Fluorescence separation based on the spatiotemporal Gaussian mixture model for dynamic fluorescence molecular tomography

Dynamic fluorescence molecular tomography (DFMT) is a promising imaging method that can furnish three-dimensional information regarding the absorption, distribution, and excretion of fluorescent probes in organisms. Achieving precise dynamic fluorescence images is the linchpin for realizing high-resolution, high-sensitivity, and high-precision tomography. Traditional preprocessing methods for dynamic fluorescence images often face challenges due to the non-specificity of fluorescent probes in living organisms, requiring complex imaging systems or biological interventions. These methods can result in significant processing errors, negatively impacting the imaging accuracy of DFMT. In this study, we present, a novel, to the best of our knowledge, strategy based on the spatiotemporal Gaussian mixture model (STGMM) for the processing of dynamic fluorescence images. The STGMM is primarily divided into four components: dataset construction, time domain prior information, spatial Gaussian fitting with time prior, and fluorescence separation. Numerical simulations and in vivo experimental results demonstrate that our proposed method significantly enhances image processing speed and accuracy compared to existing methods, especially when faced with fluorescence interference from other organs. Our research contributes to substantial reductions in time and processing complexity, providing robust support for dynamic imaging applications.

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.

Indirect and direct estimation of pharmacokinetic parameters in dynamic diffuse fluorescence tomography by adaptive extended Kalman filtering

Pharmacokinetic parameter estimation with the support of dynamic diffuse fluorescence tomography (DFT) can provide helpful diagnostic information for tumor differentiation and monitoring. Adaptive extended Kalman filtering (AEKF) as a nonlinear filter method has the merits of high quantitativeness, noise robustness, and initialization independence. In this paper, indirect and direct AEKF schemes combining with a commonly used two-compartment model were studied to estimate the pharmacokinetic parameters based on our self-designed dynamic DFT system. To comprehensively compare the performances of both schemes, the selection of optimal noise covariance matrices affecting estimation results was first studied, then a series of numerical simulations with the metabolic time ranged from 4.16 min to 38 min was carried out and quantitatively evaluated. The comparison results show that the direct AEKF outperforms the indirect EKF in estimation accuracy at different metabolic velocity and demonstrates stronger stability at the large metabolic velocity. Furtherly, the in vivo experiment was conducted to achieve the indocyanine green pharmacokinetic-rate images in the mouse liver. The experimental results confirmed the capability of both schemes to estimate the pharmacokinetic-rate images and were in agreement with the theory predictions and the numerical simulation results.

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

SIGNIFICANCE

Pharmacokinetic 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.

AIM

In 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.

APPROACH

First, 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.

RESULTS

This 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.

CONCLUSIONS

Compared 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.

A review of advances in imaging methodology in fluorescence molecular tomography

Objective. Fluorescence molecular tomography (FMT) is a promising non-invasive optical molecular imaging technology with strong specificity and sensitivity that has great potential for preclinical and clinical studies in tumor diagnosis, drug development and therapeutic evaluation. However, the strong scattering of photons and insufficient surface measurements make it very challenging to improve the quality of FMT image reconstruction and its practical application for early tumor detection. Therefore, continuous efforts have been made to explore more effective approaches or solutions in the pursuit of high-quality FMT reconstructions. Approach. This review takes a comprehensive overview of advances in imaging methodology for FMT, mainly focusing on two critical issues in FMT reconstructions: improving the accuracy of solving the forward physical model and mitigating the ill-posed nature of the inverse problem from a methodological point of view. More importantly, numerous impressive and practical strategies and methods for improving the quality of FMT reconstruction are summarized. Notably, deep learning methods are discussed in detail to illustrate their advantages in promoting the imaging performance of FMT thanks to large datasets, the emergence of optimized algorithms and the application of innovative networks. Main results. The results demonstrate that the imaging quality of FMT can be effectively promoted by improving the accuracy of optical parameter modeling, combined with prior knowledge, and reducing dimensionality. In addition, the traditional regularization-based methods and deep neural network-based methods, especially end-to-end deep networks, can enormously alleviate the ill-posedness of the inverse problem and improve the quality of FMT image reconstruction. Significance. This review aims to illustrate a variety of effective and practical methods for the reconstruction of FMT images that may benefit future research. Furthermore, it may provide some valuable research ideas and directions for FMT in the future, and could promote, to a certain extent, the development of FMT and other methods of optical tomography.

