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Journal of Biomedical Optics • Open Access

Detection of mouse liver cancer via a parallel iterative shrinkage method in hybrid optical/microcomputed tomography imaging
Author(s): Ping Wu; Kai Liu; Qian Zhang; Zhenwen Xue; Yongbao Li; Nannan Ning; Xin Yang; Xingde Li; Jie Tian

Paper Abstract

Liver cancer is one of the most common malignant tumors worldwide. In order to enable the noninvasive detection of small liver tumors in mice, we present a parallel iterative shrinkage (PIS) algorithm for dual-modality tomography. It takes advantage of microcomputed tomography and multiview bioluminescence imaging, providing anatomical structure and bioluminescence intensity information to reconstruct the size and location of tumors. By incorporating prior knowledge of signal sparsity, we associate some mathematical strategies including specific smooth convex approximation, an iterative shrinkage operator, and affine subspace with the PIS method, which guarantees the accuracy, efficiency, and reliability for three-dimensional reconstruction. Then an in vivo experiment on the bead-implanted mouse has been performed to validate the feasibility of this method. The findings indicate that a tiny lesion less than 3 mm in diameter can be localized with a position bias no more than 1 mm; the computational efficiency is one to three orders of magnitude faster than the existing algorithms; this approach is robust to the different regularization parameters and the lp norms. Finally, we have applied this algorithm to another in vivo experiment on an HCCLM3 orthotopic xenograft mouse model, which suggests the PIS method holds the promise for practical applications of whole-body cancer detection.

Paper Details

Date Published: 5 December 2012
PDF: 12 pages
J. Biomed. Opt. 17(12) 126012 doi: 10.1117/1.JBO.17.12.126012
Published in: Journal of Biomedical Optics Volume 17, Issue 12
Show Author Affiliations
Ping Wu, Institute of Automation (China)
Kai Liu, IBM China Research Lab. (China)
Qian Zhang, Xidian Univ. (China)
Zhenwen Xue, Institute of Automation (China)
Yongbao Li, Institute of Automation (China)
Nannan Ning, Harbin Univ. of Science and Technology (China)
Xin Yang, Institute of Automation (China)
Xingde Li, Johns Hopkins Univ. (United States)
Jie Tian, Institute of Automation (China)


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