Jingjing Yu

Improved sparse reconstruction for fluorescence molecular tomography with L 1/2 regularization

Fluorescence molecular tomography (FMT) is a promising imaging technique that allows in vivo visualization of molecular-level events associated with disease progression and treatment response. Accurate and efficient 3D reconstruction algorithms will facilitate the wide-use of FMT in preclinical research. Here, we utilize L1/2-norm regularization for improving FMT reconstruction. To efficiently solve the nonconvex L1/2-norm penalized problem, we transform it into a weighted L1-norm minimization problem and employ a homotopy-based iterative reweighting algorithm to recover small fluorescent targets. Both simulations on heterogeneous mouse model and in vivo experiments demonstrated that the proposed L1/2-norm method outperformed the comparative L1-norm reconstruction methods in terms of location accuracy, spatial resolution and quantitation of fluorescent yield. Furthermore, simulation analysis showed the robustness of the proposed method, under different levels of measurement noise and number of excitation sources.

Adaptive hp finite element method for fluorescence molecular tomography with simplified spherical harmonics approximation

Recently, the simplified spherical harmonics equations (SPN) model has attracted much attention in modeling the light propagation in small tissue geometries at visible and near-infrared wavelengths. In this paper, we report an efficient numerical method for fluorescence molecular tomography (FMT) that combines the advantage of SPN model and adaptive hp finite element method (hp-FEM). For purposes of comparison, hp-FEM and h-FEM are, respectively applied to the reconstruction process with diffusion approximation and SPN model. Simulation experiments on a 3D digital mouse atlas and physical experiments on a phantom are designed to evaluate the reconstruction methods in terms of the location and the reconstructed fluorescent yield. The experimental results demonstrate that hp-FEM with SPN model, yield more accurate results than h-FEM with diffusion approximation model does. The phantom experiments show the potential and feasibility of the proposed approach in FMT applications.

Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm

Fluorescence molecular tomography (FMT) is a fast-developing optical imaging modality that has great potential in early diagnosis of disease and drugs development. However, reconstruction algorithms have to address a highly ill-posed problem to fulfill 3D reconstruction in FMT. In this contribution, we propose an efficient iterative algorithm to solve the large-scale reconstruction problem, in which the sparsity of fluorescent targets is taken as useful a priori information in designing the reconstruction algorithm. In the implementation, a fast sparse approximation scheme combined with a stage-wise learning strategy enable the algorithm to deal with the ill-posed inverse problem at reduced computational costs. We validate the proposed fast iterative method with numerical simulation on a digital mouse model. Experimental results demonstrate that our method is robust for different finite element meshes and different Poisson noise levels.

Reconstruction algorithm for fluorescence molecular tomography using sorted L-one penalized estimation

Fluorescence molecular tomography (FMT) has been a promising imaging tool that provides convenience for accurate localization and quantitative analysis of the fluorescent probe. In this study, we present a reconstruction method combining sorted L-one penalized estimation with an iterative-shrinking permissible region strategy to reconstruct fluorescence targets. Both numerical simulation experiments on a three-dimensional digital mouse model and physical experiments on a cubic phantom were carried out to validate the accuracy, effectiveness, and robustness of the proposed method. The results indicate that the proposed method can produce better location and satisfactory fluorescent yield with computational efficiency, which makes it a practical and promising reconstruction method for FMT.

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

Fluorescence molecular tomography (FMT) allows the detection and quantification of various biological processes in small animals in vivo, which expands the horizons of pre-clinical research and drug development. Efficient three-dimensional (3D) reconstruction algorithm is the key to accurate localization and quantification of fluorescent 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 fluorescent signals. Moreover, to reduce the modeling error, the simplified spherical harmonics approximation to the radiative transfer equation (RTE), more specifically SP3, 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 SP3 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.

