Image reconstruction with a deep convolutional neural network in high-density super-resolution microscopy

Yao, Bowen; Li, Wen; Pan, Wenhui; Yang, Zhigang; Chen, Danni; Li, Jia; Qu, Junle

doi:10.1364/oe.392358

Cited by 20 publications

(10 citation statements)

References 20 publications

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“…Similarly, numerous optimization scenarios within the optoelectronic system require optimization, such as adaptive optics and fiber coupling. Adaptive optics requires multidimensional optimization that can enable clear imaging in optoelectronic tracking systems, making image quality much better, and reducing the difficulty of target detection and target tracking algorithms [21]. Fiber-optic coupling is a simplified version of adaptive optics, and generally, needles are optimized in only two dimensions.…”

Section: Experimental Designmentioning

confidence: 99%

A Neural Network-Based Method for Fast Capture and Tracking of Laser Links between Nonorbiting Platforms

et al. 2022

Computational Intelligence and Neuroscience

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In this paper, a neural network approach is used to conduct an in-depth study and analysis of the fast capture tracking method for laser links between nonorbiting platforms. The experimental platform of the convolutional neural network- (CNN-) based free-space optical communication (FSO) wavefront correction system is built indoors, and the wavefront distortion correction performance of the CNN-based wavefront correction method is investigated. The experimental results show that the coupling power loss can be reduced to small after the CNN method correction under weak and strong turbulence. The accuracy of the above model is verified by comparing the simulation data with the experimentally measured data, thus realizing the coordinate decoupling of the coarse aiming mechanism and weakening the influence of structural factors on the tracking accuracy of the system. The tracking correlation equation of the influence of beam far-field dynamic characteristics on the tracking stability of the link is established, and the correlation factor variance of beam far-field dynamic characteristics is used to provide a quantitative analysis method for the evaluation and prediction of the comprehensive performance of the link tracking stability. The influence of beam divergence angle, wavefront distortion, detector accuracy, and atmospheric turbulence disturbance on the correlation factor variance of beam far-field dynamic characteristics of laser link beacons is modelled, and the link tracking stability optimization method is proposed under the requirement of link tracking accuracy, which provides an effective solution analysis method to realize the improvement of laser link tracking stability.

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Section: Experimental Designmentioning

confidence: 99%

A Neural Network-Based Method for Fast Capture and Tracking of Laser Links between Nonorbiting Platforms

et al. 2022

Computational Intelligence and Neuroscience

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“…With the continuous development of imaging technology, perineotomy under the guidance of ultrasound images can not only show the layers of the tissues but also reduce the risk of pudendal nerve block [8]. Yao et al [9] reported that the use of an ultrasound probe to move along the pudendal nerve in the human body for translation can clearly show the pudendal nerve at all levels. Liu et al [10] proposed the fast super resolution reconstructed convolutional neural network (FSRCNN) algorithm in 2018. e network structure is improved.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Image-Guided Pudendal Nerve Block on Analgesic Effect of Perineotomy under Optimized Fast Super Resolution Reconstructed Convolutional Neural Network Algorithm

Zhang

Xiao-ying

Zhao

et al. 2021

Scientific Programming

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This work was aimed to study the analgesic effect of pudendal nerve block on obstetrics and gynecology under the guidance of ultrasound image based on optimized fast super resolution reconstructed convolutional neural network (FSRCNN) algorithm. A total of 110 primiparas from hospital who gave birth through vagina were randomly rolled into experimental group (55 cases) and control group (55 cases). The optimized FSRCNN algorithm was constructed, compared with the FSRCNN algorithm and the Bicubic algorithm and applied to 110 cases of maternal patients undergoing perineotomy under ultrasound image-guided pudendal nerve block. Visual analogue scoring (VAS), incision suture pain VAS score, occurrence of complications, puerpera labor time, and newborn weight were recorded and compared, so did Apgar score of newborns, numbness of maternal thigh, recovery of puncture site, and satisfaction of maternal analgesia. The results showed that the peak signal-to-noise ratio (PSNR) of the high-resolution image reconstructed by the FSRCNN algorithm was 32.68 dB and that reconstructed by the optimized FSRCNN algorithm was 32.19 dB. The PSNR of the Bicubic algorithm processed image was 28.51 dB. In the lateral resection of episiotomy in the second stage of labor, the visual analog score (2.3 ± 1.5 points) of the experimental group was inferior to that of the control group (7.1 ± 2.6 points) ( P < 0.05 ). The visual analogue score of stitch pain (1.3 ± 0.8 points) was also inferior to that of the control group (5.2 ± 1.9 points) ( P < 0.05 ). Moreover, the satisfaction of the parturients in the experimental group (9.86 ± 0.41 points) was considerably superior to that of the control group (7.36 ± 1.25 points) ( P < 0.05 ). In short, the optimized FSRCNN algorithm had a short training time and good reconstruction effect. Ultrasound-guided pudendal nerve block had a substantial analgesic effect on the second stage of labor and improved parturients’ satisfaction.