Review of in vivo optical molecular imaging and sensing from x-ray excitation

SIGNIFICANCE

Deep-tissue penetration by x-rays to induce optical responses of specific molecular reporters is a new way to sense and image features of tissue function in vivo. Advances in this field are emerging, as biocompatible probes are invented along with innovations in how to optimally utilize x-ray sources.

AIM

A comprehensive review is provided of the many tools and techniques developed for x-ray-induced optical molecular sensing, covering topics ranging from foundations of x-ray fluorescence imaging and x-ray tomography to the adaptation of these methods for sensing and imaging in vivo.

APPROACH

The ways in which x-rays can interact with molecules and lead to their optical luminescence are reviewed, including temporal methods based on gated acquisition and multipoint scanning for improved lateral or axial resolution.

RESULTS

While some known probes can generate light upon x-ray scintillation, there has been an emergent recognition that excitation of molecular probes by x-ray-induced Cherenkov light is also possible. Emission of Cherenkov radiation requires a threshold energy of x-rays in the high kV or MV range, but has the advantage of being able to excite a broad range of optical molecular probes. In comparison, most scintillating agents are more readily activated by lower keV x-ray energies but are composed of crystalline inorganic constituents, although some organic biocompatible agents have been designed as well. Methods to create high-resolution structured x-ray-optical images are now available, based upon unique scanning approaches and/or a priori knowledge of the scanned x-ray beam geometry. Further improvements in spatial resolution can be achieved by careful system design and algorithm optimization. Current applications of these hybrid x-ray-optical approaches include imaging of tissue oxygenation and pH as well as of certain fluorescent proteins.

CONCLUSIONS

Discovery of x-ray-excited reporters combined with optimized x-ray scan sequences can improve imaging resolution and sensitivity.

Parameterized level-set based pharmacokinetic fluorescence optical tomography using the regularized Gauss-Newton filter

Pharmacokinetic tomography is emerging as an important methodology for detecting abnormalities in tissue based upon spatially varying estimation of the pharmacokinetic rates governing the leakage of an injected fluorophore between blood plasma and tissue. We present a shape-based reconstruction framework of a compartment-model based formulation of this dynamic fluorescent optical tomography problem to solve for the pharmacokinetic rates and concentrations of the fluorophore from time-varying log intensity measurements of the optical signal. The compartment-model based state variable model is set up in a radial basis function parameterized level set setting. The state (concentrations) and (pharmacokinetic) parameter estimation problem is solved with an iteratively regularized Gauss-Newton filter in a trust-region framework. Reconstructions obtained using this scheme for noisy data obtained from cancer mimicking numerical phantoms of near/sub-cm sizes show a good localization of the affected regions and reasonable estimates of the pharmacokinetic rates and concentration curves.

Enhancing in vivo renal ischemia assessment by high-dynamic-range fluorescence molecular imaging

Fluorescence imaging has been used to evaluate the physiological features of renal ischemia in animal model. However, the fluorophore distribution details of the ischemia model could not be fully represented due to the limited dynamic range of the charged-couple device. A high-dynamic-range (HDR) strategy was adopted in renal ischemia fluorescence imaging, both ex vivo and in vivo. The HDR strategy successfully combined ischemia relevant biological features that could only be captured with different exposure times, and then presented these features in the HDR results. The HDR results effectively highlighted the renal ischemic areas with relatively better perfusion and diminished the saturation that resulted from long exposure time. The relative fluorescence intensities of the ischemic kidneys and the image entropy values were significantly higher in the HDR images than in the original images, therefore enhancing the visualization of the renal ischemia model. The results suggest that HDR could serve as a postprocessing strategy to enhance the assessment of in vivo renal ischemia, and HDR fluorescence molecular imaging could be a valuable imaging tool for future studies of clinical ischemia detection and evaluation.

