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Proceedings Paper

Hybrid light transport model based bioluminescence tomography reconstruction for early gastric cancer detection
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Paper Abstract

Gastric cancer is the second cause of cancer-related death in the world, and it remains difficult to cure because it has been in late-stage once that is found. Early gastric cancer detection becomes an effective approach to decrease the gastric cancer mortality. Bioluminescence tomography (BLT) has been applied to detect early liver cancer and prostate cancer metastasis. However, the gastric cancer commonly originates from the gastric mucosa and grows outwards. The bioluminescent light will pass through a non-scattering region constructed by gastric pouch when it transports in tissues. Thus, the current BLT reconstruction algorithms based on the approximation model of radiative transfer equation are not optimal to handle this problem. To address the gastric cancer specific problem, this paper presents a novel reconstruction algorithm that uses a hybrid light transport model to describe the bioluminescent light propagation in tissues. The radiosity theory integrated with the diffusion equation to form the hybrid light transport model is utilized to describe light propagation in the non-scattering region. After the finite element discretization, the hybrid light transport model is converted into a minimization problem which fuses an l1 norm based regularization term to reveal the sparsity of bioluminescent source distribution. The performance of the reconstruction algorithm is first demonstrated with a digital mouse based simulation with the reconstruction error less than 1mm. An in situ gastric cancer-bearing nude mouse based experiment is then conducted. The primary result reveals the ability of the novel BLT reconstruction algorithm in early gastric cancer detection.

Paper Details

Date Published: 29 February 2012
PDF: 8 pages
Proc. SPIE 8216, Multimodal Biomedical Imaging VII, 82160Q (29 February 2012); doi: 10.1117/12.908052
Show Author Affiliations
Xueli Chen, Xidian Univ. (China)
Jimin Liang, Xidian Univ. (China)
Hao Hu, Fourth Military Medical Univ. (China)
Xiaochao Qu, Xidian Univ. (China)
Defu Yang, Xidian Univ. (China)
Duofang Chen, Xidian Univ. (China)
Shouping Zhu, Xidian Univ. (China)
Jie Tian, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 8216:
Multimodal Biomedical Imaging VII
Fred S. Azar; Xavier Intes, Editor(s)

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