Huangsheng Pu

A Direct Method With Structural Priors for Imaging Pharmacokinetic Parameters in Dynamic Fluorescence Molecular Tomography

Images of pharmacokinetic parameters in dynamic fluorescence molecular tomography (FMT) have the potential to provide quantitative physiological information for biological studies and drug development. However, images obtained with conventional indirect methods suffer from low signal-to-noise ratio because of failure in efficiently modeling the measurement noise. Besides, FMT suffers from low spatial resolution due to its ill-posed nature, which further reduces the image quality. In this letter, we present a direct method with structural priors for imaging pharmacokinetic parameters, which uses a nonlinear objective function to efficiently model the measurement noise and utilizes the structural priors to mitigate the ill-posedness of FMT. The results of numerical simulations and in vivo mouse experiments demonstrate that the proposed method leads to significant improvements in the image quality.

Bayesian Framework Based Direct Reconstruction of Fluorescence Parametric Images

Fluorescence imaging has been successfully used in the study of pharmacokinetic analysis, while dynamic fluorescence molecular tomography (FMT) is an attractive imaging technique for three-dimensionally resolving the metabolic process of fluorescent biomarkers in small animals in vivo. Parametric images obtained by combining dynamic FMT with compartmental modeling can provide quantitative physiological information for biological studies and drug development. However, images obtained with conventional indirect methods suffer from poor image quality because of failure in utilizing the temporal correlations of boundary measurements. Besides, FMT suffers from low spatial resolution due to its ill-posed nature, which further reduces the image quality. In this paper, we propose a novel method to directly reconstruct parametric images from boundary measurements based on maximum a posteriori (MAP) estimation with structural priors in a Bayesian framework. The proposed method can utilize structural priors obtained from an X-ray computed tomography system to mitigate the ill-posedness of dynamic FMT inverse problem, and use direct reconstruction strategy to make full use of temporal correlations of boundary measurements. The results of numerical simulations and in vivo mouse experiments demonstrate that the proposed method leads to significant improvements in the reconstruction quality of parametric images as compared with the conventional indirect method and a previously developed direct method.

Full-direct method for imaging pharmacokinetic parameters in dynamic fluorescence molecular tomography

Images of pharmacokinetic parameters (also known as parametric images) in dynamic fluorescence molecular tomography (FMT) can provide three-dimensional metabolic information for biological studies and drug development. However, the ill-posed nature of FMT and the high temporal variation of fluorophore concentration together make it difficult to obtain accurate parametric images in small animals in vivo. In this letter, we present a method to directly reconstruct the parametric images from the boundary measurements based on hybrid FMT/X-ray computed tomography (XCT) system. This method can not only utilize structural priors obtained from the XCT system to mitigate the ill-posedness of FMT but also make full use of the temporal correlations of boundary measurements to model the high temporal variation of fluorophore concentration. The results of numerical simulation and mouse experiment demonstrate that the proposed method leads to significant improvements in the reconstruction quality of parametric images.

An adaptive support driven reweighted L1-regularization algorithm for fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) is a promising in vivo functional imaging modality in preclinical study. When solving the ill-posed FMT inverse problem, L1 regularization can preserve the details and reduce the noise in the reconstruction results effectively. Moreover, compared with the regular L1 regularization, reweighted L1 regularization is recently reported to improve the performance. In order to realize the reweighted L1 regularization for FMT, an adaptive support driven reweighted L1-regularization (ASDR-L1) algorithm is proposed in this work. This algorithm has two integral parts: an adaptive support estimate and the iteratively updated weights. In the iteratively reweighted L1-minimization sub-problem, different weights are equivalent to different regularization parameters at different locations. Thus, ASDR-L1 can be considered as a kind of spatially variant regularization methods for FMT. Physical phantom and in vivo mouse experiments were performed to validate the proposed algorithm. The results demonstrate that the proposed reweighted L1-reguarization algorithm can significantly improve the performance in terms of relative quantitation and spatial resolution.

Separating structures of different fluorophore concentrations by principal component analysis on multispectral excitation-resolved fluorescence tomography images

Multispectral excitation-resolved fluorescence tomography (MEFT) uses excitation light of different wavelengths to illuminate the fluorophores and obtains the reconstruction image frame which is fluorescence yield at each corresponding wavelength. For structures containing fluorophores of different concentrations, fluorescence yields show different variation trends with the excitation spectrum. In this study, principal component analysis (PCA) is used to analyze the MEFT reconstructed image frames. By taking advantage of the different variation trends of fluorescence yields, PCA can provide a set of principal components (PCs) in which structures containing different concentrations of fluorophores are shown separately. Simulations and experiments are both performed to test the performance of the proposed algorithm. The results suggest that the location and structure of fluorophores with different concentrations can be obtained and the contrast of fluorophores can be improved further by using this algorithm.

