Sparse-graph manifold learning method for bioluminescence tomography.

Abstract:

: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.

journal_name

J Biophotonics

journal_title

Journal of biophotonics

authors

Guo H,Gao L,Yu J,He X,Wang H,Zheng J,Yang X

doi

10.1002/jbio.201960218

subject

Has Abstract

pub_date

2020-04-01 00:00:00

pages

e201960218

issue

4

eissn

1864-063X

issn

1864-0648

journal_volume

13

pub_type

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