Dynamic Tomographic Imaging Research Articles

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.

A dedicated high-resolution PET imager for plant sciences

PET provides an in vivo molecular and functional imaging capability that could be valuable for studying the interaction of plants in changing environments at the whole-plant level. We have developed a dedicated plant PET imager housed in a plant growth chamber (PGC), which provides a fully controlled environment. The system currently contains two types of scintillation detector modules from commercial small animal PET scanners: 84 microPET® detectors, which are made with scintillation crystal arrays of 2.2 mm3 × 2.2 mm3 × 10 mm3 crystals to provide a large detection area; and 32 Inveon™ detectors, which are made with scintillation crystal arrays of 1.5 mm3 × 1.5 mm3 × 10 mm3 crystals to provide higher spatial resolution. The detector modules are configured to form two half-rings, which provide a 15 cm-diameter trans-axial field of view (FOV) for dynamic tomographic imaging of small plants. Alternatively, the Inveon detectors can be reconfigured to form quarter-rings, which provide a 25 cm FOV using step-and-shoot motion. The imager contains two linear stages that move detectors vertically at different heights for multisection scanning, and two rotation stages to collect coincidence events from all angles when using the step-and-shoot acquisition. The detector modules and mechanical components of the imager are housed inside a PGC that regulates the environmental parameters. The system has a typical energy resolution of 15% for the Inveon detectors and 24% for the microPET detectors, timing resolution of 1.8 ns, and sensitivity of 1.3%, 1.4% and 3.0% measured at the center of the FOV, 5 cm off to the larger half-ring and 5 cm off to the smaller half-ring, respectively (with a 350–650 keV energy window and 3.1 ns timing window). The system’s spatial resolution is capable of resolving rod sources of 1.25 mm diameter spaced 2.5 mm apart (center to center) using the ML-EM reconstruction algorithm. Preliminary imaging experiments using soybean and wild type and mutant maize labeled with 11CO2 produced high-quality dynamic PET images that reveal the translocation and distribution patterns of photoassimilates. This system can be used to provide an in vivo molecular and functional imaging capability for plant research.

4-D Reconstruction for Dynamic Fluorescence Diffuse Optical Tomography

Dynamic fluorescence diffuse optical tomography (FDOT) is important for the research of drug delivery, medical diagnosis and treatment. Conventionally, dynamic tomographic images are reconstructed frame by frame, independently. This approach fails to account for the temporal correlations in measurement data. Ideally, the entire image sequence should be considered as a whole and a four-dimensional (4-D) reconstruction should be performed. However, the fully 4-D reconstruction is computationally intensive. In this paper, we propose a new 4-D reconstruction approach for dynamic FDOT, which is achieved by applying a temporal Karhunen-Loève (KL) transformation to the imaging equation. By taking advantage of the decorrelation and compression properties of the KL transformation, the complex 4-D optical reconstruction problem is greatly simplified. To evaluate the performance of the method, simulation, phantom, and in vivo experiments (N=7) are performed on a hybrid FDOT/x-ray computed tomography imaging system. The experimental results indicate that the reconstruction images obtained by the KL method provide good reconstruction quality. Additionally, by discarding high-order KL components, the computation time involved with fully 4-D reconstruction can be greatly reduced in contrast to the conventional frame-by-frame reconstruction.

Imaging of Indocyanine Green Perfusion in Mouse Liver With Fluorescence Diffuse Optical Tomography

Dynamic fluorescence diffuse optical tomography (D-FDOT) is important for drug deliver research, as it allows to image the distribution of fluorescent biomarkers over time in vivo. In this letter, we visualize mouse liver perfusion after intravenous injection of indocyanine green (ICG) using a hybrid free-space FDOT and microcomputed tomography (micro-CT) system. In vivo dynamic tomographic images (110 frames; approximately 120 min) of mouse liver are reconstructed using structural a priori information provided by micro-CT, incorporating heterogeneous forward model. Further, the pharmacokinetic parameters of ICG in the liver, reflecting the uptake and excretion rate constants, are obtained using a two-compartmental model. The results show that we cannot only tomographically image the perfusion of fluorescent biomarkers in small animals on minute time scale (approximately 1.1 min/frame) but also obtain the corresponding pharmacokinetic parameters in vivo using the hybrid FDOT/micro-CT imaging system.

In vivo whole-body imaging of optical agent dynamics using full angle fluorescence diffuse optical tomography

Dynamic fluorescence diffuse optical tomography (FDOT) is important in drug deliver research. In this letter, we first image the metabolic processes of micelles indocyanine green throughout the whole body of a nude mouse using the full-angle FDOT system with line illumination (L-FDOT). The resolution of L-FDOT is evaluated using phantom experiment. Next, in vivo dynamic tomographic images (100 frames; approximately 170 min) of mouse liver and abdomen are shown and cross-validated by planar fluorescence reflectance imaging in vitro. Results provide evidence on applicability of the tomographic image whole-body biological activities in vivo on minute timescale (approximately 1.7 min) using L-FDOT.

Visualizing localized dynamic changes during epileptic seizure onset in vivo with diffuse optical tomography

Diffuse optical tomography (DOT) is a promising functional imaging modality due to its ability to provide quantitative and dynamic tomographic imaging of brain functions. This pilot study was conducted to demonstrate that DOT can be used to visualize the changes in local hemodynamics during seizures. The focal seizure was induced by microinjection of 10 microl of 1.9 mM GABAA antagonist bicuculline methiodide (BMI) into the left parietal neocortex of male Harlen Sprague-Dawley rats, which was imaged by a multispectral continuous-wave DOT system. Functional images were obtained by our finite element based image reconstruction algorithm. A series of dynamic 2D images were obtained to delineate the time course of concentration changes of oxyhaemoglobin, deoxyhaemoglobin, and total hemoglobin in the rat brain during seizure onset. The BMI induced epileptic foci were localized and observed over time from the images obtained. Our results suggest that diffuse optical tomography may be a promising modality for epilepsy imaging due to its ability to localize epileptic foci as well as its potential to map the functional activity in the area of human cerebral cortex in planning of epilepsy surgery.

Kullback proximal algorithms for maximum-likelihood estimation

Accelerated algorithms for maximum-likelihood image reconstruction are essential for emerging applications such as three-dimensional (3-D) tomography, dynamic tomographic imaging, and other high-dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization methods for computing maximum-likelihood estimates and study its convergence properties. These methods are based on a proximal point algorithm implemented with the Kullback-Liebler (KL) divergence between posterior densities of the complete data as a proximal penalty function. When the proximal relaxation parameter is set to unity, one obtains the classical expectation-maximization (EM) algorithm. For a decreasing sequence of relaxation parameters, relaxed versions of EM are obtained which can have much faster asymptotic convergence without sacrifice of monotonicity. We present an implementation of the algorithm using More's (1983) trust region update strategy. For illustration, the method is applied to a nonquadratic inverse problem with Poisson distributed data.