Spectrally resolved bioluminescence tomography with adaptive finite element : methodology and simulation

As a molecular imaging technique, bioluminescence tomography (BLT) with its highly sensitive detection and facile operation can significantly reveal molecular and cellular information in vivo at the whole-body small animal level. However, because of complex photon transportation in biological tissue and boundary detection data with high noise, bioluminescent sources in deeper positions generally cannot be localized. In our previous work, we used achromatic or monochromatic measurements and a priori permissible source region strategy to develop a multilevel adaptive finite-element algorithm. In this paper, we propose a spectrally solved tomographic algorithm with a posteriori permissible source region selection. Multispectral measurements, and anatomical and optical information first deal with the nonuniqueness of BLT and constrain the possible solution of source reconstruction. The use of adaptive mesh refinement and permissible source region based on a posteriori measures not only avoids the dimension disaster arising from the multispectral measured data but also reduces the ill-posedness of BLT and therefore improves the reconstruction quality. Reconsideration to the optimization method and related modifications further enhance reconstruction robustness and efficiency. We also incorporate into the method some improvements for reducing computational burdens. Finally, using a whole-body virtual mouse phantom, we demonstrate the capability of the proposed BLT algorithm to reconstruct accurately bioluminescent sources in deeper positions. In terms of optical property errors and two sources of discernment in deeper positions, this BLT algorithm represents the unique predominance for BLT reconstruction. (Some figures in this article are in colour only in the electronic version) 0031-9155/07/000001+16$30.00 © 2007 IOP Publishing Ltd Printed in the UK 1

[1]  Tamara L. Troy,et al.  Determination of depth of in vivo bioluminescent signals using spectral imaging techniques , 2003, SPIE BiOS.

[2]  Vasilis Ntziachristos,et al.  Looking and listening to light: the evolution of whole-body photonic imaging , 2005, Nature Biotechnology.

[3]  Charles A. Bouman,et al.  Nonlinear multigrid algorithms for Bayesian optical diffusion tomography , 2001, IEEE Trans. Image Process..

[4]  Roy,et al.  Active constrained truncated Newton method for simple-bound optical tomography , 2000, Journal of the Optical Society of America. A, Optics, image science, and vision.

[5]  Yi Liu,et al.  Geometrical modeling using multiregional marching tetrahedra for bioluminescence tomography , 2005, SPIE Medical Imaging.

[6]  J. Bey,et al.  Tetrahedral grid refinement , 1995, Computing.

[7]  A. Chatziioannou,et al.  Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: a computer simulation feasibility study , 2005, Physics in medicine and biology.

[8]  Ge Wang,et al.  A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with the Monte Carlo method. , 2004, Academic radiology.

[9]  M. Jiang,et al.  Uniqueness theorems in bioluminescence tomography. , 2004, Medical physics.

[10]  P. Hansen.  Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion , 1987 .

[11]  Ge Wang,et al.  Multispectral Bioluminescence Tomography: Methodology and Simulation , 2006, Int. J. Biomed. Imaging.

[12]  S. Gambhir,et al.  Optical imaging of Renilla luciferase reporter gene expression in living mice , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[13]  M. Schweiger,et al.  The finite element method for the propagation of light in scattering media: boundary and source conditions. , 1995, Medical physics.

[14]  S R Arridge,et al.  Recent advances in diffuse optical imaging , 2005, Physics in medicine and biology.

[15]  Arion F Chatziioannou,et al.  Effect of optical property estimation accuracy on tomographic bioluminescence imaging: simulation of a combined optical–PET (OPET) system , 2006, Physics in medicine and biology.

[16]  A. Joshi,et al.  Adaptive finite element based tomography for fluorescence optical imaging in tissue. , 2004, Optics express.

[17]  Britton Chance,et al.  Diffuse optical tomography of highly heterogeneous media , 2001, IEEE Transactions on Medical Imaging.

[18]  Jie Tian,et al.  A multilevel adaptive finite element algorithm for bioluminescence tomography. , 2006, Optics express.

[19]  B. Pogue,et al.  Spectrally resolved bioluminescence optical tomography. , 2006, Optics letters.

[20]  Eric C Frey,et al.  Development of a 4-D digital mouse phantom for molecular imaging research. , 2004, Molecular imaging and biology : MIB : the official publication of the Academy of Molecular Imaging.

[21]  B. Pogue,et al.  Comparison of imaging geometries for diffuse optical tomography of tissue. , 1999, Optics express.

[22]  C. Contag,et al.  Emission spectra of bioluminescent reporters and interaction with mammalian tissue determine the sensitivity of detection in vivo. , 2005, Journal of biomedical optics.

[23]  S. Gambhir,et al.  Molecular imaging in living subjects: seeing fundamental biological processes in a new light. , 2003, Genes & development.

[24]  Yi Liu,et al.  A practical method to determine the light source distribution in bioluminescent imaging , 2004, SPIE Optics + Photonics.

[25]  R. Leahy,et al.  Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging , 2005, Physics in medicine and biology.

[26]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[27]  Per Christian Hansen,et al.  Regularization methods for large-scale problems , 1993 .

[28]  Geoffrey McLennan,et al.  Practical reconstruction method for bioluminescence tomography. , 2005, Optics express.

[29]  J. Ripoll,et al.  Free-space propagation of diffuse light: theory and experiments. , 2003, Physical review letters.

[30]  Ziji Wu,et al.  Multiple material marching cubes algorithm , 2003 .

[31]  José Mario Martínez,et al.  Large-Scale Active-Set Box-Constrained Optimization Method with Spectral Projected Gradients , 2002, Comput. Optim. Appl..

[32]  J. Ripoll,et al.  Fast analytical approximation for arbitrary geometries in diffuse optical tomography. , 2002, Optics letters.