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Journal of Biomedical Optics • Open Access

Comparisons of hybrid radiosity-diffusion model and diffusion equation for bioluminescence tomography in cavity cancer detection
Author(s): Xueli Chen; Defu Yang; Xiaochao Qu; Jimin Liang; Jie Tian; Hao Hu; Xinbo Gao

Paper Abstract

Bioluminescence tomography (BLT) has been successfully applied to the detection and therapeutic evaluation of solid cancers. However, the existing BLT reconstruction algorithms are not accurate enough for cavity cancer detection because of neglecting the void problem. Motivated by the ability of the hybrid radiosity-diffusion model (HRDM) in describing the light propagation in cavity organs, an HRDM-based BLT reconstruction algorithm was provided for the specific problem of cavity cancer detection. HRDM has been applied to optical tomography but is limited to simple and regular geometries because of the complexity in coupling the boundary between the scattering and void region. In the provided algorithm, HRDM was first applied to three-dimensional complicated and irregular geometries and then employed as the forward light transport model to describe the bioluminescent light propagation in tissues. Combining HRDM with the sparse reconstruction strategy, the cavity cancer cells labeled with bioluminescent probes can be more accurately reconstructed. Compared with the diffusion equation based reconstruction algorithm, the essentiality and superiority of the HRDM-based algorithm were demonstrated with simulation, phantom and animal studies. An in vivo gastric cancer-bearing nude mouse experiment was conducted, whose results revealed the ability and feasibility of the HRDM-based algorithm in the biomedical application of gastric cancer detection.

Paper Details

Date Published: 5 June 2012
PDF: 10 pages
J. Biomed. Opt. 17(6) 066015 doi: 10.1117/1.JBO.17.6.066015
Published in: Journal of Biomedical Optics Volume 17, Issue 6
Show Author Affiliations
Xueli Chen, Xidian Univ. (China)
Defu Yang, Xidian Univ. (China)
Xiaochao Qu, Xidian Univ. (China)
Jimin Liang, Xidian Univ. (China)
Jie Tian, Xidian Univ. (China)
Hao Hu, Fourth Military Medical Univ. (China)
Xinbo Gao, Xidian Univ. (China)

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