Image-guided simulation for bioluminescence tomographic imaging

Durairaj Kumar; Wenxiang Cong; Jacqueline Thiesse; Earl Nixon; John Meinel Jr.; Alex Cong; Geoffrey McLennan M.D.; Eric A. Hoffman; Jiang Ming; Ge Wang

doi:10.1117/12.595387

12 April 2005 Image-guided simulation for bioluminescence tomographic imaging

Durairaj Kumar, Wenxiang Cong, Jacqueline Thiesse, Earl Nixon, John Meinel Jr., Alex Cong, Geoffrey McLennan M.D., Eric A. Hoffman, Jiang Ming, Ge Wang

Author Affiliations +

Proceedings Volume 5744, Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display; (2005) https://doi.org/10.1117/12.595387
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

Noninvasive imaging of the reporter gene expression based on bioluminescence is playing an important role in the areas of cancer biology, cell biology, and gene therapy. The central problem for the bioluminescence tomography (BLT) we are developing is to reconstruct the underlying bioluminescent source distribution in a small animal using a modality fusion approach. To solve this inversion problem, a mathematical model of the mouse is built from a CT/micro-CT scan, which enables the assignment of optical parameters to various regions in the model. This optical geometrical model is used in the Monte Carlo simulation to calculate the flux distribution on the animal body surface, as a key part of the BLT process. The model development necessitates approximations in surface simplification, and so on. It leads to the model mismatches of different kinds. To overcome such discrepancies, instead of developing a mathematical model, segmented CT images are directly used in our simulation software. While the simulation code is executed, those images that are relevant are assessed according to the location of the propagating photon. Depending upon the segmentation rules including the pixel value range, appropriate optical parameters are selected for statistical sampling of the free path and weight of the photon. In this paper, we report luminescence experiments using a physical mouse phantom to evaluate this image-guided simulation procedure, which suggest both the feasibility and some advantages of this technique over the existing methods.

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Durairaj Kumar, Wenxiang Cong, Jacqueline Thiesse, Earl Nixon, John Meinel Jr., Alex Cong, Geoffrey McLennan M.D., Eric A. Hoffman, Jiang Ming, and Ge Wang "Image-guided simulation for bioluminescence tomographic imaging", Proc. SPIE 5744, Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display, (12 April 2005); https://doi.org/10.1117/12.595387

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KEYWORDS

Monte Carlo methods

Image segmentation

Animal model studies

Photon transport

3D modeling

Reverse modeling

Bioluminescence

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