonics · 2024Article
- Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration.Photoacoustics · 2023Article
- Photoacoustic imaging of hidden dental caries using visible-light diode laser.Journal of applied clinical medical physics · 2023Article
- Photoacoustic imaging with limited sampling: a review of machine learning approaches.Biomedical optics express · 2023Review
- A deep-learning-based dose verification tool utilizing fluence maps for a cobalt-60 compensator-based intensity-modulated radiation therapy system.Physics and imaging in radiation oncology · 2023Article
- Application of photoacoustic computed tomography in biomedical imaging: A literature review.Bioengineering & translational medicine · 2023Review
- Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation.Photoacoustics · 2023Article
- Ultrasound-enhanced Unet model for quantitative photoacoustic tomography of ovarian lesions.Photoacoustics · 2022Article
- Alignment-invariant signal reality reconstruction in hyperspectral imaging using a deep convolutional neural network architecture.Scientific reports · 2022Article
- Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging.Journal of biomedical optics · 2022Article
- High frame rate (∼3 Hz) circular photoacoustic tomography using single-element ultrasound transducer aided with deep learning.Journal of biomedical optics · 2022Article
- Spectral crosstalk in photoacoustic computed tomography.Photoacoustics · 2022Article
Corrections and comments
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional reconstruction methods for photoacoustic images are not suitable for the scenario of sparse sensing and geometrical limitation. To overcome these challenges and enhance the quality of reconstruction, several learning-based methods have recently been introduced for photoacoustic tomography reconstruction. The goal of this study is to compare and systematically evaluate the recently proposed learning-based methods and modified networks for photoacoustic image reconstruction. Specifically, learning-based post-processing methods and model-based learned iterative reconstruction methods are investigated. In addition to comparing the differences inherently brought by the models, we also study the impact of different inputs on the reconstruction effect. Our results demonstrate that the reconstruction performance mainly stems from the effective amount of information carried by the input. The inherent difference of the models based on the learning-based post-processing method does not provide a significant difference in photoacoustic image reconstruction. Furthermore, the results indicate that the model-based learned iterative reconstruction method outperforms all other learning-based post-processing methods in terms of generalizability and robustness.
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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.