Laplacian manifold regularization method for fluorescence molecular tomography

He, Xuelei; Wang, Xiaodong; Yi, Huangjian; Chen, Yanrong; Zhang, Xu; Yu, Jingjing; He, Xiaowei

doi:10.1117/1.jbo.22.4.045009

Cited by 21 publications

(10 citation statements)

References 43 publications

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“…To verify the performance of our proposed algorithm, three comparative algorithms, including the sparse reconstruction algorithm based on ℓ 1/2 ‐norm regularization , the morphology recovery algorithm based on Gaussian weighted Laplace prior (GWLP) regularization , and the manifold regularization method based on gradient projection‐resolved Laplacian manifold (GPRLM) based on a joint ℓ 1 and Laplacian regularization were adopted for comparison.…”

Section: Methodsmentioning

confidence: 99%

“…As a geometrically motivated framework, many graph‐based manifold learning methods have been constructed in face recognition, image classification, medical imaging, and image reconstruction . In , a novel Laplacian manifold regularization method has been proposed to improve the reconstruction performance in both spatial aggregation and location accuracy for fluorescence molecular tomography. The advantage of manifold learning is its ability to preserve the intrinsic geometric information of data points, which will propose a new solution for tumor morphology recovery in BLT.…”

Section: Introductionmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparse‐graph manifold learning method for bioluminescence tomography

Guo

Gao

et al. 2020

Journal of Biophotonics

Self Cite

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“…In the simulation experiments, the fluorescence yield was set to 0.5𝑚𝑚 −1 , and the excitation and emission wavelength are 650 𝑛𝑚 and 670 𝑛𝑚, respectively. The relevant optical properties of the various organs of the digital mouse are in [41] . To evaluate the performance of the MPD strategy, we designed effectiveness and robustness experiments, respectively.…”

Section: Numerical Simulationmentioning

confidence: 99%

A adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction

Guo

Zhang

et al. 2023

Preprint

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To alleviate the ill-posed of the inverse problem in Fluorescent molecular tomography (FMT), many regularization methods based on L 2 or L 1 norm have been proposed. Whereas, the quality of regularization parameters affect the performance of the reconstruction algorithm. Some classical parameter selection strategies usually need initialization of parameter range and high computing costs, which is not universal in the practical application of FMT. In this paper, an universally applicable adaptive parameter selection method based on maximizing the probability of data (MPD) strategy was proposed. This strategy used maximum a posteriori (MAP) estimation and maximum likelihood (ML) estimation to establish a regularization parameters model. The stable optimal regularization parameters can be determined by multiple iterative estimates. Numerical simulations and in vivo experiments show that MPD strategy can obtain stable regularization parameters for both regularization algorithms based on L 2 or L 1 norm and achieve good reconstruction performance.

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“…A small signal disturbance may lead to a large reconstruction error. Therefore, researchers apply regularization techniques to FMT reconstruction to constrain the reconstruction process and reduce morbidity [8,9,28,30,63,[98][99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115]. The main principle of regularization is as follows:…”

Section: Inverse Problem Solvingmentioning

confidence: 99%

Recent methodology advances in fluorescence molecular tomography

Wang

Tian

2018

Vis. Comput. Ind. Biomed. Art

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Molecular imaging (MI) is a novel imaging discipline that has been continuously developed in recent years. It combines biochemistry, multimodal imaging, biomathematics, bioinformatics, cell & molecular physiology, biophysics, and pharmacology, and it provides a new technology platform for the early diagnosis and quantitative analysis of diseases, treatment monitoring and evaluation, and the development of comprehensive physiology. Fluorescence Molecular Tomography (FMT) is a type of optical imaging modality in MI that captures the three-dimensional distribution of fluorescence within a biological tissue generated by a specific molecule of fluorescent material within a biological tissue. Compared with other optical molecular imaging methods, FMT has the characteristics of high sensitivity, low cost, and safety and reliability. It has become the research frontier and research hotspot of optical molecular imaging technology. This paper took an overview of the recent methodology advances in FMT, mainly focused on the photon propagation model of FMT based on the radiative transfer equation (RTE), and the reconstruction problem solution consist of forward problem and inverse problem. We introduce the detailed technologies utilized in reconstruction of FMT. Finally, the challenges in FMT were discussed. This survey aims at summarizing current research hotspots in methodology of FMT, from which future research may benefit.

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Laplacian manifold regularization method for fluorescence molecular tomography

Cited by 21 publications

References 43 publications

Sparse‐graph manifold learning method for bioluminescence tomography

Sparse‐graph manifold learning method for bioluminescence tomography

A adaptive parameter selection strategy based on maximizing the probability of data for robust fluorescence molecular tomography reconstruction

Recent methodology advances in fluorescence molecular tomography

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