? Small animals, particularly genetically engineered mice, are of increasing importance to understand the biologic basis for pathologic disease manifestations. Major efforts are being made to link the genome to the phenotypic expression in both form and function (""""""""Physiome""""""""). Small animal imaging offers the opportunity to evaluate pathologic progression in a much-compressed time frame and with a much-improved resolution. In this R21/R33 proposal, we plan to develop and integrate the first bioluminescent CT (BLCT) device for imaging gene expression and a state-of-the-art micro-CT scanner for studies of the lung in the living mouse model. Our long term goal is to advance molecular and micro imaging technology, and make the proposed integrated system an in vivo tool in biomedical applications, especially for small animal tomography.
The specific aims of the R21 Phase are to (1) develop a data acquisition module for bioluminescent imaging of the mouse lung; (2) establish the forward model for bioluminescence based on the optical properties of the mouse as derived from its CT volume; (3) demonstrate the feasibility of bioluminescent tomography in numerical simulation and phantom experiments.
The specific aims of the R33 Phase are to (1) prototype a bioluminescent CT device with I0 CCD cameras, and develop an iterative algorithm for a spatial resolution on the order of 1 ram; (2) build a multi-resolution micro-CT scanner with dual imaging, and develop analytic algorithms for high quality reconstructions, including the in vivo mode with about 20 micron spatial resolution and 20 second temporal resolution and a maximal spatial resolution on the order of 5 microns; (3) integrate the two cutting-edge CT modalities (optical and X-ray), and demonstrate the feasibility and utility of the integrated system in small animal studies. Upon completion of the R21/33 project, a unique micro-tomography system will have been developed with molecular, multi-resolution, multi-energy, dynamic and bioluminescent imaging capabilities. We will demonstrate, through example projects, the utility of the system in generating critical physiological and pathological information of the lung. ? ?
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| Deng, Junjun; Yu, Hengyong; Ni, Jun et al. (2009) Parallelism of iterative CT reconstruction based on local reconstruction algorithm. J Supercomput 48:1-14 |
| Han, Weimin; Wang, Ge (2008) Bioluminescence Tomography: Biomedical Background, Mathematical Theory, and Numerical Approximation. J Comput Math 26:324-335 |