Resolving fluorophores by unmixing multispectral fluorescence tomography with independent component analysis

It is a challenging problem to resolve and identify drug (or non-specific fluorophore) distribution throughout the whole body of small animals in vivo. In this article, an algorithm of unmixing multispectral fluorescence tomography (MFT) images based on independent component analysis (ICA) is proposed to solve this problem. ICA is used to unmix the data matrix assembled by the reconstruction results from MFT. Then the independent components (ICs) that represent spatial structures and the corresponding spectrum courses (SCs) which are associated with spectral variations can be obtained. By combining the ICs with SCs, the recovered MFT images can be generated and fluorophore concentration can be calculated. Simulation studies, phantom experiments and animal experiments with different concentration contrasts and spectrum combinations are performed to test the performance of the proposed algorithm. Results demonstrate that the proposed algorithm can not only provide the spatial information of fluorophores, but also recover the actual reconstruction of MFT images.

Acceleration of dynamic fluorescence molecular tomography with principal component analysis

Dynamic fluorescence molecular tomography (FMT) is an attractive imaging technique for three-dimensionally resolving the metabolic process of fluorescent biomarkers in small animal. When combined with compartmental modeling, dynamic FMT can be used to obtain parametric images which can provide quantitative pharmacokinetic information for drug development and metabolic research. However, the computational burden of dynamic FMT is extremely huge due to its large data sets arising from the long measurement process and the densely sampling device. In this work, we propose to accelerate the reconstruction process of dynamic FMT based on principal component analysis (PCA). Taking advantage of the compression property of PCA, the dimension of the sub weight matrix used for solving the inverse problem is reduced by retaining only a few principal components which can retain most of the effective information of the sub weight matrix. Therefore, the reconstruction process of dynamic FMT can be accelerated by solving the smaller scale inverse problem. Numerical simulation and mouse experiment are performed to validate the performance of the proposed method. Results show that the proposed method can greatly accelerate the reconstruction of parametric images in dynamic FMT almost without degradation in image quality.

Unmixing multiple adjacent fluorescent targets with multispectral excited fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) can visualize biological activities at cellular and molecular levels in vivo, and has been extensively used in drug delivery and tumor detection research of small animals. The ill-posedness of the FMT inverse problem makes it difficult to reconstruct and unmix multiple adjacent fluorescent targets that have different functional features but are labeled with the same fluorochrome. A method based on independent component analysis for multispectral excited FMT was proposed in our previous study. It showed that double fluorescent targets with certain edge-to-edge distance (EED) could be unmixed by the method. In this study, the situation is promoted to unmix multiple adjacent fluorescent targets (i.e., more than two fluorescent targets and EED=0). Phantom experiments on the resolving ability of the proposed algorithm demonstrate that the algorithm performs well in unmixing multiple adjacent fluorescent targets in both lateral and axial directions. And also, we recovered the locational information of each independent fluorescent target and described the variable trends of the corresponding fluorescent targets under the excitation spectrum. This method is capable of unmixing multiple fluorescent targets with small EED but labeled with the same fluorochrome, and may be used in imaging of nonspecific probe targeting and metabolism of drugs.

Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography

Abstract. In conventional fluorescence molecular tomography, the distribution of fluorescent contrast agents is reconstructed with the assumption of constant concentration during data acquisition for each image frame. However, the concentration of fluorescent contrast target is usually time-varying in experiments or in-vivo studies. In this case, the reconstruction methods cannot be directly applied to the fluorescence measurements without considering the time-varying effects of concentration. We propose a modified forward model by dividing the fluorescence yield distribution into two parts: one is a constant representing the spatial distribution of the fluorescent target and the other is an impact factor representing the effects of the concentration change and other possible factors. By extracting spatial distribution information from the reconstruction result, the location and volume of the fluorescent target can be obtained accurately. Both simulation and phantom experiments are carried out and the results indicate that, by using the modified forward model, the quality of reconstruction could be significantly improved in terms of accurate localization and strong anti-noise ability.

Subsurface fluorescence molecular tomography with prior information

Subsurface fluorescence molecular tomography (FMT) is an emerging technique determining fluorescence distribution by tomographic means in reflectance geometry. However, due to the highly diffusive nature of the photon propagation in biological tissues and the influence of nearer source-detector separations, stand-alone subsurface FMT could not accurately reflect the fluorophore distributions. To overcome this drawback, we propose a method to improve the performance of fluorescence imaging by coupling x-ray computed tomography (XCT) and subsurface FMT modalities. A Laplacian-type regularization matrix generated with tissue prior information obtained from XCT images is used to guide the reconstruction of fluorophore distribution. Reconstruction results of both simulation and phantom studies showed that significant improvements in localization and demarcation of fluorescent targets can be obtained with the proposed method compared to the reconstruction method without structural prior information.