Dynamic Fluorescence Molecular Tomography Research Articles

Group sparse-based Taylor expansion method for liver pharmacokinetic parameters imaging of dynamic fluorescence molecular tomography

Objective. Pharmacokinetic parametric images obtained through dynamic fluorescence molecular tomography (DFMT) has ability of capturing dynamic changes in fluorescence concentration, thereby providing three-dimensional metabolic information for applications in biological research and drug development. However, data processing of DFMT is time-consuming, involves a vast amount of data, and the problem itself is ill-posed, which significantly limits the application of pharmacokinetic parametric images reconstruction. In this study, group sparse-based Taylor expansion method is proposed to address these problems. Approach. Firstly, Taylor expansion framework is introduced to reduce time and computational cost. Secondly, group sparsity based on structural prior is introduced to improve reconstruction accuracy. Thirdly, alternating iterative solution based on accelerated gradient descent algorithm is introduced to solve the problem. Main results. Numerical simulation and in vivo experimental results demonstrate that, in comparison to existing methods, the proposed approach significantly enhances reconstruction speed without a degradation of quality, particularly when confronted with background fluorescence interference from other organs. Significance. Our research greatly reduces time and computational cost, providing strong support for real-time monitoring of liver metabolism.

Dynamic fluorescence molecular tomography metabolic parameters solution based on problem decomposition and prior refactor.

Dynamic fluorescence molecular tomography (DFMT), as a noninvasive optical imaging method, can quantify metabolic parameters of living animal organs and assist in the diagnosis of metabolic diseases. However, existing DFMT methods do not have a high capacity to reconstruct abnormal metabolic regions, and require additional prior information and complicated solution methods. This paper introduces a problem decomposition and prior refactor (PDPR) method. The PDPR decomposes the metabolic parameters into two kinds of problems depending on their temporal coupling, which are solved using regularization and parameter fitting. Moreover, PDPR introduces the idea of divide-and-conquer to refactor prior information to ensure discrimination between metabolic abnormal regions and normal tissues. Experimental results show that PDPR is capable of separating abnormal metabolic regions of the liver and has the potential to quantify metabolic parameters and diagnose liver metabolic diseases in small animals.

Liver injury monitoring using dynamic fluorescence molecular tomography based on a time-energy difference strategy.

Dynamic fluorescence molecular tomography (DFMT) is a promising molecular imaging technique that offers the potential to monitor fast kinetic behaviors within small animals in three dimensions. Early monitoring of liver disease requires the ability to distinguish and analyze normal and injured liver tissues. However, the inherent ill-posed nature of the problem and energy signal interference between the normal and injured liver regions limit the practical application of liver injury monitoring. In this study, we propose a novel strategy based on time and energy, leveraging the temporal correlation in fluorescence molecular imaging (FMI) sequences and the metabolic differences between normal and injured liver tissue. Additionally, considering fluorescence signal distribution disparity between the injured and normal regions, we designed a universal Golden Ratio Primal-Dual Algorithm (GRPDA) to reconstruct both the normal and injured liver regions. Numerical simulation and in vivo experiment results demonstrate that the proposed strategy can effectively avoid signal interference between liver and liver injury energy and lead to significant improvements in morphology recovery and positioning accuracy compared to existing approaches. Our research presents a new perspective on distinguishing normal and injured liver tissues for early liver injury monitoring.

In vivo accurate detection of the liver tumor with pharmacokinetic parametric images from dynamic fluorescence molecular tomography.

.SignificancePharmacokinetic parametric images in dynamic fluorescence molecular tomography (FMT) can describe three-dimensional (3D) physiological and pathological information inside biological tissues, potentially providing quantitative assessment tools for biological research and drug development.AimIn vivo imaging of the liver tumor with pharmacokinetic parametric images from dynamic FMT based on the differences in metabolic properties of indocyanine green (ICG) between normal liver cells and tumor liver cells inside biological tissues.ApproachFirst, an orthotopic liver tumor mouse model was constructed. Then, with the help of the FMT/computer tomography (CT) dual-modality imaging system and the direct reconstruction algorithm, 3D imaging of liver metabolic parameters in nude mice was achieved to distinguish liver tumors from normal tissues. Finally, pharmacokinetic parametric imaging results were validated against in vitro anatomical results.ResultsThis letter demonstrates the ability of dynamic FMT to monitor the pharmacokinetic delivery of the fluorescent dye ICG in vivo, thus, enabling the distinction between normal and tumor tissues based on the pharmacokinetic parametric images derived from dynamic FMT.ConclusionsCompared with CT structural imaging technology, dynamic FMT combined with compartmental modeling as an analytical method can obtain quantitative images of pharmacokinetic parameters, thus providing a more powerful research tool for organ function assessment, disease diagnosis and new drug development.

