X. Ma, J. Tian, X. Yang, C. Qin, S. Zhu, and Z. Xue, “Research on liver tumor proliferation and angiogenesis based on multi-modality molecular imaging,” Acta Biophys. Sin. 27, 355–364 (2011).

K. Liu, J. Tian, C. Qin, X. Yang, S. Zhu, D. Han, and P. Wu, “Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models,” J. Biomed. Opt. 16, 046016 (2011).

J. Feng, C. Qin, K. Jia, D. Han, K. Liu, S. Zhu, X. Yang, and J. Tian, “An adaptive regularization parameter choice strategy for multi-spectral bioluminescence tomography,” Med. Phys. 38, 5933–5944 (2011).

[CrossRef]

L. Yao and H. Jiang, “Photoacoustic image reconstruction from few-detector and limited-angle data,” Biomed. Opt. Express 2, 2649–2654 (2011).

[CrossRef]

L. Yao and H. Jiang, “Enhancing finite element based photoacoustic tomography using total-variation minimization,” Appl. Opt. 50, 5031–5041 (2011).

[CrossRef]

H. Gao and H. Zhao, “Multilevel bioluminescence tomography based on radiative transfer equation Part 1: l1 regularization,” Opt. Express 18, 1854–1871 (2010).

[CrossRef]

H. Gao and H. Zhao, “Multilevel bioluminescence tomography based on radiative transfer equation Part 2: total variation and l1 data fidelity,” Opt. Express 18, 2894–2912 (2010).

[CrossRef]

J. Feng, K. Jia, C. Qin, S. Zhu, X. Yang, and J. Tian, “Sparse Bayesian reconstruction method for multispectral bioluminescence tomography,” Chin. Opt. Lett. 8, 1010–1014 (2010).

[CrossRef]

T. Goldstein, X. Bresson, and S. Osher, “Geometric applications of the split Bregman method: segmentation and surface reconstruction,” J. Sci. Comput. 45, 272–293 (2010).

[CrossRef]

G. C. Kagadis, G. Loudos, K. Katsanos, S. G. Langer, and G. C. Nikiforidis, “In vivo small animal imaging: current status and future prospects,” Med. Phys. 37, 6421–6442 (2010).

[CrossRef]

Y. Lu, B. Zhu, H. Shen, J. C. Rasmussen, G. Wang, and E. M. Sevick-Muraca, “A parallel adaptive finite element simplified spherical harmonics approximation solver for frequency domain fluorescence molecular imaging,” Phys. Med. Biol. 55, 4625–4645 (2010).

[CrossRef]

A. D. Klose, B. J. Beattie, H. Dehghani, L. Vider, C. Le, V. Ponomarev, and R. Blasberg, “In vivo bioluminescence tomography with a blocking-off finite-difference SP3 method and MRI/CT coregistration,” Med. Phys. 37, 329–338 (2010).

[CrossRef]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80, 043701 (2009).

[CrossRef]

J. Cai, S. Osher, and Z. Shen, “Linearized Bregman iterations for compressed sensing,” Math. Comput. 78, 1515–1536(2009).

[CrossRef]

J. Cai, S. Osher, and Z. Shen, “Convergence of the linearized Bregman iteration for l1-norm minimization,” Math. Comput. 78, 2127–2136 (2009).

[CrossRef]

T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009).

J. Feng, K. Jia, C. Qin, G. Yan, X. Zhang, J. Liu, and J. Tian, “3D bioluminescence tomography based on Bayesian approach,” Opt. Express 17, 16834–16848 (2009).

[CrossRef]

J. Feng, K. Jia, G. Yan, S. Zhu, Y. Lv, and J. Tian, “An optimal permissible source region strategy for multispectral bioluminescence tomography,” Opt. Express 16, 15640–15654 (2008).

[CrossRef]

H. Dehghani, S. C. Davis, and B. W. Pogue, “Spectrally resolved bioluminescence tomography using the reciprocity approach,” Med. Phys. 35, 4863–4871 (2008).

[CrossRef]

S. Ahn, A. J. Chaudhari, F. Darvas, C. A. Bouman, and R. M. Leahy, “Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography,” Phys. Med. Biol. 53, 3921–3942 (2008).

[CrossRef]

W. Yin, S. Osher, D. Goldfarb, and J. Darbon, “Bregman iterative algorithms for l1-minimization with applications to compressed sensing,” SIAM J. Imaging Sci. 1, 143–168 (2008).

J. K. Willmann, N. van Bruggen, L. M. Dinkelborg, and S. S. Gambhir, “Molecular imaging in drug development,” Nat. Rev. Drug Discov. 7, 591–607 (2008).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, W. Yang, C. Qin, and M. Xu, “Spectrally resolved bioluminescence tomography with adaptive finite element analysis: methodology and simulation,” Phys. Med. Biol. 52, 4497–4512 (2007).

[CrossRef]

M. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1, 586–597 (2007).

[CrossRef]

H. Dehghani, S. C. Davis, S. Jiang, B. W. Pogue, K. D. Paulsen, and M. S. Patterson, “Spectrally resolved bioluminescence optical tomography,” Opt. Lett. 31, 365–367 (2006).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, J. Luo, W. Yang, and H. Li, “A multilevel adaptive finite element algorithm for bioluminescence tomography,” Opt. Express 14, 8211–8223 (2006).

[CrossRef]

G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: a computer simulation feasibility study,” Phys. Med. Biol. 50, 4225–4241 (2005).

