Multi-target reconstruction strategy based on blind source separation of surface measurement signals in FMT

Lizhi Zhang; Hongbo Guo; Jintao Li; Dizhen Kang; Diya Zhang; Xiaowei He; Yizhe Zhao; De Wei; Jingjing Yu

doi:10.1364/BOE.481348

Multi-target reconstruction strategy based on blind source separation of surface measurement signals in FMT

Biomed Opt Express. 2023 Feb 16;14(3):1159-1177. doi: 10.1364/BOE.481348. eCollection 2023 Mar 1.

Authors

Lizhi Zhang^{1

2}, Hongbo Guo^{1

2

3}, Jintao Li^{1

2}, Dizhen Kang^{1

2}, Diya Zhang^{1

2}, Xiaowei He^{1

2}, Yizhe Zhao^{1

2}, De Wei^{1

2}, Jingjing Yu⁴

Affiliations

¹ The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China.
² School of Information Sciences and Technology, Northwest University, Xi'an 710127, China.
³ guohb@nwu.edu.cn.
⁴ School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China.

Abstract

Fluorescence molecular tomography (FMT) is a promising molecular imaging technique for tumor detection in the early stage. High-precision multi-target reconstructions are necessary for quantitative analysis in practical FMT applications. The existing reconstruction methods perform well in retrieving a single fluorescent target but may fail in reconstructing a multi-target, which remains an obstacle to the wider application of FMT. In this paper, a novel multi-target reconstruction strategy based on blind source separation (BSS) of surface measurement signals was proposed, which transformed the multi-target reconstruction problem into multiple single-target reconstruction problems. Firstly, by multiple points excitation, multiple groups of superimposed measurement signals conforming to the conditions of BSS were constructed. Secondly, an efficient nonnegative least-correlated component analysis with iterative volume maximization (nLCA-IVM) algorithm was applied to construct the separation matrix, and the superimposed measurement signals were separated into the measurements of each target. Thirdly, the least squares fitting method was combined with BSS to determine the number of fluorophores indirectly. Lastly, each target was reconstructed based on the extracted surface measurement signals. Numerical simulations and in vivo experiments proved that it has the ability of multi-target resolution for FMT. The encouraging results demonstrate the significant effectiveness and potential of our method for practical FMT applications.