Image Restoration for Fluorescence Planar Imaging with Diffusion Model

Fluorescence planar imaging (FPI) is failure to capture high resolution images of deep fluorochromes due to photon diffusion. This paper presents an image restoration method to deal with this kind of blurring. The scheme of this method is conceived based on a reconstruction method in fluorescence molecular tomography (FMT) with diffusion model. A new unknown parameter is defined through introducing the first mean value theorem for definite integrals. System matrix converting this unknown parameter to the blurry image is constructed with the elements of depth conversion matrices related to a chosen plane named focal plane. Results of phantom and mouse experiments show that the proposed method is capable of reducing the blurring of FPI image caused by photon diffusion when the depth of focal plane is chosen within a proper interval around the true depth of fluorochrome. This method will be helpful to the estimation of the size of deep fluorochrome.

Self-prior strategy for organ reconstruction in fluorescence molecular tomography

The purpose of this study is to propose a strategy for organ reconstruction in fluorescence molecular tomography (FMT) without prior information from other imaging modalities, and to overcome the high cost and ionizing radiation caused by the traditional structural prior strategy. The proposed strategy is designed as an iterative architecture to solve the inverse problem of FMT. In each iteration, a short time Fourier transform (STFT) based algorithm is used to extract the self-prior information in the space-frequency energy spectrum with the assumption that the regions with higher fluorescence concentration have larger energy intensity, then the cost function of the inverse problem is modified by the self-prior information, and lastly an iterative Laplacian regularization algorithm is conducted to solve the updated inverse problem and obtains the reconstruction results. Simulations and in vivo experiments on liver reconstruction are carried out to test the performance of the self-prior strategy on organ reconstruction. The organ reconstruction results obtained by the proposed self-prior strategy are closer to the ground truth than those obtained by the iterative Tikhonov regularization (ITKR) method (traditional non-prior strategy). Significant improvements are shown in the evaluation indexes of relative locational error (RLE), relative error (RE) and contrast-to-noise ratio (CNR). The self-prior strategy improves the organ reconstruction results compared with the non-prior strategy and also overcomes the shortcomings of the traditional structural prior strategy. Various applications such as metabolic imaging and pharmacokinetic study can be aided by this strategy.

Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.

Effects of temperature on multiparametric evaluation of hindlimb ischemia with dynamic fluorescence imaging

Quantitative evaluation of hindlimb ischemia is essential for early diagnosis and therapy of peripheral arterial disease (PAD). Dynamic imaging using near-infrared (NIR) fluorophore indocyanine green (ICG) is a noninvasive and effective tool to monitor multiple vascular parameters including perfusion rate (PR), perfusion vascular density (PVD) and hemodynamics. It has been previously demonstrated that temperature changes could lead to significant variations of blood flow rate and vascular perfusion. In this paper, multiparametric evaluation of hindlimb ischemia was performed at different temperatures. Five different parameters were extracted from dynamic fluorescence imaging, including PR, PVD, rising time (T_rise ), blood flow index (BFI) and mean fluorescence intensity (MFI). Temperatures varied from 15 °C to 40 °C were set on a mouse model of hindlimb ischemia. The aforementioned five parameters were obtained at each temperature. The results suggest that PVD, BFI and MFI could be effective indicators to distinguish ischemic tissues from normal tissues in mouse hindlimb at different temperatures. In contrast, PR is effective only when the temperature is higher than 25 °C, while T_rise is effective only when the temperature is lower than 35 °C. The parameters of PVD, BFI and MFI could provide quantitative and comprehensive evaluation for PAD at different temperatures. (A) Bright-field image of the normal (left) and ischemic (right) hindlimbs. (B-D) Parametric images of perfusion vascular density, blood flow index and mean fluorescence intensity.