[CrossRef]

A. J. Chaudhari, F. Darvas, J. R. Bading, R. A. Moats, P. S. Conti, D. J. Smith, S. R. Cherry, and R. M. Leahy, “Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging,” Phys. Med. Biol. 50, 5421–5441 (2005).

[CrossRef]

S. Osher, M. Burger, D. Glodfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation based image restoration,” Multiscale Model. Simul. 4, 460–489 (2005).

[CrossRef]

G. Wang, Y. Li, and M. Jiang, “Uniqueness theorems in bioluminescence tomography,” Med. Phys. 31, 2289–2299 (2004).

[CrossRef]

H. Li, J. Tian, F. Zhu, W. Cong, L. V. Wang, E. A. Hoffman, and G. Wang, “A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with Monte Carlo method,” Acad. Radiol. 11, 1029–1038 (2004).

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision 20, 89–97 (2004).

[CrossRef]

C. H. Contag and M. H. Bachmann, “Advances in in vivo bioluminescence imaging of gene expression,” Annu. Rev. Biomed. Eng. 4, 235–260 (2002).

[CrossRef]

T. Chan and C. K. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process. 7, 370–375 (1998).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60, 259–268 (1992).

[CrossRef]

S. Ahn, A. J. Chaudhari, F. Darvas, C. A. Bouman, and R. M. Leahy, “Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography,” Phys. Med. Biol. 53, 3921–3942 (2008).

[CrossRef]

G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: a computer simulation feasibility study,” Phys. Med. Biol. 50, 4225–4241 (2005).

[CrossRef]

C. H. Contag and M. H. Bachmann, “Advances in in vivo bioluminescence imaging of gene expression,” Annu. Rev. Biomed. Eng. 4, 235–260 (2002).

[CrossRef]

A. J. Chaudhari, F. Darvas, J. R. Bading, R. A. Moats, P. S. Conti, D. J. Smith, S. R. Cherry, and R. M. Leahy, “Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging,” Phys. Med. Biol. 50, 5421–5441 (2005).

[CrossRef]

A. D. Klose, B. J. Beattie, H. Dehghani, L. Vider, C. Le, V. Ponomarev, and R. Blasberg, “In vivo bioluminescence tomography with a blocking-off finite-difference SP3 method and MRI/CT coregistration,” Med. Phys. 37, 329–338 (2010).

[CrossRef]

A. D. Klose, B. J. Beattie, H. Dehghani, L. Vider, C. Le, V. Ponomarev, and R. Blasberg, “In vivo bioluminescence tomography with a blocking-off finite-difference SP3 method and MRI/CT coregistration,” Med. Phys. 37, 329–338 (2010).

[CrossRef]

S. Ahn, A. J. Chaudhari, F. Darvas, C. A. Bouman, and R. M. Leahy, “Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography,” Phys. Med. Biol. 53, 3921–3942 (2008).

[CrossRef]

T. Goldstein, X. Bresson, and S. Osher, “Geometric applications of the split Bregman method: segmentation and surface reconstruction,” J. Sci. Comput. 45, 272–293 (2010).

[CrossRef]

S. Osher, M. Burger, D. Glodfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation based image restoration,” Multiscale Model. Simul. 4, 460–489 (2005).

[CrossRef]

J. Cai, S. Osher, and Z. Shen, “Convergence of the linearized Bregman iteration for l1-norm minimization,” Math. Comput. 78, 2127–2136 (2009).

[CrossRef]

J. Cai, S. Osher, and Z. Shen, “Linearized Bregman iterations for compressed sensing,” Math. Comput. 78, 1515–1536(2009).

[CrossRef]

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision 20, 89–97 (2004).

[CrossRef]

T. Chan and C. K. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process. 7, 370–375 (1998).

[CrossRef]

G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: a computer simulation feasibility study,” Phys. Med. Biol. 50, 4225–4241 (2005).

[CrossRef]

S. Ahn, A. J. Chaudhari, F. Darvas, C. A. Bouman, and R. M. Leahy, “Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography,” Phys. Med. Biol. 53, 3921–3942 (2008).

[CrossRef]

A. J. Chaudhari, F. Darvas, J. R. Bading, R. A. Moats, P. S. Conti, D. J. Smith, S. R. Cherry, and R. M. Leahy, “Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging,” Phys. Med. Biol. 50, 5421–5441 (2005).

[CrossRef]

A. J. Chaudhari, F. Darvas, J. R. Bading, R. A. Moats, P. S. Conti, D. J. Smith, S. R. Cherry, and R. M. Leahy, “Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging,” Phys. Med. Biol. 50, 5421–5441 (2005).

[CrossRef]

T. F. Coleman and Y. Li, “A reflective newton method for minimizing a quadratic function subject to bounds on some of the variables,” SIAM J. Optim. 6, 1040–1058 (1996).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, W. Yang, C. Qin, and M. Xu, “Spectrally resolved bioluminescence tomography with adaptive finite element analysis: methodology and simulation,” Phys. Med. Biol. 52, 4497–4512 (2007).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, J. Luo, W. Yang, and H. Li, “A multilevel adaptive finite element algorithm for bioluminescence tomography,” Opt. Express 14, 8211–8223 (2006).

[CrossRef]

H. Li, J. Tian, F. Zhu, W. Cong, L. V. Wang, E. A. Hoffman, and G. Wang, “A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with Monte Carlo method,” Acad. Radiol. 11, 1029–1038 (2004).

