Nonmodel-based bioluminescence tomography using a machine-learning reconstruction strategy

Gao, Yuan; Wang, Kun; An, Yu; Jiang, Shixin; Meng, Hui; Tian, Jie

doi:10.1364/optica.5.001451

Cited by 65 publications

(38 citation statements)

References 27 publications

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“…In our future work, more effective algorithms, such as half thresholding algorithm, can be combined in the SGML model for better computational efficiency and accuracy. Besides that, machine‐learning reconstruction strategy has been a new direction of BLT, which abandon the forward photon propagation modeling and model‐based inverse reconstruction with the advantage of high accuracy in 3D morphological imaging . However, high quality training data sets and the interpretability of deep learning model are two inherent limitation for this data driven method.…”

Section: Discussionmentioning

confidence: 99%

Sparse‐graph manifold learning method for bioluminescence tomography

Guo

Gao

et al. 2020

Journal of Biophotonics

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In preclinical researches, bioluminescence tomography (BLT) has widely been used for tumor imaging and monitoring, imaged-guided tumor therapy, and so forth. For these biological applications, both tumor spatial location and morphology analysis are the leading problems needed to be taken into account. However, most existing BLT reconstruction methods were proposed for some specific applications with a focus on sparse representation or morphology recovery, respectively. How to design a versatile algorithm that can simultaneously deal with both aspects remains an impending problem. In this study, a Sparse-Graph Manifold Learning (SGML) method was proposed to balance the source sparseness and morphology, by integrating non-convex sparsity constraint and dynamic Laplacian graph model. Furthermore, based on the nonconvex optimization theory and some iterative approximation, we proposed a novel iteratively reweighted soft thresholding algorithm (IRSTA) to solve the SGML model. Numerical simulations and in vivo experiments result demonstrated that the proposed SGML method performed much superior to the comparative methods in spatial localization and tumor morphology recovery for various source settings. It is believed that the SGML method can be applied to the related optical tomography and facilitate the development of optical molecular tomography. K E Y W O R D Sbiology and medicine, bioluminescence tomography, image reconstruction techniques, inverse problems

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Section: Discussionmentioning

confidence: 99%

Sparse‐graph manifold learning method for bioluminescence tomography

Guo

Gao

et al. 2020

Journal of Biophotonics

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“…A multi-layer perceptron (MLP)-based inverse problem simulation (IPS) method was proposed by Gao et al to obtain an end-to-end reconstruction for BLT. 67 The IPS architecture, which constructed the mapping from the measured data at the surface of biological tissue to the distribution of bioluminescence inside the tissue, consisted of 1 input layer, 4 hidden layers, and 1 output layer. According to Gao's work, the IPS could be regarded as a simulation to the widely used iterative shrinkage threshold (IST) method in solving the inverse problems.…”

Section: End-to-end Methods With Deep Learningmentioning

confidence: 99%

Brief review on learning-based methods for optical tomography

Zhang

2019

J. Innov. Opt. Health Sci.

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Learning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention. For instance, massive researches of deep learning methods for image reconstructions of computed tomography (CT) and magnetic resonance imaging (MRI) have been reported, indicating the great potential of deep learning for inverse problems. Optical technology-related medical imaging modalities including diffuse optical tomography (DOT), fluorescence molecular tomography (FMT), bioluminescence tomography (BLT), and photoacoustic tomography (PAT) are also dramatically innovated by introducing learning-based methods, in particular deep learning methods, to obtain better reconstruction results. This review depicts the latest researches on learning-based optical tomography of DOT, FMT, BLT, and PAT. According to the most recent studies, learning-based methods applied in the field of optical tomography are categorized as kernel-based methods and deep learning methods. In this review, the former are regarded as a sort of conventional learning-based methods and the latter are subdivided into model-based methods, post-processing methods, and end-to-end methods. Algorithm as well as data acquisition strategy are discussed in this review. The evaluations of these methods are summarized to illustrate the performance of deep learning-based reconstruction.

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“…The CNN fits nonlinear equations by machine learning rather than manually providing equations. The fitting results using CNN have exceeded the performance of many traditional nonlinear algorithms [21][22][23][24]. The super-resolution technology developed based on CNN improves blurring by up sampling the low-resolution images [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A new deep learning method for image deblurring in optical microscopic systems

Zhao

Chen

et al. 2020

Journal of Biophotonics

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Deconvolution is the most commonly used image processing method in optical imaging systems to remove the blur caused by the point‐spread function (PSF). While this method has been successful in deblurring, it suffers from several disadvantages, such as slow processing time due to multiple iterations required to deblur and suboptimal in cases where the experimental operator chosen to represent PSF is not optimal. In this paper, we present a deep‐learning‐based deblurring method that is fast and applicable to optical microscopic imaging systems. We tested the robustness of proposed deblurring method on the publicly available data, simulated data and experimental data (including 2D optical microscopic data and 3D photoacoustic microscopic data), which all showed much improved deblurred results compared to deconvolution. We compared our results against several existing deconvolution methods. Our results are better than conventional techniques and do not require multiple iterations or pre‐determined experimental operator. Our method has several advantages including simple operation, short time to compute, good deblur results and wide application in all types of optical microscopic imaging systems. The deep learning approach opens up a new path for deblurring and can be applied in various biomedical imaging fields.

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Nonmodel-based bioluminescence tomography using a machine-learning reconstruction strategy

Cited by 65 publications

References 27 publications

Sparse‐graph manifold learning method for bioluminescence tomography

Sparse‐graph manifold learning method for bioluminescence tomography

Brief review on learning-based methods for optical tomography

A new deep learning method for image deblurring in optical microscopic systems

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