Guanglei Zhang

Imaging of pharmacokinetic rates of indocyanine green in mouse liver with a hybrid fluorescence molecular tomography/x-ray computed tomography system

Abstract. Pharmacokinetic rates have the potential to provide quantitative physiological and pathological information for biological studies and drug development. Fluorescence molecular tomography (FMT) is an attractive imaging tool for three-dimensionally resolving fluorophore distribution in small animals. In this letter, pharmacokinetic rates of indocyanine green (ICG) in mouse liver are imaged with a hybrid FMT and x-ray computed tomography (XCT) system. A recently developed FMT method using structural priors from an XCT system is adopted to improve the quality of FMT reconstruction. In the in vivo experiments, images of uptake and excretion rates of ICG in mouse liver are obtained, which can be used to quantitatively evaluate liver function. The accuracy of the results is validated by a fiber-based fluorescence measurement system.

Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm

Abstract. Owing to the high degree of scattering of light through tissues, the ill-posedness of fluorescence molecular tomography (FMT) inverse problem causes relatively low spatial resolution in the reconstruction results. Unlike L2 regularization, L1 regularization can preserve the details and reduce the noise effectively. Reconstruction is obtained through a restarted L1 regularization-based nonlinear conjugate gradient (re-L1-NCG) algorithm, which has been proven to be able to increase the computational speed with low memory consumption. The algorithm consists of inner and outer iterations. In the inner iteration, L1-NCG is used to obtain the L1-regularized results. In the outer iteration, the restarted strategy is used to increase the convergence speed of L1-NCG. To demonstrate the performance of re-L1-NCG in terms of spatial resolution, simulation and physical phantom studies with fluorescent targets located with different edge-to-edge distances were carried out. The reconstruction results show that the re-L1-NCG algorithm has the ability to resolve targets with an edge-to-edge distance of 0.1 cm at a depth of 1.5 cm, which is a significant improvement for FMT.

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.

MAP estimation with structural priors for fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) is an attractive imaging tool for quantitatively and three-dimensionally resolving fluorophore distributions in small animals, but it suffers from low spatial resolution due to its inherent ill-posed nature. Structural priors obtained from a secondary modality system such as x-ray computed tomography or magnetic resonance imaging can help to improve FMT reconstruction results. However, challenge remains in how to fully take advantage of the structural priors while effectively avoid undesirable influence caused by an immoderate usage. In this paper, we propose a new method to resolve the FMT inverse problem based on maximum a posteriori (MAP) estimation with structural priors (MAP-SP) in a Bayesian framework. Instead of imposing the structural priors directly on the reconstruction results, the MAP-SP method utilizes them to constrain the unknown hyperparameters of the prior information model which is essential for the Bayesian framework. Then, a low dimensional inverse problem and an alternating optimization scheme are used to automatically calculate the unknown hyperparameters, which make the FMT reconstruction process self-adaptive. Simulation and phantom results show that the proposed MAP-SP method can effectively make use of the structural priors and leads to improvements in reconstruction quality as compared with traditional regularization methods.

Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian Method

X-ray luminescence computed tomography (XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. Combining the principles of X-ray excitation of luminescence-based probes and optical signal detection, XLCT naturally fuses functional and anatomical images and provides complementary information for a wide range of applications in biomedical research. In order to improve the data acquisition efficiency of previously developed narrow-beam XLCT, a cone beam XLCT (CB-XLCT) mode is adopted here to take advantage of the useful geometric features of cone beam excitation. Practically, a major hurdle in using cone beam X-ray for XLCT is that the inverse problem here is seriously ill-conditioned, hindering us to achieve good image quality. In this paper, we propose a novel Bayesian method to tackle the bottleneck in CB-XLCT reconstruction. The method utilizes a local regularization strategy based on Gaussian Markov random field to mitigate the ill-conditioness of CB-XLCT. An alternating optimization scheme is then used to automatically calculate all the unknown hyperparameters while an iterative coordinate descent algorithm is adopted to reconstruct the image with a voxel-based closed-form solution. Results of numerical simulations and mouse experiments show that the self-adaptive Bayesian method significantly improves the CB-XLCT image quality as compared with conventional methods.

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.

Reconstruction of Fluorophore Concentration Variation in Dynamic Fluorescence Molecular Tomography

Dynamic fluorescence molecular tomography (DFMT) is a potential approach for drug delivery, tumor detection, diagnosis, and staging. The purpose of DFMT is to quantify the changes of fluorescent agents in the bodies, which offer important information about the underlying physiological processes. However, the conventional method requires that the fluorophore concentrations to be reconstructed are stationary during the data collection period. As thus, it cannot offer the dynamic information of fluorophore concentration variation within the data collection period. In this paper, a method is proposed to reconstruct the fluorophore concentration variation instead of the fluorophore concentration through a linear approximation. The fluorophore concentration variation rate is introduced by the linear approximation as a new unknown term to be reconstructed and is used to obtain the time courses of fluorophore concentration. Simulation and phantom studies are performed to validate the proposed method. The results show that the method is able to reconstruct the fluorophore concentration variation rates and the time courses of fluorophore concentration with relative errors less than 0.0218.

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.

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.

Synthesis, Characterization, and Biomedical Applications of a Targeted Dual-Modal Near-Infrared-II Fluorescence and Photoacoustic Imaging Nanoprobe

Our development of multifunctional dual-modal imaging probes aims to integrate the benefits from both second near-infrared (NIR-II) fluorescence (1000-1700 nm) and photoacoustic imaging with an ultimate goal of improving overall cancer diagnosis efficacy. Herein we designed a donor-acceptor chromophore based nanoparticle (DAP) as a dual-modal image contrast agent has strong absorption in the NIR-I window and a strong fluorescence emission peak in the NIR-II region. The dual-modal DAPs composed of D-π-A-π-D-type chromophores were PEGylated through nanoprecipitation. The multifunctional DAP surface was thus available for subsequent bioconjugation of EGFR Affibody (Ac-Cys-ZEGFR:1907) to target EGFR-positive cancers. The Affibody-conjugated DAPs appeared as highly monodisperse nanoparticles (∼30 nm) with strong absorption in the NIR-I window (at ca. 680 nm) and an extremely high fluorescence in the NIR-II region (maximum peak at 1000 nm). Consequently, the Affibody-DAPs show significantly enhanced photoacoustic and NIR-II fluorescence contrast effects in both in vitro and in vivo experiments. Moreover, the Affibody-DAPs have the capability to selectively target EGFR-positive tumors in an FTC-133 subcutaneous mouse model with relatively high photoacoustic and fluorescent signals. By taking advantage of high spatial resolution and excellent temporal resolution, photoacoustic/NIR-II fluorescence imaging with targeted dual-modal contrast agents allows us to specifically image and detect various cancers and diseases in an accurate manner.