C. H. Contag and M. H. Bachmann, “Advances in in vivo bioluminescence imaging of gene expression,” Annu. Rev. Biomed. Eng. 4, 235–260 (2002).

[CrossRef]

A. J. Chaudhari, F. Darvas, J. R. Bading, R. A. Moats, P. S. Conti, D. J. Smith, S. R. Cherry, and R. M. Leahy, “Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging,” Phys. Med. Biol. 50, 5421–5441 (2005).

[CrossRef]

W. Yin, S. Osher, D. Goldfarb, and J. Darbon, “Bregman iterative algorithms for l1-minimization with applications to compressed sensing,” SIAM J. Imaging Sci. 1, 143–168 (2008).

S. Ahn, A. J. Chaudhari, F. Darvas, C. A. Bouman, and R. M. Leahy, “Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography,” Phys. Med. Biol. 53, 3921–3942 (2008).

[CrossRef]

A. J. Chaudhari, F. Darvas, J. R. Bading, R. A. Moats, P. S. Conti, D. J. Smith, S. R. Cherry, and R. M. Leahy, “Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging,” Phys. Med. Biol. 50, 5421–5441 (2005).

[CrossRef]

H. Dehghani, S. C. Davis, and B. W. Pogue, “Spectrally resolved bioluminescence tomography using the reciprocity approach,” Med. Phys. 35, 4863–4871 (2008).

[CrossRef]

H. Dehghani, S. C. Davis, S. Jiang, B. W. Pogue, K. D. Paulsen, and M. S. Patterson, “Spectrally resolved bioluminescence optical tomography,” Opt. Lett. 31, 365–367 (2006).

[CrossRef]

A. D. Klose, B. J. Beattie, H. Dehghani, L. Vider, C. Le, V. Ponomarev, and R. Blasberg, “In vivo bioluminescence tomography with a blocking-off finite-difference SP3 method and MRI/CT coregistration,” Med. Phys. 37, 329–338 (2010).

[CrossRef]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80, 043701 (2009).

[CrossRef]

H. Dehghani, S. C. Davis, and B. W. Pogue, “Spectrally resolved bioluminescence tomography using the reciprocity approach,” Med. Phys. 35, 4863–4871 (2008).

[CrossRef]

H. Dehghani, S. C. Davis, S. Jiang, B. W. Pogue, K. D. Paulsen, and M. S. Patterson, “Spectrally resolved bioluminescence optical tomography,” Opt. Lett. 31, 365–367 (2006).

[CrossRef]

J. K. Willmann, N. van Bruggen, L. M. Dinkelborg, and S. S. Gambhir, “Molecular imaging in drug development,” Nat. Rev. Drug Discov. 7, 591–607 (2008).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60, 259–268 (1992).

[CrossRef]

J. Feng, C. Qin, K. Jia, D. Han, K. Liu, S. Zhu, X. Yang, and J. Tian, “An adaptive regularization parameter choice strategy for multi-spectral bioluminescence tomography,” Med. Phys. 38, 5933–5944 (2011).

[CrossRef]

J. Feng, K. Jia, C. Qin, S. Zhu, X. Yang, and J. Tian, “Sparse Bayesian reconstruction method for multispectral bioluminescence tomography,” Chin. Opt. Lett. 8, 1010–1014 (2010).

[CrossRef]

J. Feng, K. Jia, C. Qin, G. Yan, X. Zhang, J. Liu, and J. Tian, “3D bioluminescence tomography based on Bayesian approach,” Opt. Express 17, 16834–16848 (2009).

[CrossRef]

J. Feng, K. Jia, G. Yan, S. Zhu, Y. Lv, and J. Tian, “An optimal permissible source region strategy for multispectral bioluminescence tomography,” Opt. Express 16, 15640–15654 (2008).

[CrossRef]

M. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1, 586–597 (2007).

[CrossRef]

J. K. Willmann, N. van Bruggen, L. M. Dinkelborg, and S. S. Gambhir, “Molecular imaging in drug development,” Nat. Rev. Drug Discov. 7, 591–607 (2008).

[CrossRef]

S. Osher, M. Burger, D. Glodfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation based image restoration,” Multiscale Model. Simul. 4, 460–489 (2005).

[CrossRef]

W. Yin, S. Osher, D. Goldfarb, and J. Darbon, “Bregman iterative algorithms for l1-minimization with applications to compressed sensing,” SIAM J. Imaging Sci. 1, 143–168 (2008).

T. Goldstein, X. Bresson, and S. Osher, “Geometric applications of the split Bregman method: segmentation and surface reconstruction,” J. Sci. Comput. 45, 272–293 (2010).

[CrossRef]

T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009).

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80, 043701 (2009).

[CrossRef]

K. Liu, J. Tian, C. Qin, X. Yang, S. Zhu, D. Han, and P. Wu, “Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models,” J. Biomed. Opt. 16, 046016 (2011).

J. Feng, C. Qin, K. Jia, D. Han, K. Liu, S. Zhu, X. Yang, and J. Tian, “An adaptive regularization parameter choice strategy for multi-spectral bioluminescence tomography,” Med. Phys. 38, 5933–5944 (2011).

[CrossRef]

H. Li, J. Tian, F. Zhu, W. Cong, L. V. Wang, E. A. Hoffman, and G. Wang, “A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with Monte Carlo method,” Acad. Radiol. 11, 1029–1038 (2004).

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80, 043701 (2009).

[CrossRef]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80, 043701 (2009).