Multiparametric evaluation of hindlimb ischemia using time-series indocyanine green fluorescence imaging

Peripheral arterial disease (PAD) can further cause lower limb ischemia. Quantitative evaluation of the vascular perfusion in the ischemic limb contributes to diagnosis of PAD and preclinical development of new drug. In vivo time-series indocyanine green (ICG) fluorescence imaging can noninvasively monitor blood flow and has a deep tissue penetration. The perfusion rate estimated from the time-series ICG images is not enough for the evaluation of hindlimb ischemia. The information relevant to the vascular density is also important, because angiogenesis is an essential mechanism for post-ischemic recovery. In this paper, a multiparametric evaluation method is proposed for simultaneous estimation of multiple vascular perfusion parameters, including not only the perfusion rate but also the vascular perfusion density and the time-varying ICG concentration in veins. The target method is based on a mathematical model of ICG pharmacokinetics in the mouse hindlimb. The regression analysis performed on the time-series ICG images obtained from a dynamic reflectance fluorescence imaging system. The results demonstrate that the estimated multiple parameters are effective to quantitatively evaluate the vascular perfusion and distinguish hypo-perfused tissues from well-perfused tissues in the mouse hindlimb. The proposed multiparametric evaluation method could be useful for PAD diagnosis. The estimated perfusion rate and vascular perfusion density maps (left) and the time-varying ICG concentration in veins of the ankle region (right) of the normal and ischemic hindlimbs.

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.

Region-based diffuse optical tomography with registered atlas: in vivo acquisition of mouse optical properties

The reconstruction quality in the model-based optical tomography modalities can greatly benefit from a priori information of accurate tissue optical properties, which are difficult to be obtained in vivo with a conventional diffuse optical tomography (DOT) system alone. One of the solutions is to apply a priori anatomical structures obtained with anatomical imaging systems such as X-ray computed tomography (XCT) to constrain the reconstruction process of DOT. However, since X-ray offers low soft-tissue contrast, segmentation of abdominal organs from sole XCT images can be problematic. In order to overcome the challenges, the current study proposes a novel method of recovering a priori organ-oriented tissue optical properties, where anatomical structures of an in vivo mouse are approximately obtained by registering a standard anatomical atlas, i.e., the Digimouse, to the target XCT volume with the non-rigid image registration, and, in turn, employed to guide DOT for extracting the optical properties of inner organs. Simulative investigations have validated the methodological availability of such atlas-registration-based DOT strategy in revealing both a priori anatomical structures and optical properties. Further experiments have demonstrated the feasibility of the proposed method for acquiring the organ-oriented tissue optical properties of in vivo mice, making it as an efficient way of the reconstruction enhancement.

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.

Development of computational small animal models and their applications in preclinical imaging and therapy research

The development of multimodality preclinical imaging techniques and the rapid growth of realistic computer simulation tools have promoted the construction and application of computational laboratory animal models in preclinical research. Since the early 1990s, over 120 realistic computational animal models have been reported in the literature and used as surrogates to characterize the anatomy of actual animals for the simulation of preclinical studies involving the use of bioluminescence tomography, fluorescence molecular tomography, positron emission tomography, single-photon emission computed tomography, microcomputed tomography, magnetic resonance imaging, and optical imaging. Other applications include electromagnetic field simulation, ionizing and nonionizing radiation dosimetry, and the development and evaluation of new methodologies for multimodality image coregistration, segmentation, and reconstruction of small animal images. This paper provides a comprehensive review of the history and fundamental technologies used for the development of computational small animal models with a particular focus on their application in preclinical imaging as well as nonionizing and ionizing radiation dosimetry calculations. An overview of the overall process involved in the design of these models, including the fundamental elements used for the construction of different types of computational models, the identification of original anatomical data, the simulation tools used for solving various computational problems, and the applications of computational animal models in preclinical research. The authors also analyze the characteristics of categories of computational models (stylized, voxel-based, and boundary representation) and discuss the technical challenges faced at the present time as well as research needs in the future.