Performance evaluation of the simplified spherical harmonics approximation for cone-beam X-ray luminescence computed tomography imaging

As an emerging molecular imaging modality, cone-beam X-ray luminescence computed tomography (CB-XLCT) uses X-ray-excitable probes to produce near-infrared (NIR) luminescence and then reconstructs three-dimensional (3D) distribution of the probes from surface measurements. A proper photon-transportation model is critical to accuracy of XLCT. Here, we presented a systematic comparison between the common-used Monte Carlo model and simplified spherical harmonics (SPN). The performance of the two methods was evaluated over several main spectrums using a known XLCT material. We designed both a global measurement based on the cosine similarity and a locally-averaged relative error, to quantitatively assess these methods. The results show that the SP3 could reach a good balance between the modeling accuracy and computational efficiency for all of the tested emission spectrums. Besides, the SP1 (which is equivalent to the diffusion equation (DE)) can be a reasonable alternative model for emission wavelength over 6...

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

Bioluminescence tomography is a preclinical imaging modality to locate and quantify internal bioluminescent sources from surface measurements, which experienced rapid growth in the last 10 years. However, multiple-source resolving remains a challenging issue in BLT. In this study, it is treated as an unsupervised pattern recognition problem based on the reconstruction result, and a novel hybrid clustering algorithm combining the advantages of affinity propagation (AP) and K-means is developed to identify multiple sources automatically. Moreover, we incorporate the clustering analysis into a general multiple-source reconstruction framework, which can provide stable reconstruction and accurate resolving result without providing the number of targets. Numerical simulations and in vivo experiments on 4T1-luc2 mouse model were conducted to assess the performance of the proposed method in multiple-source resolving. The encouraging results demonstrate significant effectiveness and potential of our method in preclinical BLT applications.

Synchronization-based clustering algorithm for reconstruction of multiple reconstructed targets in fluorescence molecular tomography

Fluorescence molecular tomography (FMT) is an important in vivo molecular imaging technique and has been widely studied in preclinical research. Many methods perform well in the reconstruction of a single fluorescent target but may fail in reconstructing multiple targets because of the severe ill-posedness of the FMT inverse problem. In this paper the original synchronization-inspired clustering algorithm (OSC) is introduced into FMT for resolving multiple targets from the reconstruction result. Based on OSC, a synchronization-based clustering algorithm for FMT (SC-FMT) is developed to further improve location accuracy. Both algorithms utilize the minimum spanning tree to automatically identify the number of the reconstructed targets without prior information and human intervention. A serial of numerical simulation results demonstrates that SC-FMT and OSC can resolve multiple targets robustly and automatically, which also shows the potential of the proposed postprocessing algorithms in FMT reconstruction.

L1/2 REGULARIZATION METHOD FOR MULTIPLE-TARGET RECONSTRUCTION IN FLUORESCENT MOLECULAR TOMOGRAPHY

We present a method to accurately localize multiple small fluorescent objects within the tissue using fluorescence molecular tomography (FMT). The proposed method exploits the localized or sparse nature of the fluorophores in the tissue as a priori information to considerably improve the accuracy of the reconstruction of fluorophore distribution. This is accomplished by minimizing a cost function that includes the L1/2 norm of the fluorophore distribution vector. To deal with the nonconvex penalty, the L1/2 regularizer is transformed into a reweighted L1-norm minimization problem and then it is efficiently solved by a homotopy-based algorithm. Simulation experiments on a 3D digital mouse atlas are performed to verify the feasibility of the proposed method, and the results demonstrate L1/2 regularization is a promising approach for image reconstruction problem of FMT.

A hp adaptive finite element algorithm for fluorescence molecular tomography based on SPN model

The diffusion approximation of the radiative transport equation is the most widely used model in current researches on fluorescence molecular tomography (FMT), which is limited in some low or zero scattering regions. Recently, the simplified spherical harmonics equations (SPN) model has attracted much attention in modeling the light propagation in small tissue geometries at visible and near-infrared wavelengths. In this paper, we report an efficient numerical method for FMT that combines the advantage of SPN model and hp-FEM. For comparison purposes, hp-FEM and h-FEM are respectively applied in the reconstruction process with diffusion model and SPN model. Simulation experiments on a 3D digital mouse atlas are designed to evaluate the reconstruction methods in terms of the location and the reconstructed fluorescent yield. The experimental results demonstrate that hp-FEM with SPN model, yield more accurate results than h-FEM with DA model does. And the reconstructed results show the potential and feasibility of the proposed approach.