[CrossRef]

J. Feng, C. Qin, K. Jia, D. Han, K. Liu, S. Zhu, X. Yang, and J. Tian, “An adaptive regularization parameter choice strategy for multi-spectral bioluminescence tomography,” Med. Phys. 38, 5933–5944 (2011).

[CrossRef]

J. Feng, K. Jia, C. Qin, S. Zhu, X. Yang, and J. Tian, “Sparse Bayesian reconstruction method for multispectral bioluminescence tomography,” Chin. Opt. Lett. 8, 1010–1014 (2010).

[CrossRef]

J. Feng, K. Jia, C. Qin, G. Yan, X. Zhang, J. Liu, and J. Tian, “3D bioluminescence tomography based on Bayesian approach,” Opt. Express 17, 16834–16848 (2009).

[CrossRef]

J. Feng, K. Jia, G. Yan, S. Zhu, Y. Lv, and J. Tian, “An optimal permissible source region strategy for multispectral bioluminescence tomography,” Opt. Express 16, 15640–15654 (2008).

[CrossRef]

G. Wang, Y. Li, and M. Jiang, “Uniqueness theorems in bioluminescence tomography,” Med. Phys. 31, 2289–2299 (2004).

[CrossRef]

G. C. Kagadis, G. Loudos, K. Katsanos, S. G. Langer, and G. C. Nikiforidis, “In vivo small animal imaging: current status and future prospects,” Med. Phys. 37, 6421–6442 (2010).

[CrossRef]

G. C. Kagadis, G. Loudos, K. Katsanos, S. G. Langer, and G. C. Nikiforidis, “In vivo small animal imaging: current status and future prospects,” Med. Phys. 37, 6421–6442 (2010).

[CrossRef]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80, 043701 (2009).

[CrossRef]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80, 043701 (2009).

[CrossRef]

A. D. Klose, B. J. Beattie, H. Dehghani, L. Vider, C. Le, V. Ponomarev, and R. Blasberg, “In vivo bioluminescence tomography with a blocking-off finite-difference SP3 method and MRI/CT coregistration,” Med. Phys. 37, 329–338 (2010).

[CrossRef]

G. C. Kagadis, G. Loudos, K. Katsanos, S. G. Langer, and G. C. Nikiforidis, “In vivo small animal imaging: current status and future prospects,” Med. Phys. 37, 6421–6442 (2010).

[CrossRef]

A. D. Klose, B. J. Beattie, H. Dehghani, L. Vider, C. Le, V. Ponomarev, and R. Blasberg, “In vivo bioluminescence tomography with a blocking-off finite-difference SP3 method and MRI/CT coregistration,” Med. Phys. 37, 329–338 (2010).

[CrossRef]

S. Ahn, A. J. Chaudhari, F. Darvas, C. A. Bouman, and R. M. Leahy, “Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography,” Phys. Med. Biol. 53, 3921–3942 (2008).

[CrossRef]

A. J. Chaudhari, F. Darvas, J. R. Bading, R. A. Moats, P. S. Conti, D. J. Smith, S. R. Cherry, and R. M. Leahy, “Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging,” Phys. Med. Biol. 50, 5421–5441 (2005).

[CrossRef]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80, 043701 (2009).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, J. Luo, W. Yang, and H. Li, “A multilevel adaptive finite element algorithm for bioluminescence tomography,” Opt. Express 14, 8211–8223 (2006).

[CrossRef]

H. Li, J. Tian, F. Zhu, W. Cong, L. V. Wang, E. A. Hoffman, and G. Wang, “A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with Monte Carlo method,” Acad. Radiol. 11, 1029–1038 (2004).

G. Wang, Y. Li, and M. Jiang, “Uniqueness theorems in bioluminescence tomography,” Med. Phys. 31, 2289–2299 (2004).

[CrossRef]

T. F. Coleman and Y. Li, “A reflective newton method for minimizing a quadratic function subject to bounds on some of the variables,” SIAM J. Optim. 6, 1040–1058 (1996).

[CrossRef]

J. Feng, C. Qin, K. Jia, D. Han, K. Liu, S. Zhu, X. Yang, and J. Tian, “An adaptive regularization parameter choice strategy for multi-spectral bioluminescence tomography,” Med. Phys. 38, 5933–5944 (2011).

[CrossRef]

K. Liu, J. Tian, C. Qin, X. Yang, S. Zhu, D. Han, and P. Wu, “Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models,” J. Biomed. Opt. 16, 046016 (2011).

G. C. Kagadis, G. Loudos, K. Katsanos, S. G. Langer, and G. C. Nikiforidis, “In vivo small animal imaging: current status and future prospects,” Med. Phys. 37, 6421–6442 (2010).

[CrossRef]

Y. Lu, B. Zhu, H. Shen, J. C. Rasmussen, G. Wang, and E. M. Sevick-Muraca, “A parallel adaptive finite element simplified spherical harmonics approximation solver for frequency domain fluorescence molecular imaging,” Phys. Med. Biol. 55, 4625–4645 (2010).

[CrossRef]

J. Feng, K. Jia, G. Yan, S. Zhu, Y. Lv, and J. Tian, “An optimal permissible source region strategy for multispectral bioluminescence tomography,” Opt. Express 16, 15640–15654 (2008).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, W. Yang, C. Qin, and M. Xu, “Spectrally resolved bioluminescence tomography with adaptive finite element analysis: methodology and simulation,” Phys. Med. Biol. 52, 4497–4512 (2007).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, J. Luo, W. Yang, and H. Li, “A multilevel adaptive finite element algorithm for bioluminescence tomography,” Opt. Express 14, 8211–8223 (2006).