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.

Direct reconstruction in CT-analogous pharmacokinetic diffuse fluorescence tomography: two-dimensional simulative and experimental validations

We present a generalized strategy for direct reconstruction in pharmacokinetic diffuse fluorescence tomography (DFT) with CT-analogous scanning mode, which can accomplish one-step reconstruction of the indocyanine-green pharmacokinetic-rate images within in vivo small animals by incorporating the compartmental kinetic model into an adaptive extended Kalman filtering scheme and using an instantaneous sampling dataset. This scheme, compared with the established indirect and direct methods, eliminates the interim error of the DFT inversion and relaxes the expensive requirement of the instrument for obtaining highly time-resolved date-sets of complete 360 deg projections. The scheme is validated by two-dimensional simulations for the two-compartment model and pilot phantom experiments for the one-compartment model, suggesting that the proposed method can estimate the compartmental concentrations and the pharmacokinetic-rates simultaneously with a fair quantitative and localization accuracy, and is well suitable for cost-effective and dense-sampling instrumentation based on the highly-sensitive photon counting technique.

Reconstruction of in vivo fluorophore concentration variation with structural priors and smooth penalty

Reconstruction of fluorophore concentration variation in fluorescence molecular tomography is expected to reveal the metabolic processes of fluorescent biomarkers in vivo. However, the complicated and strong noise within in vivo data inhibits its applications for in vivo cases. A smooth penalty method is presented in this work to suppress the noise. The method is based on a recursive reconstruction scheme which reconstructs the fluorophore concentration variation rates (FCVRs) of two neighboring frames at the same time within an inner iteration. In addition, the performance of the Laplacian-type regularization incorporating structural priors is investigated. Results of simulations suggest that the smooth penalty method almost has no influence on the reconstructed FCVRs when the target curve is smooth, and results of in vivo experiments on mice indicate that the method is capable of suppressing the noise and achieving smooth time courses of fluorescent yield. Results of both the simulations and in vivo experiments demonstrate that the Laplacian-type regularization can improve the image quality.

Shape-based reconstruction of dynamic fluorescent yield with a level set method

Background

Fluorescence molecular tomography (FMT) is an optical imaging technique that reveals biological processes within small animals through non-invasively reconstructing the distributions of fluorescent agents. The primary problem in FMT with non-stationary fluorescent yield is the increase of the unknown parameters to be reconstructed. In this paper, a method is proposed to reconstruct dynamic fluorescent yield.

Methods

A shape-based reconstruction method that recovers dynamic fluorescent yield with a level set method is proposed for FMT. To reduce the number of unknown parameters, a level set function is introduced to describe the shape of target and a small number of parameters are used to describe the fluorescent yields at different time points.

Results

Results of simulations and phantom experiments demonstrate that the proposed method can recover well the dynamic fluorescent yields, shapes and locations of the target.

Conclusions

The proposed method can handle the cases with non-stationary fluorescent yields and recover the fluorescent yields at each projection angle.

Advances in optical imaging for pharmacological studies

Imaging approaches are an essential tool for following up over time representative parameters of in vivo models, providing useful information in pharmacological studies. Main advantages of optical imaging approaches compared to other imaging methods are their safety, straight-forward use and cost-effectiveness. A main drawback, however, is having to deal with the presence of high scattering and high absorption in living tissues. Depending on how these issues are addressed, three different modalities can be differentiated: planar imaging (including fluorescence and bioluminescence in vivo imaging), optical tomography, and optoacoustic approaches. In this review we describe the latest advances in optical in vivo imaging with pharmacological applications, with special focus on the development of new optical imaging probes in order to overcome the strong absorption introduced by different tissue components, especially hemoglobin, and the development of multimodal imaging systems in order to overcome the resolution limitations imposed by scattering.

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.

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.

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.