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SYSTEM AND METHOD FOR MULTIMODE THREE DIMENSIONAL OPTICAL TOMOGRAPHY BASED ON SPECIFICITY
专利权人:
INSTITUTE OF AUTOMATION; CHINESE ACADEMY OF SCIENCES
发明人:
TIAN, JIE,YANG, XIN,LIU, KAI,HAN, DONG,QIN, CHENGHU
申请号:
CNCN2010/001930
公开号:
WO2012/071682A1
申请日:
2010.11.30
申请国别(地区):
WO
年份:
2012
代理人:
摘要:
A multimode three dimensional optical tomography system and method thereof based on specificity are disclosed. The multimode three dimensional optical tomography system based on specificity comprises: an optical imaging submodule, a CT imaging submodule, a translating table (102), a rotary table (103), an electric control system (106), a rotating control and processing software platform (107). The electric control system (106) is used to control the translating table (102) and the rotary table (103), the rotating control and processing software platform (107) is used to establish an equation representing the linear relationship among the obtained optical signal intensity distribution of the target surface, the obtained CT discrete grid data and the unknown inner self-luminescence light source distribution, to establish a sparse regularized target function in each iterative step for the above equation and reconstruct a tomographic image. Besides, the multimode three dimensional optical tomography method based on specificity comprises the following steps: conducting an optical imaging to obtain an optical signal intensity of the body surface of the target to be imaged conducting a CT imaging to obtain data of the structure body establishing an equation representing the linear relationship among the obtained optical signal intensity distribution of the target surface, the obtained CT discrete grid data and the unknown inner self-luminescence light source distribution establishing a dynamic sparse regularized target function in each iterative step for the above equation and reconstructing a tomographic image. The tomography system and method in the present invention can realize three dimensional tomography for an object to be imaged wholly avoid the dependence on the prior knowledge about the approximate distribution position improve the robustness of the image reconstruction and decrease the dependence on the selection of regularization parameters.Linvention concerne u
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