[CrossRef]

X. Ma, J. Tian, X. Yang, C. Qin, S. Zhu, and Z. Xue, “Research on liver tumor proliferation and angiogenesis based on multi-modality molecular imaging,” Acta Biophys. Sin. 27, 355–364 (2011).

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80, 043701 (2009).

[CrossRef]

A. J. Chaudhari, F. Darvas, J. R. Bading, R. A. Moats, P. S. Conti, D. J. Smith, S. R. Cherry, and R. M. Leahy, “Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging,” Phys. Med. Biol. 50, 5421–5441 (2005).

[CrossRef]

G. C. Kagadis, G. Loudos, K. Katsanos, S. G. Langer, and G. C. Nikiforidis, “In vivo small animal imaging: current status and future prospects,” Med. Phys. 37, 6421–6442 (2010).

[CrossRef]

M. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1, 586–597 (2007).

[CrossRef]

T. Goldstein, X. Bresson, and S. Osher, “Geometric applications of the split Bregman method: segmentation and surface reconstruction,” J. Sci. Comput. 45, 272–293 (2010).

[CrossRef]

T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009).

J. Cai, S. Osher, and Z. Shen, “Convergence of the linearized Bregman iteration for l1-norm minimization,” Math. Comput. 78, 2127–2136 (2009).

[CrossRef]

J. Cai, S. Osher, and Z. Shen, “Linearized Bregman iterations for compressed sensing,” Math. Comput. 78, 1515–1536(2009).

[CrossRef]

W. Yin, S. Osher, D. Goldfarb, and J. Darbon, “Bregman iterative algorithms for l1-minimization with applications to compressed sensing,” SIAM J. Imaging Sci. 1, 143–168 (2008).

S. Osher, M. Burger, D. Glodfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation based image restoration,” Multiscale Model. Simul. 4, 460–489 (2005).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60, 259–268 (1992).

[CrossRef]

H. Dehghani, S. C. Davis, S. Jiang, B. W. Pogue, K. D. Paulsen, and M. S. Patterson, “Spectrally resolved bioluminescence optical tomography,” Opt. Lett. 31, 365–367 (2006).

[CrossRef]

K. D. Paulsen and H. Jiang, “Enhanced frequency-domain optical image reconstruction in tissues through total-variation minimization,” Appl. Opt. 35, 3447–3458 (1996).

[CrossRef]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80, 043701 (2009).

[CrossRef]

H. Dehghani, S. C. Davis, and B. W. Pogue, “Spectrally resolved bioluminescence tomography using the reciprocity approach,” Med. Phys. 35, 4863–4871 (2008).

[CrossRef]

H. Dehghani, S. C. Davis, S. Jiang, B. W. Pogue, K. D. Paulsen, and M. S. Patterson, “Spectrally resolved bioluminescence optical tomography,” Opt. Lett. 31, 365–367 (2006).

[CrossRef]

A. D. Klose, B. J. Beattie, H. Dehghani, L. Vider, C. Le, V. Ponomarev, and R. Blasberg, “In vivo bioluminescence tomography with a blocking-off finite-difference SP3 method and MRI/CT coregistration,” Med. Phys. 37, 329–338 (2010).

[CrossRef]

K. Liu, J. Tian, C. Qin, X. Yang, S. Zhu, D. Han, and P. Wu, “Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models,” J. Biomed. Opt. 16, 046016 (2011).

X. Ma, J. Tian, X. Yang, C. Qin, S. Zhu, and Z. Xue, “Research on liver tumor proliferation and angiogenesis based on multi-modality molecular imaging,” Acta Biophys. Sin. 27, 355–364 (2011).

J. Feng, C. Qin, K. Jia, D. Han, K. Liu, S. Zhu, X. Yang, and J. Tian, “An adaptive regularization parameter choice strategy for multi-spectral bioluminescence tomography,” Med. Phys. 38, 5933–5944 (2011).

[CrossRef]

J. Feng, K. Jia, C. Qin, S. Zhu, X. Yang, and J. Tian, “Sparse Bayesian reconstruction method for multispectral bioluminescence tomography,” Chin. Opt. Lett. 8, 1010–1014 (2010).

[CrossRef]

J. Feng, K. Jia, C. Qin, G. Yan, X. Zhang, J. Liu, and J. Tian, “3D bioluminescence tomography based on Bayesian approach,” Opt. Express 17, 16834–16848 (2009).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, W. Yang, C. Qin, and M. Xu, “Spectrally resolved bioluminescence tomography with adaptive finite element analysis: methodology and simulation,” Phys. Med. Biol. 52, 4497–4512 (2007).

[CrossRef]

G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: a computer simulation feasibility study,” Phys. Med. Biol. 50, 4225–4241 (2005).

[CrossRef]

Y. Lu, B. Zhu, H. Shen, J. C. Rasmussen, G. Wang, and E. M. Sevick-Muraca, “A parallel adaptive finite element simplified spherical harmonics approximation solver for frequency domain fluorescence molecular imaging,” Phys. Med. Biol. 55, 4625–4645 (2010).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60, 259–268 (1992).

[CrossRef]

Y. Lu, B. Zhu, H. Shen, J. C. Rasmussen, G. Wang, and E. M. Sevick-Muraca, “A parallel adaptive finite element simplified spherical harmonics approximation solver for frequency domain fluorescence molecular imaging,” Phys. Med. Biol. 55, 4625–4645 (2010).

