Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications
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Abstract (arXiv)
Photoacoustic imaging (PAI) represents an innovative biomedical imaging modality that harnesses the advantages of optical resolution and acoustic penetration depth while ensuring enhanced safety. Despite its promising potential across a diverse array of preclinical and clinical applications, the clinical implementation of PAI faces significant challenges, including the trade-off between penetration depth and spatial resolution, as well as the demand for faster imaging speeds. This paper explores the fundamental principles underlying PAI, with a particular emphasis on three primary implementations: photoacoustic computed tomography (PACT), photoacoustic microscopy (PAM), and photoacoustic endoscopy (PAE). We undertake a critical assessment of their respective strengths and practical limitations. Furthermore, recent developments in utilizing conventional or deep learning (DL) methodologies for image reconstruction and artefact mitigation across PACT, PAM, and PAE are outlined, demonstrating considerable potential to enhance image quality and accelerate imaging processes. Furthermore, this paper examines the recent developments in quantitative analysis within PAI, including the quantification of haemoglobin concentration, oxygen saturation, and other physiological parameters within tissues. Finally, our discussion encompasses current trends and future directions in PAI research while emphasizing the transformative impact of deep learning on advancing PAI.
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References · 23
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
- 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
- 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
- 2014Deep Learning in Neural Networks: An Overviewcited by 765
- 2019On instabilities of deep learning in image reconstruction - Does AI come at a cost?cited by 211
- 2020Residual Feature Distillation Network for Lightweight Image Super-Resolutioncited by 122
- 2023CoSeR: Bridging Image and Language for Cognitive Super-Resolutioncited by 66
- 2019Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learningcited by 60
- 2018Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removalcited by 56
- 2017Model based learning for accelerated, limited-view 3D photoacoustic tomographycited by 45
11 more
- 2024CAMixerSR: Only Details Need More "Attention"cited by 30
- 2020Deep Learning in Photoacoustic Tomography: Current approaches and future directionscited by 25
- 2021Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopycited by 20
- 2019Progressive Augmentation of GANscited by 13
- 2015Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissuescited by 6
- 2019Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approachcited by 5
- 2021AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Datacited by 3
- 2017Context encoding enables machine learning-based quantitative photoacousticscited by 2
- 2023Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomographycited by 1
- 2023Speeding up Photoacoustic Imaging using Diffusion Modelscited by 1
- 2024Streamlined Photoacoustic Image Processing with Foundation Models: A Training-Free Solutioncited by 1
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