High-fidelity deconvolution for acoustic-resolution photoacoustic microscopy enabled by convolutional neural networks

Fěng, Fang; Liang, Siqi; Luo, Jiajia; Chen, Sung‐Liang

doi:10.1016/j.pacs.2022.100360

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…To synthesize AR-PAM images with different lateral resolutions, different PSF kernels should be applied. The lateral resolution can be measured with FWHM metric, as indicated in [40], the corresponding PSF kernel can be calculated with following formula (23) assuming the PSF kernels have gaussian profile:…”

Section: Simulation Resultsmentioning

confidence: 99%

“…al proposed to realistically enhance the lateral resolution of AR-PAM image with MultiResU-Net and conditional generative adversarial network [31][32][33][34][35]. Feng, Fei, et al proposed to use convolutional neural network to implement the deconvolution operation to the AR-PAM images, so that the lateral resolution of the imaging results can be improved [36]. Zhou, Yifeng et al proposed to use conditional GAN to enhance the imaging resolution obtained with Bessel beam to similar level of Gaussian beam, while overcoming the limited depth of focus imposed by the Gaussian-beam excitation [37].…”

Section: Introductionmentioning

confidence: 99%

“…What's more, the trained model lacks the generalizability to deal with various degradation mechanisms. As indicated in the state of the art work [40], the trained model for AR-PAM image deconvolution can only fit for specific degradation mechanism defined in the training dataset and hard to generalize to other situations. The lack of adaptability and generalizability has obviously hurt the wide application of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Enhancement of Acoustic Resolution Photoacoustic Microscopy Imaging Via Deep CNN Prior

et al. 2022

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Enhancement of Acoustic Resolution Photoacoustic Microscopy Imaging Via Deep CNN Prior

et al. 2022

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“…The experimental findings demonstrate that the artificial neural network models, namely EDSR and RRDBNet, exhibit enhanced capabilities in recovering multiscale features. These models are effective in accurately deconvolving complex feature sizes within photoacoustic microscopy images (Feng et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Comparison ConvDeconvNet Algorithm Vs. UNET for Fish Object Detection

Hindarto

2023

SinkrOn

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The precise identification and localization of fish entities within visual data is essential in diverse domains, such as marine biology and fisheries management, within computer vision. This study provides a thorough performance evaluation of two prominent deep learning algorithms, ConvDeconvNet and UNET, in the context of fish object detection. Both models are assessed using a dataset comprising a wide range of fish species, considering various factors, including accuracy of detection, speed of processing, and complexity of the model. The findings demonstrate that ConvDeconvNet exhibits superior performance in terms of detection accuracy, attaining a noteworthy degree of precision and recall in identifying fish entities. In contrast, the UNET model displays a notable advantage in terms of processing speed owing to its distinctive architectural design, rendering it a viable option for applications requiring real-time performance. The discourse surrounding the trade-off between accuracy and speed is examined, offering valuable perspectives for algorithm selection following specific criteria. Furthermore, this study highlights the significance of incorporating a diverse range of datasets for training and testing purposes when utilizing these models, as it significantly influences their overall performance. This study makes a valuable contribution to the continuous endeavors to improve the detection of fish objects in underwater images. It provides a thorough evaluation and comparison of ConvDeconvNet and UNET, thereby assisting researchers and practitioners in making well-informed decisions regarding selecting these models for their specific applications.

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“…High fidelity deconvolution methods, such as using RRDBNet, 109 also leverage deep learning for resolution improvement. RRDBNet is a deep residual network tailored for image deconvolution.…”

Section: Pa Plus Deep Learningmentioning

confidence: 99%

Photoacoustic imaging plus X: a review

Jiang,

Zhu,

Tong

et al. 2023

J. Biomed. Opt.

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. Significance Photoacoustic (PA) imaging (PAI) represents an emerging modality within the realm of biomedical imaging technology. It seamlessly blends the wealth of optical contrast with the remarkable depth of penetration offered by ultrasound. These distinctive features of PAI hold tremendous potential for various applications, including early cancer detection, functional imaging, hybrid imaging, monitoring ablation therapy, and providing guidance during surgical procedures. The synergy between PAI and other cutting-edge technologies not only enhances its capabilities but also propels it toward broader clinical applicability. Aim The integration of PAI with advanced technology for PA signal detection, signal processing, image reconstruction, hybrid imaging, and clinical applications has significantly bolstered the capabilities of PAI. This review endeavor contributes to a deeper comprehension of how the synergy between PAI and other advanced technologies can lead to improved applications. Approach An examination of the evolving research frontiers in PAI, integrated with other advanced technologies, reveals six key categories named “PAI plus X.” These categories encompass a range of topics, including but not limited to PAI plus treatment, PAI plus circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. Results After conducting a comprehensive review of the existing literature and research on PAI integrated with other technologies, various proposals have emerged to advance the development of PAI plus X. These proposals aim to enhance system hardware, improve imaging quality, and address clinical challenges effectively. Conclusions The progression of innovative and sophisticated approaches within each category of PAI plus X is positioned to drive significant advancements in both the development of PAI technology and its clinical applications. Furthermore, PAI not only has the potential to integrate with the above-mentioned technologies but also to broaden its applications even further.

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High-fidelity deconvolution for acoustic-resolution photoacoustic microscopy enabled by convolutional neural networks

Cited by 9 publications

References 31 publications

Adaptive Enhancement of Acoustic Resolution Photoacoustic Microscopy Imaging Via Deep CNN Prior

Adaptive Enhancement of Acoustic Resolution Photoacoustic Microscopy Imaging Via Deep CNN Prior

Performance Comparison ConvDeconvNet Algorithm Vs. UNET for Fish Object Detection

Photoacoustic imaging plus X: a review

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