Effective and robust approach for fluorescence molecular tomography based on CoSaMP and SP3 model

Abstract

Fluorescence molecular tomography (FMT) allows the detection and quanti¯cation of various biological processes in small animals in vivo, which expands the horizons of pre-clinical research and drug development. E±cient three-dimensional (3D) reconstruction algorithm is the key to accurate localization and quanti¯cation of°uorescent target in FMT. In this paper, 3D reconstruction of FMT is regarded as a sparse signal recovery problem and the compressive sampling matching pursuit (CoSaMP) algorithm is adopted to obtain greedy recovery of°uorescent signals. Moreover, to reduce the modeling error, the simpli¯ed spherical harmonics approximation to the radiative transfer equation (RTE), more speci¯cally SP 3 , is utilized to describe light propagation in biological tissues. The performance of the proposed reconstruction method is thoroughly evaluated by simulations on a 3D digital mouse model by comparing it with three representative greedy methods including orthogonal matching pursuit (OMP), stagewise OMP (StOMP), and regularized OMP (ROMP). The CoSaMP combined with SP 3 shows an improvement in reconstruction accuracy and exhibits distinct advantages over the comparative algorithms in multiple targets resolving. Stability analysis suggests that CoSaMP is robust to noise and performs stably with reduction of measurements. The feasibility and reconstruction accuracy of the proposed method are further validated by phantom experimental data.

Sparse‐graph manifold learning method for bioluminescence tomography

“…However, in three in vivo experiments, the actual source intensity and inverse mesh were significant difference. In order to reduce or eliminate this effect, we normalized the surface photon intensity and the columns of system matrix , and plotted the GCV‐curves and GCV‐surface.…”

K-Nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography

“…To alleviate the modeling error and solve the ill-posed inverse problem in FMT reconstruction, many researchers have developed different model-based methods. High-order approximation models [8,9] were proposed to describe the photon propagation. Moreover, the priori knowledge such as structural information (magnetic resonance imaging (MRI) or computed tomography (CT)) with corresponding optical parameters was utilized to build the photon propagation model [10,11].…”

A hybrid clustering algorithm for multiple‐source resolving in bioluminescence tomography

“…This is generally accomplished by solving a severely ill‐posed inverse problem with a reconstruction algorithm based on a light transport model . Great efforts have been made to reconstruct diffuse light sources, including conventional gradient‐based optimization, algebraic reconstruction technique (ART) and various regularization methods . In conventional reconstruction methods, one usually assumes that the number of light sources is known.…”