A Graph-guided Hybrid Regularization Method For Bioluminescence Tomography

Chu, Mengxiang; Guo, Hongbo; He, Xuelei; Wang, Beilei; Li, Yanqiu; Hu, Xiangong; Yu, Jingjing

doi:10.1016/j.cmpb.2022.107329

Computer Methods and Programs in Biomedicine

2023

DOI: 10.1016/j.cmpb.2022.107329

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A Graph-guided Hybrid Regularization Method For Bioluminescence Tomography

Mengxiang Chu

Hongbo Guo

Xuelei He

et al.

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Cited by 3 publications

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Dictionary Learning Method Based on K‐Sparse Approximation and Orthogonal Procrustes Analysis for Reconstruction in Bioluminescence Tomography

Su,

Chen,

Tang

et al. 2024

Journal of Biophotonics

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Bioluminescence tomography (BLT) is one kind of noninvasive optical molecular imaging technology, widely used to study molecular activities and disease progression inside live animals. By combining the optical propagation model and inversion algorithm, BLT enables three‐dimensional imaging and quantitative analysis of light sources within organisms. However, challenges like light scattering and absorption in tissues, and the complexity of biological structures, significantly impact the accuracy of BLT reconstructions. Here, we propose a dictionary learning method based on K‐sparse approximation and Orthogonal Procrustes analysis (KSAOPA). KSAOPA uses an iterative alternating optimization strategy, enhancing solution sparsity with k‐coefficients Lipschitzian mappings for sparsity(K‐LIMAPS) in the sparse coding stage, and reducing errors with Orthogonal Procrustes analysis in the dictionary update stage, leading to stable and precise reconstructions. We assessed the method performance through simulations and in vivo experiments, which showed that KSAOPA excels in localization accuracy, morphological recovery, and in vivo applicability compared to other methods.

show abstract

Dictionary Learning Method Based on K‐Sparse Approximation and Orthogonal Procrustes Analysis for Reconstruction in Bioluminescence Tomography

Su,

Chen,

Tang

et al. 2024

Journal of Biophotonics

View full text Add to dashboard Cite

show abstract

Robust transformed l1 metric for fluorescence molecular tomography

Yuan

Kang

et al. 2023

Computer Methods and Programs in Biomedicine

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FSMN-Net: a free space matching network based on manifold convolution for optical molecular tomography

Li,

Wang,

et al. 2024

Opt. Lett.

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Optical molecular tomography (OMT) can monitor glioblastomas in small animals non-invasively. Although deep learning (DL) methods have made remarkable achievements in this field, improving its generalization against diverse reconstruction systems remains a formidable challenge. In this Letter, a free space matching network (FSMN-Net) was presented to overcome the parameter mismatch problem in different reconstruction systems. Specifically, a novel, to the best of our knowledge, manifold convolution operator was designed by considering the mathematical model of OMT as a space matching process. Based on the dynamic domain expansion concept, an end-to-end fully convolutional codec further integrates this operator to realize robust reconstruction with voxel-level accuracy. The results of numerical simulations and in vivo experiments demonstrate that the FSMN-Net can stably generate high-resolution reconstruction volumetric images under different reconstruction systems.

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A Graph-guided Hybrid Regularization Method For Bioluminescence Tomography

Cited by 3 publications

References 34 publications

Dictionary Learning Method Based on K‐Sparse Approximation and Orthogonal Procrustes Analysis for Reconstruction in Bioluminescence Tomography

Dictionary Learning Method Based on K‐Sparse Approximation and Orthogonal Procrustes Analysis for Reconstruction in Bioluminescence Tomography

Robust transformed l1 metric for fluorescence molecular tomography

FSMN-Net: a free space matching network based on manifold convolution for optical molecular tomography

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