Research Papers: Imaging

Adaptive row-action inverse solver for fast noise-robust three-dimensional reconstructions in bioluminescence tomography: theory and dual-modality optical/computed tomography in vivo studies

[+] Author Affiliations
Ali Behrooz

PerkinElmer, Inc., 2061 Challenger Drive, Alameda, California 94501

School of Electrical and Computer Engineering, Georgia Institute of Technology, 777 Atlantic Drive, Atlanta, Georgia 30332

Chaincy Kuo, Heng Xu, Brad Rice

PerkinElmer, Inc., 2061 Challenger Drive, Alameda, California 94501

J. Biomed. Opt. 18(7), 076010 (Jul 10, 2013). doi:10.1117/1.JBO.18.7.076010
History: Received March 27, 2013; Revised May 17, 2013; Accepted May 21, 2013
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Abstract.  A novel approach is presented for obtaining fast robust three-dimensional (3-D) reconstructions of bioluminescent reporters buried deep inside animal subjects from multispectral images of surface bioluminescent photon densities. The proposed method iteratively acts upon the equations relating the multispectral data to the luminescent distribution with high computational efficiency to provide robust 3-D reconstructions. Unlike existing algebraic reconstruction techniques, the proposed method is designed to use adaptive projections that iteratively guide the updates to the solution with improved speed and robustness. Contrary to least-squares reconstruction methods, the proposed technique does not require parameter selection or optimization for optimal performance. Additionally, optimized schemes for thresholding, sampling, and ordering of the bioluminescence tomographic data used by the proposed method are presented. The performance of the proposed approach in reconstructing the shape, volume, flux, and depth of luminescent inclusions is evaluated in a multitude of phantom-based and dual-modality in vivo studies in which calibrated sources are implanted in animal subjects and imaged in a dual-modality optical/computed tomography platform. Statistical analysis of the errors in the depth and flux of the reconstructed inclusions and the convergence time of the proposed method is used to demonstrate its unbiased performance, low error variance, and computational efficiency.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Ali Behrooz ; Chaincy Kuo ; Heng Xu and Brad Rice
"Adaptive row-action inverse solver for fast noise-robust three-dimensional reconstructions in bioluminescence tomography: theory and dual-modality optical/computed tomography in vivo studies", J. Biomed. Opt. 18(7), 076010 (Jul 10, 2013). ; http://dx.doi.org/10.1117/1.JBO.18.7.076010


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