[CrossRef]

Y. Lu, B. Zhu, H. Shen, J. C. Rasmussen, G. Wang, and E. M. Sevick-Muraca, “A parallel adaptive finite element simplified spherical harmonics approximation solver for frequency domain fluorescence molecular imaging,” Phys. Med. Biol. 55, 4625–4645 (2010).

[CrossRef]

J. Cai, S. Osher, and Z. Shen, “Linearized Bregman iterations for compressed sensing,” Math. Comput. 78, 1515–1536(2009).

[CrossRef]

J. Cai, S. Osher, and Z. Shen, “Convergence of the linearized Bregman iteration for l1-norm minimization,” Math. Comput. 78, 2127–2136 (2009).

[CrossRef]

A. J. Chaudhari, F. Darvas, J. R. Bading, R. A. Moats, P. S. Conti, D. J. Smith, S. R. Cherry, and R. M. Leahy, “Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging,” Phys. Med. Biol. 50, 5421–5441 (2005).

[CrossRef]

K. Liu, J. Tian, C. Qin, X. Yang, S. Zhu, D. Han, and P. Wu, “Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models,” J. Biomed. Opt. 16, 046016 (2011).

X. Ma, J. Tian, X. Yang, C. Qin, S. Zhu, and Z. Xue, “Research on liver tumor proliferation and angiogenesis based on multi-modality molecular imaging,” Acta Biophys. Sin. 27, 355–364 (2011).

J. Feng, C. Qin, K. Jia, D. Han, K. Liu, S. Zhu, X. Yang, and J. Tian, “An adaptive regularization parameter choice strategy for multi-spectral bioluminescence tomography,” Med. Phys. 38, 5933–5944 (2011).

[CrossRef]

J. Feng, K. Jia, C. Qin, S. Zhu, X. Yang, and J. Tian, “Sparse Bayesian reconstruction method for multispectral bioluminescence tomography,” Chin. Opt. Lett. 8, 1010–1014 (2010).

[CrossRef]

J. Feng, K. Jia, C. Qin, G. Yan, X. Zhang, J. Liu, and J. Tian, “3D bioluminescence tomography based on Bayesian approach,” Opt. Express 17, 16834–16848 (2009).

[CrossRef]

J. Feng, K. Jia, G. Yan, S. Zhu, Y. Lv, and J. Tian, “An optimal permissible source region strategy for multispectral bioluminescence tomography,” Opt. Express 16, 15640–15654 (2008).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, W. Yang, C. Qin, and M. Xu, “Spectrally resolved bioluminescence tomography with adaptive finite element analysis: methodology and simulation,” Phys. Med. Biol. 52, 4497–4512 (2007).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, J. Luo, W. Yang, and H. Li, “A multilevel adaptive finite element algorithm for bioluminescence tomography,” Opt. Express 14, 8211–8223 (2006).

[CrossRef]

H. Li, J. Tian, F. Zhu, W. Cong, L. V. Wang, E. A. Hoffman, and G. Wang, “A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with Monte Carlo method,” Acad. Radiol. 11, 1029–1038 (2004).

J. K. Willmann, N. van Bruggen, L. M. Dinkelborg, and S. S. Gambhir, “Molecular imaging in drug development,” Nat. Rev. Drug Discov. 7, 591–607 (2008).

[CrossRef]

A. D. Klose, B. J. Beattie, H. Dehghani, L. Vider, C. Le, V. Ponomarev, and R. Blasberg, “In vivo bioluminescence tomography with a blocking-off finite-difference SP3 method and MRI/CT coregistration,” Med. Phys. 37, 329–338 (2010).

[CrossRef]

Y. Lu, B. Zhu, H. Shen, J. C. Rasmussen, G. Wang, and E. M. Sevick-Muraca, “A parallel adaptive finite element simplified spherical harmonics approximation solver for frequency domain fluorescence molecular imaging,” Phys. Med. Biol. 55, 4625–4645 (2010).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, W. Yang, C. Qin, and M. Xu, “Spectrally resolved bioluminescence tomography with adaptive finite element analysis: methodology and simulation,” Phys. Med. Biol. 52, 4497–4512 (2007).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, J. Luo, W. Yang, and H. Li, “A multilevel adaptive finite element algorithm for bioluminescence tomography,” Opt. Express 14, 8211–8223 (2006).

[CrossRef]

H. Li, J. Tian, F. Zhu, W. Cong, L. V. Wang, E. A. Hoffman, and G. Wang, “A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with Monte Carlo method,” Acad. Radiol. 11, 1029–1038 (2004).

G. Wang, Y. Li, and M. Jiang, “Uniqueness theorems in bioluminescence tomography,” Med. Phys. 31, 2289–2299 (2004).

[CrossRef]

H. Li, J. Tian, F. Zhu, W. Cong, L. V. Wang, E. A. Hoffman, and G. Wang, “A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with Monte Carlo method,” Acad. Radiol. 11, 1029–1038 (2004).

J. K. Willmann, N. van Bruggen, L. M. Dinkelborg, and S. S. Gambhir, “Molecular imaging in drug development,” Nat. Rev. Drug Discov. 7, 591–607 (2008).

[CrossRef]

T. Chan and C. K. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process. 7, 370–375 (1998).

[CrossRef]

M. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1, 586–597 (2007).

[CrossRef]

K. Liu, J. Tian, C. Qin, X. Yang, S. Zhu, D. Han, and P. Wu, “Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models,” J. Biomed. Opt. 16, 046016 (2011).

