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Proceedings Paper

A posteriori correction for source decay in 3D bioluminescent source localization using multiview measured data
Author(s): Li Sun; Pu Wang; Jie Tian; Dan Liu; Ruifang Wang
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Paper Abstract

As a novel optical molecular imaging technique, bioluminescence tomography (BLT) can be used to monitor the biological activities non-invasively at the cellular and molecular levels. In most of known BLT studies, however, the time variation of the bioluminescent source is neglected. It gives rise to the inconsistent views during the multiview continuous wave measurement. In other words, the real measured data from different measured views come from 'different' bioluminescent sources. It could bring large errors in bioluminescence reconstruction. In this paper, a posteriori correction strategy for adaptive FEM-based reconstruction is proposed and developed. The method helps to improve the source localization considering the bioluminescent energy variance during the multiview measurement. In the method, the correction for boundary signals by means of a posteriori correction strategy, which adopts the energy ratio of measured data in the overlapping domains between the adjacent measurements as the correcting factor, can eliminate the effect of the inconsistent views. Then the adaptive mesh refinement with a posteriori error estimation helps to improve the precision and efficiency of BLT reconstruction. In addition, a priori permissible source region selection based on the surface measured data further reduces the ill-posedness of BLT and enhances numerical stability. Finally, three-dimension numerical simulations using the heterogeneous phantom are performed. The numerically measured data is generated by Monte Carlo (MC) method which is known as the Gold standard and can avoid the inverse crime. The reconstructed result with correction shows more accuracy compared to that without correction.

Paper Details

Date Published: 27 February 2009
PDF: 8 pages
Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 72622I (27 February 2009); doi: 10.1117/12.811394
Show Author Affiliations
Li Sun, Beijing Univ. of Technology (China)
Pu Wang, Beijing Univ. of Technology (China)
Jie Tian, Institute of Automation (China)
Dan Liu, Institute of Automation (China)
Ruifang Wang, Institute of Automation (China)

Published in SPIE Proceedings Vol. 7262:
Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging
Xiaoping P. Hu; Anne V. Clough, Editor(s)

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