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“…CBV refers to the VOLUME of blood through a certain cross-section of cerebrovascular in a unit time, which can reflect the damage of brain tissue. 14 - 15 CBF refers to the total amount of blood contained in cerebral blood vessels (including cerebral arteries, arterioles, and capillaries), and it has important guiding value for the clinical treatment of patients with acute cerebral infarction. 16 , 17 .…”

Section: Discussionmentioning

confidence: 99%

Adoption of computerized tomography perfusion imaging in the diagnosis of acute cerebral infarct under optimized deconvolution algorithm

Fang¹,

Zhai

2021

Pak J Med Sci

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Objectives: To explore the significance of the hemodynamic parameters of Computerized Tomography Perfusion Imaging (CTPI) under the deconvolution optimization algorithm for the diagnosis and treatment of patients with acute cerebral infarct (ACI). Methods: A hundred and ten patients with ACI from December 2018 to September 2019 were selected for research, and CTPI was performed before and after Edaravone injection treatment. Then, the CTPI deconvolution algorithm based on the weighted adaptive (WA) total variation (TV) (WA-TV) optimization was constructed, which was compared with tensor total variation (TTV) and Motion-adaptive sparse parity (MASP). Brain Perfusion 4.0 was applied to obtain the relative time to peak (rTTP), the relative transit time of mean (rMTT), relative cerebral blood volume (rCBV), and relative cerebral blood flow (rCBF) of the core infarction area (CIA) and penumbra ischemic (PI). Results: In four parameters of rTTP, rMTT, rCBV, and CBF, the peak signal to noise ratio (PSNR) of the WA-TV algorithm was higher than the MSAP and TTV algorithms, while the Mean Square Error (MSE) and Mean Absolute Error (MAE) were lower than MSAP and TTV algorithms (P<0.05); the parameters of rCBV (71.56±9.87), rCBF (43.17±7.06) of the CIA before treatment were higher than PI (23.66±7.22; 18.37±3.99), rMTT (124.83±9.73) and rTTP (122.57±7.41) were lower than the PI (183.17±10.16); 150.74±9.74) (P<0.05). After treatment, the rCBV and rCBF of PI were higher than before treatment, and rMTT and rTTP were lower than before treatment (P<0.05), and there was no obvious difference in rCBV, rCBF, rMTT, and rTTP before and after treatment in the CIA (P>0.05). Conclusion: Compared with TTV and MASP, the WA-TV algorithm performs better in noise reduction and artifact reduction. The CTPI parameters of rCBV, rCBF, rMTT, and rTTP are all important indications for the diagnosis of PI and ACI. doi: https://doi.org/10.12669/pjms.37.6-WIT.4884 How to cite this:Fang B, Zhai H. Adoption of computerized tomography perfusion imaging in the diagnosis of acute cerebral infarct under optimized deconvolution algorithm. Pak J Med Sci. 2021;37(6):1687-1692. doi: https://doi.org/10.12669/pjms.37.6-WIT.4884 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Image reconstruction with a deep convolutional neural network in high-density super-resolution microscopy

Cited by 20 publications

References 20 publications

A Neural Network-Based Method for Fast Capture and Tracking of Laser Links between Nonorbiting Platforms

A Neural Network-Based Method for Fast Capture and Tracking of Laser Links between Nonorbiting Platforms

Ultrasound Image-Guided Pudendal Nerve Block on Analgesic Effect of Perineotomy under Optimized Fast Super Resolution Reconstructed Convolutional Neural Network Algorithm

Adoption of computerized tomography perfusion imaging in the diagnosis of acute cerebral infarct under optimized deconvolution algorithm

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