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Proceedings Paper

Novel fusion for hybrid optical/microcomputed tomography imaging based on natural light surface reconstruction and iterated closest point
Author(s): Nannan Ning; Jie Tian; Xia Liu; Kexin Deng; Ping Wu; Bo Wang; Kun Wang; Xibo Ma
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Paper Abstract

In mathematics, optical molecular imaging including bioluminescence tomography (BLT), fluorescence tomography (FMT) and Cerenkov luminescence tomography (CLT) are concerned with a similar inverse source problem. They all involve the reconstruction of the 3D location of a single/multiple internal luminescent/fluorescent sources based on 3D surface flux distribution. To achieve that, an accurate fusion between 2D luminescent/fluorescent images and 3D structural images that may be acquired form micro-CT, MRI or beam scanning is extremely critical. However, the absence of a universal method that can effectively convert 2D optical information into 3D makes the accurate fusion challengeable. In this study, to improve the fusion accuracy, a new fusion method for dual-modality tomography (luminescence/fluorescence and micro-CT) based on natural light surface reconstruction (NLSR) and iterated closest point (ICP) was presented. It consisted of Octree structure, exact visual hull from marching cubes and ICP. Different from conventional limited projection methods, it is 360° free-space registration, and utilizes more luminescence/fluorescence distribution information from unlimited multi-orientation 2D optical images. A mouse mimicking phantom (one XPM-2 Phantom Light Source, XENOGEN Corporation) and an in-vivo BALB/C mouse with implanted one luminescent light source were used to evaluate the performance of the new fusion method. Compared with conventional fusion methods, the average error of preset markers was improved by 0.3 and 0.2 pixels from the new method, respectively. After running the same 3D internal light source reconstruction algorithm of the BALB/C mouse, the distance error between the actual and reconstructed internal source was decreased by 0.19 mm.

Paper Details

Date Published: 17 February 2014
PDF: 7 pages
Proc. SPIE 8937, Multimodal Biomedical Imaging IX, 893713 (17 February 2014); doi: 10.1117/12.2038691
Show Author Affiliations
Nannan Ning, Harbin Univ. of Science and Technology (China)
Jie Tian, Institute of Automation (China)
Xidian Univ. (China)
Xia Liu, Harbin Univ. of Science and Technology (China)
Kexin Deng, Xidian Univ. (China)
Ping Wu, Institute of Automation (China)
Bo Wang, Harbin Univ. of Science and Technology (China)
Kun Wang, Institute of Automation (China)
Xibo Ma, Institute of Automation (China)


Published in SPIE Proceedings Vol. 8937:
Multimodal Biomedical Imaging IX
Fred S. Azar; Xavier Intes, Editor(s)

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