Accelerating vasculature imaging in tumor using mesoscopic fluorescence molecular tomography via a hybrid reconstruction strategy

Yang, Fuzheng; Gong, Xue; Faulkner, Denzel; Gao, Shan; Yao, Ru; Zhang, Yanli; Intes, Xavier

doi:10.1016/j.bbrc.2021.05.023

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…built upon this previous research in Ref. 134 . Here DL is used as a complementary method to accelerate reconstruction time and quality, while employing a conventional inverse solving algorithm, in this case, the least-squares inverse solver with weighted

norm.…”

Section: Deep Learning For Diffuse Optics-based Tomographic Imagingmentioning

confidence: 83%

Deep learning in macroscopic diffuse optical imaging

et al. 2022

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. Significance : Biomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in computational modeling and has demonstrated utility in numerous scientific domains and various forms of data analysis. Aim : We aim to comprehensively review the use of DL applied to macroscopic diffuse optical imaging (DOI). Approach : First, we provide a layman introduction to DL. Then, the review summarizes current DL work in some of the most active areas of this field, including optical properties retrieval, fluorescence lifetime imaging, and diffuse optical tomography. Results : The advantages of using DL for DOI versus conventional inverse solvers cited in the literature reviewed herein are numerous. These include, among others, a decrease in analysis time (often by many orders of magnitude), increased quantitative reconstruction quality, robustness to noise, and the unique capability to learn complex end-to-end relationships. Conclusions : The heavily validated capability of DL’s use across a wide range of complex inverse solving methodologies has enormous potential to bring novel DOI modalities, otherwise deemed impractical for clinical translation, to the patient’s bedside.

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norm.…”

Section: Deep Learning For Diffuse Optics-based Tomographic Imagingmentioning

confidence: 83%

Deep learning in macroscopic diffuse optical imaging

et al. 2022

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“…For these measurements, signal processing and pattern recognition are the critical data analysis tasks. In recent work, ANNs and convolutional neural networks for deep learning are being used for pattern recognition (135)(136)(137). In fact, artificial intelligence technologies are not just being used to detect specific features, but they are being used to correct (138,139) hyperspectral images (140) for measurement limitations.…”

Section: Mining Photoluminescence Imagesmentioning

confidence: 99%

Photoluminescence Probes in Data-Enabled Sensing

Suskil

Murray

Sanap

et al. 2023

Annual Rev. Anal. Chem.

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This review summarizes the current status of development in photoluminescent probes, multidimensional photoluminescence detection, and multivariate data analysis methods. It then highlights reports featuring multivariate analysis of multidimensional measurements of photoluminescent probes published between June 2015 and June 2022, emphasizing work in the last 5 years. Important trends include the development of probe arrays, which provide fingerprint responses to the analyte(s) of interest and facilitate the analysis of complex samples; the application of neural networks and deep learning to pattern recognition and feature selection in photoluminescence images; and the application of multiway multivariate analysis to mining matrices, three-way arrays, and higher-order measurements, including hyperspectral intensity and lifetime images. These examples illustrate the increase in information extraction provided by the combination of multidimensional measurements and multivariate analysis. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 16 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…The effectiveness of the method was verified by in vivo experiments. In 2021, Yang from Shandong Institute of Business and Technology reported a combination of convolutional symmetric network and traditional iterative algorithm to solve the inverse problem of mesoscopic DFT [9] . This method improved the accuracy and speed of reconstruction by reducing the redundant terms of sensitivity matrix and noise suppression.…”

Section: Introductionmentioning

confidence: 99%

Fluorescence pharmacokinetic parametric imaging method based on dynamic diffuse fluorescence tomography and deep learning

Zhang¹,

Zhang²,

Liu³

et al. 2022

Optics in Health Care and Biomedical Optics XII

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At present, the imaging of fluorescence pharmacokinetic parameters based on dynamic diffuse fluorescence tomography (D-DFT) technology have limitations in some aspects including the accuracy of physical model and the quantification of method. In this work, we propose a fluorescence pharmacokinetic parametric imaging method of tumor tissues based on D-DFT and deep learning. It mainly includes: a more realistic training and test simulation data set can be established by combining non-uniform tissue photon transport model and biological tissue fluorescence kinetics method. A fluorescence pharmacokinetic parametric reconstruction algorithm that involves an improved U-Net architecture based on the fully convolutional neural network is developed to break through the complexity bottleneck of imaging physical model. The numerical simulation results show that the method can realize image reconstruction of pharmacokinetic parameters with high spatial resolution and high quantitative accuracy.

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Accelerating vasculature imaging in tumor using mesoscopic fluorescence molecular tomography via a hybrid reconstruction strategy

Cited by 7 publications

References 21 publications

Deep learning in macroscopic diffuse optical imaging

Deep learning in macroscopic diffuse optical imaging

Photoluminescence Probes in Data-Enabled Sensing

Fluorescence pharmacokinetic parametric imaging method based on dynamic diffuse fluorescence tomography and deep learning

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