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Nonconvex Laplacian Manifold Joint Method for Morphological Reconstruction of Fluorescence Molecular Tomography

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Abstract

Purpose

Fluorescence molecular tomography (FMT) is a promising technique for three-dimensional (3D) visualization of biomarkers in small animals. Morphological reconstruction is valuable and necessary for further applications of FMT owing to its innate requirement for knowledge of the molecular probe distributions.

Procedures

In this study, a Laplacian manifold regularization joint ℓ1/2-norm model is proposed for morphological reconstruction and solved by a nonconvex algorithm commonly referred to as the half-threshold algorithm. The model is combined with the structural and sparsity priors to achieve the location and structure of the target. In addition, two improvement forms (truncated and hybrid truncated forms) are proposed for better morphological reconstruction. The truncated form is proposed for balancing the sharpness and smoothness of the boundary of reconstruction. A hybrid truncated form is proposed for more structural priors. To evaluate the proposed methods, three simulation studies (morphological, robust, and double target analyses) and an in vivo experiment were performed.

Results

The proposed methods demonstrated morphological accuracy, location accuracy, and reconstruction robustness in glioma simulation studies. An in vivo experiment with an orthotopic glioma mouse model confirmed the advantages of the proposed methods. The proposed methods always yielded the best intersection of union (IoU) in simulations and in vivo experiments (mean of 0.80 IoU).

Conclusions

Simulation studies and in vivo experiments demonstrate that the proposed half-threshold hybrid truncated Laplacian algorithm had an improved performance compared with the comparative algorithm in terms of morphology.

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Funding

This study was funded by the Ministry of Science and Technology of China (2017YFA0205200), National Natural Science Foundation of China (81227901, 81527805, 81930053, and 61671449), Chinese Academy of Sciences (KFJ-STS-ZDTP-059, YJKYYQ20180048, and QYZDJ-SSW-JSC005), and Innovative Talents Promotion Plan of Shananxi (2017SR5024).

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Authors and Affiliations

  1. School of Information Sciences and Technology, Northwest University, Xi’an, 710127, China

    Xuelei He, Xiaowei He & Xiaolei Song

  2. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Xuelei He, Hui Meng, Kun Wang & Jie Tian

  3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100190, China

    Jie Tian

  4. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China

    Jie Tian

  5. Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, 100191, China

    Jie Tian

  6. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, 710127, China

    Jie Tian

  7. Zhuhai Precision Medical Center, Zhuhai People’s Hospital (affiliated with Jinan University), Zhuhai, 519000, China

    Jie Tian

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  1. Xuelei He
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Correspondence to Jie Tian.

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He, X., Meng, H., He, X. et al. Nonconvex Laplacian Manifold Joint Method for Morphological Reconstruction of Fluorescence Molecular Tomography. Mol Imaging Biol 23, 394–406 (2021). https://doi.org/10.1007/s11307-020-01568-8

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  • DOI: https://doi.org/10.1007/s11307-020-01568-8

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