Fluorescence diffuse optical tomography (FDOT) is an emerging molecular imaging modality that uses near infrared light to excite the fluorophore injected into tissue; and to reconstruct the fluorophore concentration from boundary measurements. The FDOT image reconstruction is a highly ill-posed inverse problem due to a large number of unknowns and limited number of measurements. However, the fluorophore distribution is often very sparse in the imaging domain since fluorophores are typically designed to accumulate in relatively small regions. In this paper, we use compressive sensing (CS) framework to design light illumination and detection patterns to improve the reconstruction of sparse fluorophore concentration. Unlike the conventional FDOT imaging where spatially distributed light sources illuminate the imaging domain one at a time and the corresponding boundary measurements are used for image reconstruction, we assume that the light sources illuminate the imaging domain simultaneously several times and the corresponding boundary measurements are linearly filtered prior to image reconstruction. We design a set of optical intensities (illumination patterns) and a linear filter (detection pattern) applied to the boundary measurements to improve the reconstruction of sparse fluorophore concentration maps. We show that the FDOT sensing matrix can be expressed as a columnwise Kronecker product of two matrices determined by the excitation and emission light fields. We derive relationships between the incoherence of the FDOT forward matrix and these two matrices, and use these results to reduce the incoherence of the FDOT forward matrix. We present extensive numerical simulation and the results of a real phantom experiment to demonstrate the improvements in image reconstruction due to the CS-based light illumination and detection patterns in conjunction with relaxation and greedy-type reconstruction algorithms.

With the rapid development of Earth observation, remote sensing and geographical information system, people can obtain massive spatial data. The data people use is not yet limited to a local area, which gradually expands to the whole earth. Growth in the amount of data makes how to manage the global spatial data and how to supply user efficient data retrieval service becoming a key issue for academic community. This thesis proposes a spatial data index and retrieval method based on GeoSOT global discrete grid system. We use GeoSOT grid code in creating spatial data identification. Then the data can be managed in ordinary one-dimension database based on its GeoSOT identifications. This method can realize a unified management of global spatial data. In another aspect, it also proposes a solution for cross-database associative spatial data retrieval.

This paper addresses fluorescence diffuse optical tomography (FDOT) reconstruction problem with a priori information. We assume that approximate location of the fluorophore concentration is known from physiology of the disease and nature of the fluorophore injected. In addition, we assume the anatomical edge structures from an anatomical image modality partially coincident with the edges of the fluorophore concentration image. We formulate FDOT reconstruction in Bayesian framework and model a priori information of FDOT into a Gaussian Markov random field (GMRF) with several unknown parameters. We simultaneously estimate the optical image and the unknown a priori model parameters. Numerical simulations demonstrate that the a priori information could effectively improve the image reconstruction results.