S. Osher, M. Burger, D. Glodfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation based image restoration,” Multiscale Model. Simul. 4, 460–489 (2005).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, W. Yang, C. Qin, and M. Xu, “Spectrally resolved bioluminescence tomography with adaptive finite element analysis: methodology and simulation,” Phys. Med. Biol. 52, 4497–4512 (2007).

[CrossRef]

X. Ma, J. Tian, X. Yang, C. Qin, S. Zhu, and Z. Xue, “Research on liver tumor proliferation and angiogenesis based on multi-modality molecular imaging,” Acta Biophys. Sin. 27, 355–364 (2011).

J. Feng, K. Jia, C. Qin, G. Yan, X. Zhang, J. Liu, and J. Tian, “3D bioluminescence tomography based on Bayesian approach,” Opt. Express 17, 16834–16848 (2009).

[CrossRef]

J. Feng, K. Jia, G. Yan, S. Zhu, Y. Lv, and J. Tian, “An optimal permissible source region strategy for multispectral bioluminescence tomography,” Opt. Express 16, 15640–15654 (2008).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, W. Yang, C. Qin, and M. Xu, “Spectrally resolved bioluminescence tomography with adaptive finite element analysis: methodology and simulation,” Phys. Med. Biol. 52, 4497–4512 (2007).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, J. Luo, W. Yang, and H. Li, “A multilevel adaptive finite element algorithm for bioluminescence tomography,” Opt. Express 14, 8211–8223 (2006).

[CrossRef]

J. Feng, C. Qin, K. Jia, D. Han, K. Liu, S. Zhu, X. Yang, and J. Tian, “An adaptive regularization parameter choice strategy for multi-spectral bioluminescence tomography,” Med. Phys. 38, 5933–5944 (2011).

[CrossRef]

X. Ma, J. Tian, X. Yang, C. Qin, S. Zhu, and Z. Xue, “Research on liver tumor proliferation and angiogenesis based on multi-modality molecular imaging,” Acta Biophys. Sin. 27, 355–364 (2011).

K. Liu, J. Tian, C. Qin, X. Yang, S. Zhu, D. Han, and P. Wu, “Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models,” J. Biomed. Opt. 16, 046016 (2011).

J. Feng, K. Jia, C. Qin, S. Zhu, X. Yang, and J. Tian, “Sparse Bayesian reconstruction method for multispectral bioluminescence tomography,” Chin. Opt. Lett. 8, 1010–1014 (2010).

[CrossRef]

W. Yin, S. Osher, D. Goldfarb, and J. Darbon, “Bregman iterative algorithms for l1-minimization with applications to compressed sensing,” SIAM J. Imaging Sci. 1, 143–168 (2008).

S. Osher, M. Burger, D. Glodfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation based image restoration,” Multiscale Model. Simul. 4, 460–489 (2005).

[CrossRef]

Y. Lu, B. Zhu, H. Shen, J. C. Rasmussen, G. Wang, and E. M. Sevick-Muraca, “A parallel adaptive finite element simplified spherical harmonics approximation solver for frequency domain fluorescence molecular imaging,” Phys. Med. Biol. 55, 4625–4645 (2010).

[CrossRef]

H. Li, J. Tian, F. Zhu, W. Cong, L. V. Wang, E. A. Hoffman, and G. Wang, “A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with Monte Carlo method,” Acad. Radiol. 11, 1029–1038 (2004).

J. Feng, C. Qin, K. Jia, D. Han, K. Liu, S. Zhu, X. Yang, and J. Tian, “An adaptive regularization parameter choice strategy for multi-spectral bioluminescence tomography,” Med. Phys. 38, 5933–5944 (2011).

[CrossRef]

X. Ma, J. Tian, X. Yang, C. Qin, S. Zhu, and Z. Xue, “Research on liver tumor proliferation and angiogenesis based on multi-modality molecular imaging,” Acta Biophys. Sin. 27, 355–364 (2011).

K. Liu, J. Tian, C. Qin, X. Yang, S. Zhu, D. Han, and P. Wu, “Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models,” J. Biomed. Opt. 16, 046016 (2011).

J. Feng, K. Jia, C. Qin, S. Zhu, X. Yang, and J. Tian, “Sparse Bayesian reconstruction method for multispectral bioluminescence tomography,” Chin. Opt. Lett. 8, 1010–1014 (2010).

[CrossRef]

J. Feng, K. Jia, G. Yan, S. Zhu, Y. Lv, and J. Tian, “An optimal permissible source region strategy for multispectral bioluminescence tomography,” Opt. Express 16, 15640–15654 (2008).

[CrossRef]

H. Li, J. Tian, F. Zhu, W. Cong, L. V. Wang, E. A. Hoffman, and G. Wang, “A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with Monte Carlo method,” Acad. Radiol. 11, 1029–1038 (2004).

X. Ma, J. Tian, X. Yang, C. Qin, S. Zhu, and Z. Xue, “Research on liver tumor proliferation and angiogenesis based on multi-modality molecular imaging,” Acta Biophys. Sin. 27, 355–364 (2011).

C. H. Contag and M. H. Bachmann, “Advances in in vivo bioluminescence imaging of gene expression,” Annu. Rev. Biomed. Eng. 4, 235–260 (2002).

[CrossRef]

M. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1, 586–597 (2007).