Excitation-resolved multispectral method for imaging pharmacokinetic parameters in dynamic fluorescent molecular tomography.

Imaging of the pharmacokinetic parameters in dynamic fluorescence molecular tomography (DFMT) can provide three-dimensional metabolic information for biological studies and drug development. However, owing to the ill-posed nature of the FMT inverse problem, the relatively low quality of the parametric images makes it difficult to investigate the different metabolic processes of the fluorescent targets with small distances. An excitation-resolved multispectral DFMT method is proposed; it is based on the fact that the fluorescent targets with different concentrations show different variations in the excitation spectral domain and can be considered independent signal sources. With an independent component analysis method, the spatial locations of different fluorescent targets can be decomposed, and the fluorescent yields of the targets at different time points can be recovered. Therefore, the metabolic process of each component can be independently investigated. Simulations and phantom experiments are carried out to evaluate the performance of the proposed method. The results demonstrated that the proposed excitation-resolved multispectral method can effectively improve the reconstruction accuracy of the parametric images in DFMT.

Automatic selection of regularization parameters for dynamic fluorescence molecular tomography: a comparison of L-curve and U-curve methods.

Dynamic fluorescence molecular tomography (FMT) is a promising technique for the study of the metabolic process of fluorescent agents in the biological body in vivo, and the quality of the parametric images relies heavily on the accuracy of the reconstructed FMT images. In typical dynamic FMT implementations, the imaged object is continuously monitored for more than 50 minutes. During each minute, a set of the fluorescent measurements is acquired and the corresponding FMT image is reconstructed. It is difficult to manually set the regularization parameter in the reconstruction of each FMT image. In this paper, the parametric images obtained with the L-curve and U-curve methods are quantitatively evaluated through numerical simulations, phantom experiments and in vivo experiments. The results illustrate that the U-curve method obtains better accuracy, stronger robustness and higher noise-resistance in parametric imaging. Therefore, it is a promising approach to automatic selection of the regularization parameters for dynamic FMT.

Non-stationary reconstruction for dynamic fluorescence molecular tomography with extended kalman filter.

Dynamic fluorescence molecular tomography (FMT) plays an important role in drug delivery research. However, the majority of current reconstruction methods focus on solving the stationary FMT problems. If the stationary reconstruction methods are applied to the time-varying fluorescence measurements, the reconstructed results may suffer from a high level of artifacts. In addition, based on the stationary methods, only one tomographic image can be obtained after scanning one circle projection data. As a result, the movement of fluorophore in imaged object may not be detected due to the relative long data acquisition time (typically >1 min). In this paper, we apply extended kalman filter (EKF) technique to solve the non-stationary fluorescence tomography problem. Especially, to improve the EKF reconstruction performance, the generalized inverse of kalman gain is calculated by a second-order iterative method. The numerical simulation, phantom, and in vivo experiments are performed to evaluate the performance of the method. The experimental results indicate that by using the proposed EKF-based second-order iterative (EKF-SOI) method, we cannot only clearly resolve the time-varying distributions of fluorophore within imaged object, but also greatly improve the reconstruction time resolution (~2.5 sec/frame) which makes it possible to detect the movement of fluorophore during the imaging processes.

Fast direct reconstruction strategy of dynamic fluorescence molecular tomography using graphics processing units.

Dynamic fluorescence molecular tomography (DFMT) is a valuable method to evaluate the metabolic process of contrast agents in different organs in vivo, and direct reconstruction methods can improve the temporal resolution of DFMT. However, challenges still remain due to the large time consumption of the direct reconstruction methods. An acceleration strategy using graphics processing units (GPU) is presented. The procedure of conjugate gradient optimization in the direct reconstruction method is programmed using the compute unified device architecture and then accelerated on GPU. Numerical simulations and in vivo experiments are performed to validate the feasibility of the strategy. The results demonstrate that, compared with the traditional method, the proposed strategy can reduce the time consumption by ∼90% without a degradation of quality.

Fast reconstruction of fluorophore concentration variation based on the derivation of the diffusion equation.

