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Inside Cover: Sparse‐graph manifold learning method for bioluminescence tomography (J. Biophotonics 4/2020)
Author(s) -
Guo Hongbo,
Gao Ling,
Yu Jingjing,
He Xiaowei,
Wang Hai,
Zheng Jie,
Yang Xudong
Publication year - 2020
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Reports
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.202070011
Subject(s) - manifold (fluid mechanics) , graph , computer science , nonlinear dimensionality reduction , cover (algebra) , constraint (computer aided design) , algorithm , artificial intelligence , mathematics , theoretical computer science , engineering , mechanical engineering , dimensionality reduction , geometry
This study proposed a Sparse‐Graph Manifold Learning (SGML) method to balance the sparseness and morphology preserving for bioluminescence tomography reconstruction. It inherits the benefits of non‐convex sparsity constraint and dynamic Laplacian graph model. The results of numerical simulations and in vivo experiments demonstrate that the proposed method yields accurate and robust results in terms of tumor spatial location and morphology recovery. Further details can be found in the article by Hongbo Guo, Ling Gao, Jingjing Yu, et al. ( e201960218 )