[CrossRef]

T. Chan and C. K. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process. 7, 370–375 (1998).

[CrossRef]

K. Liu, J. Tian, C. Qin, X. Yang, S. Zhu, D. Han, and P. Wu, “Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models,” J. Biomed. Opt. 16, 046016 (2011).

A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision 20, 89–97 (2004).

[CrossRef]

T. Goldstein, X. Bresson, and S. Osher, “Geometric applications of the split Bregman method: segmentation and surface reconstruction,” J. Sci. Comput. 45, 272–293 (2010).

[CrossRef]

J. Cai, S. Osher, and Z. Shen, “Linearized Bregman iterations for compressed sensing,” Math. Comput. 78, 1515–1536(2009).

[CrossRef]

J. Cai, S. Osher, and Z. Shen, “Convergence of the linearized Bregman iteration for l1-norm minimization,” Math. Comput. 78, 2127–2136 (2009).

[CrossRef]

A. D. Klose, B. J. Beattie, H. Dehghani, L. Vider, C. Le, V. Ponomarev, and R. Blasberg, “In vivo bioluminescence tomography with a blocking-off finite-difference SP3 method and MRI/CT coregistration,” Med. Phys. 37, 329–338 (2010).

[CrossRef]

H. Dehghani, S. C. Davis, and B. W. Pogue, “Spectrally resolved bioluminescence tomography using the reciprocity approach,” Med. Phys. 35, 4863–4871 (2008).

[CrossRef]

G. Wang, Y. Li, and M. Jiang, “Uniqueness theorems in bioluminescence tomography,” Med. Phys. 31, 2289–2299 (2004).

[CrossRef]

J. Feng, C. Qin, K. Jia, D. Han, K. Liu, S. Zhu, X. Yang, and J. Tian, “An adaptive regularization parameter choice strategy for multi-spectral bioluminescence tomography,” Med. Phys. 38, 5933–5944 (2011).

[CrossRef]

G. C. Kagadis, G. Loudos, K. Katsanos, S. G. Langer, and G. C. Nikiforidis, “In vivo small animal imaging: current status and future prospects,” Med. Phys. 37, 6421–6442 (2010).

[CrossRef]

S. Osher, M. Burger, D. Glodfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation based image restoration,” Multiscale Model. Simul. 4, 460–489 (2005).

[CrossRef]

J. K. Willmann, N. van Bruggen, L. M. Dinkelborg, and S. S. Gambhir, “Molecular imaging in drug development,” Nat. Rev. Drug Discov. 7, 591–607 (2008).

[CrossRef]

H. Gao and H. Zhao, “Multilevel bioluminescence tomography based on radiative transfer equation Part 1: l1 regularization,” Opt. Express 18, 1854–1871 (2010).

[CrossRef]

H. Gao and H. Zhao, “Multilevel bioluminescence tomography based on radiative transfer equation Part 2: total variation and l1 data fidelity,” Opt. Express 18, 2894–2912 (2010).

[CrossRef]

J. Feng, K. Jia, G. Yan, S. Zhu, Y. Lv, and J. Tian, “An optimal permissible source region strategy for multispectral bioluminescence tomography,” Opt. Express 16, 15640–15654 (2008).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, J. Luo, W. Yang, and H. Li, “A multilevel adaptive finite element algorithm for bioluminescence tomography,” Opt. Express 14, 8211–8223 (2006).

[CrossRef]

J. Feng, K. Jia, C. Qin, G. Yan, X. Zhang, J. Liu, and J. Tian, “3D bioluminescence tomography based on Bayesian approach,” Opt. Express 17, 16834–16848 (2009).

[CrossRef]

Y. Lv, J. Tian, W. Cong, G. Wang, W. Yang, C. Qin, and M. Xu, “Spectrally resolved bioluminescence tomography with adaptive finite element analysis: methodology and simulation,” Phys. Med. Biol. 52, 4497–4512 (2007).

[CrossRef]

Y. Lu, B. Zhu, H. Shen, J. C. Rasmussen, G. Wang, and E. M. Sevick-Muraca, “A parallel adaptive finite element simplified spherical harmonics approximation solver for frequency domain fluorescence molecular imaging,” Phys. Med. Biol. 55, 4625–4645 (2010).

[CrossRef]

S. Ahn, A. J. Chaudhari, F. Darvas, C. A. Bouman, and R. M. Leahy, “Fast iterative image reconstruction methods for fully 3D multispectral bioluminescence tomography,” Phys. Med. Biol. 53, 3921–3942 (2008).

[CrossRef]

A. J. Chaudhari, F. Darvas, J. R. Bading, R. A. Moats, P. S. Conti, D. J. Smith, S. R. Cherry, and R. M. Leahy, “Hyperspectral and multispectral bioluminescence optical tomography for small animal imaging,” Phys. Med. Biol. 50, 5421–5441 (2005).

[CrossRef]

G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: a computer simulation feasibility study,” Phys. Med. Biol. 50, 4225–4241 (2005).

[CrossRef]

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60, 259–268 (1992).

[CrossRef]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80, 043701 (2009).

[CrossRef]

T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM J. Imaging Sci. 2, 323–343 (2009).

W. Yin, S. Osher, D. Goldfarb, and J. Darbon, “Bregman iterative algorithms for l1-minimization with applications to compressed sensing,” SIAM J. Imaging Sci. 1, 143–168 (2008).

T. F. Coleman and Y. Li, “A reflective newton method for minimizing a quadratic function subject to bounds on some of the variables,” SIAM J. Optim. 6, 1040–1058 (1996).

[CrossRef]