The information of fluorophore concentration variation (FCV) has the potential for drug development and tumor studies, but the reconstruction of FCV is time-consuming in dynamic fluorescence molecular tomography (DFMT). In this paper, a time-efficient reconstruction method for FCV is presented. The system equation of this method is derived from the derivation of the diffusion equation, and its size does not change with the number of frames. The computational time can be significantly reduced by using this method because the images of different frames are reconstructed separately. Simulations and phantom experiments are performed to validate the performance of the proposed method. The results show that compared with the previous method, the proposed method can obtain better results and consumes less computational time with the same number of iterations. In addition, the time consumption in a single iteration of the proposed method increases much slower with the number of frames.

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.

4-D reconstruction of fluorescence molecular tomography using re-assembled measurement data.

Challenges remain in the reconstruction of dynamic (4-D) fluorescence molecular tomography (FMT). In our previous work, we implemented a fully 4-D FMT reconstruction approach using Karhunen-Loève (KL) transformation. However, in the reconstruction processes, the input data were scan-by-scan fluorescence projections. As a result, the reconstruction interval is limited by the data acquisition time for scanning one circle projections, leading to a long time (typically >1 min). In this paper, we propose a new method to reduce the reconstruction interval of dynamic FMT imaging, which is achieved by re-assembling the acquired fluorescence projection sequence. Further, to eliminate the temporal correlations within measurement data, the re-assembled projection sequence is reconstructed by the KL-based method. The numerical simulation and in vivo experiments are performed to evaluate the performance of the method. The experimental results indicate that after re-assembling measurement data, the reconstruction interval can be greatly reduced (~2.5 sec/frame). In addition, the proposed re-assembling method is helpful for improving reconstruction quality of the KL-based 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.

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.

Temporal Unmixing of Dynamic Fluorescent Images by Blind Source Separation Method with a Convex Framework.

By recording a time series of tomographic images, dynamic fluorescence molecular tomography (FMT) allows exploring perfusion, biodistribution, and pharmacokinetics of labeled substances in vivo. Usually, dynamic tomographic images are first reconstructed frame by frame, and then unmixing based on principle component analysis (PCA) or independent component analysis (ICA) is performed to detect and visualize functional structures with different kinetic patterns. PCA and ICA assume sources are statistically uncorrelated or independent and don't perform well when correlated sources are present. In this paper, we deduce the relationship between the measured imaging data and the kinetic patterns and present a temporal unmixing approach, which is based on nonnegative blind source separation (BSS) method with a convex analysis framework to separate the measured data. The presented method requires no assumption on source independence or zero correlations. Several numerical simulations and phantom experiments are conducted to investigate the performance of the proposed temporal unmixing method. The results indicate that it is feasible to unmix the measured data before the tomographic reconstruction and the BSS based method provides better unmixing quality compared with PCA and ICA.

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.

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.

Quantitative Whole Body Biodistribution of Fluorescent-Labeled Agents by Non-Invasive Tomographic Imaging

When small molecules or proteins are injected into live animals, their physical and chemical properties will significantly affect pharmacokinetics, tissue penetration, and the ultimate routes of metabolism and clearance. Fluorescence molecular tomography (FMT) offers the ability to non-invasively image and quantify temporal changes in fluorescence throughout the major organ systems of living animals, in a manner analogous to traditional approaches with radiolabeled agents. This approach is best used with biotherapeutics (therapeutic antibodies, or other large proteins) or large-scaffold drug-delivery vectors, that are minimally affected by low-level fluorophore conjugation. Application to small molecule drugs should take into account the significant impact of fluorophore labeling on size and physicochemical properties, however, the presents studies show that this technique is readily applied to small molecule agents developed for far-red (FR) or near infrared (NIR) imaging. Quantification by non-invasive FMT correlated well with both fluorescence from tissue homogenates as well as with planar (2D) fluorescence reflectance imaging of excised intact organs (r2 = 0.996 and 0.969, respectively). Dynamic FMT imaging (multiple times from 0 to 24 h) performed in live mice after the injection of four different FR/NIR-labeled agents, including immunoglobulin, 20–50 nm nanoparticles, a large vascular imaging agent, and a small molecule integrin antagonist, showed clear differences in the percentage of injected dose per gram of tissue (%ID/g) in liver, kidney, and bladder signal. Nanoparticles and IgG1 favored liver over kidney signal, the small molecule integrin-binding agent favored rapid kidney and bladder clearance, and the vascular agent, showed both liver and kidney clearance. Further assessment of the volume of distribution of these agents by fluorescent volume added information regarding their biodistribution and highlighted the relatively poor extravasation into tissue by IgG1. These studies demonstrate the ability of quantitative FMT imaging of FR/NIR agents to non-invasively visualize and quantify the biodistribution of different agents over time.