A multi-phase level set framework for source reconstruction in bioluminescence tomography

We propose a novel multi-phase level set algorithm for solving the inverse problem of bioluminescence tomography. The distribution of unknown interior source is considered as piecewise constant and represented by using multiple level set functions. The localization of interior bioluminescence source is implemented by tracing the evolution of level set function. An alternate search scheme is incorporated to ensure the global optimal of reconstruction. Both numerical and physical experiments are performed to evaluate the developed level set reconstruction method. Reconstruction results show that the proposed method can stably resolve the interior source of bioluminescence tomography.

[1]  E. Hoffman,et al.  In vivo mouse studies with bioluminescence tomography. , 2006, Optics express.

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

[3]  U. Ascher,et al.  Dynamic level set regularization for large distributed parameter estimation problems , 2007 .

[4]  Jie Tian,et al.  An optimal permissible source region strategy for multispectral bioluminescence tomography. , 2008, Optics express.

[5]  Luminita A. Vese,et al.  A PIECEWISE-CONSTANT BINARY MODEL FOR ELECTRICAL IMPEDANCE TOMOGRAPHY , 2007 .

[6]  B. Rice,et al.  In vivo imaging of light-emitting probes. , 2001, Journal of biomedical optics.

[7]  R. Kress,et al.  Inverse Acoustic and Electromagnetic Scattering Theory , 1992 .

[8]  Jie Tian,et al.  A study of photon propagation in free-space based on hybrid radiosity-radiance theorem. , 2009, Optics express.

[9]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[10]  B. Wilson,et al.  A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo. , 1992, Medical physics.

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

[12]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[13]  O. Dorn,et al.  Level set methods for inverse scattering , 2006 .

[14]  Miguel Moscoso,et al.  A level set evolution strategy in microwave imaging for early breast cancer detection , 2008, Comput. Math. Appl..

[15]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[16]  David Boas,et al.  Three-dimensional shape-based imaging of absorption perturbation for diffuse optical tomography. , 2003, Applied optics.

[17]  R. Leahy,et al.  Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography , 2008, Physics in medicine and biology.

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

[19]  Eric T. Chung,et al.  Electrical impedance tomography using level set representation and total variational regularization , 2005 .

[20]  Vasilis Ntziachristos,et al.  The inverse source problem based on the radiative transfer equation in optical molecular imaging , 2005 .

[21]  Xue-Cheng Tai,et al.  Level Set Method for Positron Emission Tomography , 2007, Int. J. Biomed. Imaging.

[22]  J. Ripoll,et al.  Experimental determination of photon propagation in highly absorbing and scattering media. , 2005, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  M. Schweiger,et al.  A finite element approach for modeling photon transport in tissue. , 1993, Medical physics.

[24]  T. Chan,et al.  Level set and total variation regularization for elliptic inverse problems with discontinuous coefficients , 2004 .

[25]  Yujie Lu,et al.  A Parallel Adaptive Finite Element Method for the Simulation of Photon Migration with the Radiative-Transfer-Based Model. , 2009, Communications in numerical methods in engineering.

[26]  E. Richer,et al.  Iterative reconstruction method for light emitting sources based on the diffusion equation. , 2005, Medical physics.

[27]  Jie Tian,et al.  Multimodality Molecular Imaging , 2008, IEEE Engineering in Medicine and Biology Magazine.

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

[29]  S R Arridge,et al.  Reconstructing absorption and diffusion shape profiles in optical tomography by a level set technique. , 2006, Optics letters.

[30]  Y Liu,et al.  Flux vector formulation for photon propagation in the biological tissue. , 2007, Optics letters.

[31]  J. Willmann,et al.  Molecular imaging in drug development , 2008, Nature Reviews Drug Discovery.

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

[33]  Jie Tian,et al.  A new numerical method for BLT forward problem based on high‐order finite elements , 2009 .

[34]  Ming Jiang,et al.  Image reconstruction for bioluminescence tomography from partial measurement. , 2007, Optics express.

[35]  Vadim Y Soloviev,et al.  Tomographic bioluminescence imaging with varying boundary conditions. , 2007, Applied optics.