Abstract

Fluorescence molecular tomography (FMT) is a promising in vivo functional imaging modality in preclinical study. When solving the ill-posed FMT inverse problem, L1 regularization can preserve the details and reduce the noise in the reconstruction results effectively. Moreover, compared with the regular L1 regularization, reweighted L1 regularization is recently reported to improve the performance. In order to realize the reweighted L1 regularization for FMT, an adaptive support driven reweighted L1-regularization (ASDR-L1) algorithm is proposed in this work. This algorithm has two integral parts: an adaptive support estimate and the iteratively updated weights. In the iteratively reweighted L1-minimization sub-problem, different weights are equivalent to different regularization parameters at different locations. Thus, ASDR-L1 can be considered as a kind of spatially variant regularization methods for FMT. Physical phantom and in vivo mouse experiments were performed to validate the proposed algorithm. The results demonstrate that the proposed reweighted L1-reguarization algorithm can significantly improve the performance in terms of relative quantitation and spatial resolution.

© 2014 Optical Society of America

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2014 (5)

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
[Crossref] [PubMed]

J. Shi, F. Liu, G. Zhang, J. Luo, and J. Bai, “Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm,” J. Biomed. Opt. 19(4), 046018 (2014).
[Crossref] [PubMed]

D. Zhu and C. Li, “Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement,” Phys. Med. Biol. 59(12), 2901–2912 (2014).
[Crossref] [PubMed]

C. B. Shaw and P. K. Yalavarthy, “Performance evaluation of typical approximation algorithms for nonconvex Lp-minimization in diffuse optical tomography,” J. Opt. Soc. Am. A 31(4), 852–862 (2014).
[Crossref]

J. Shi, F. Liu, J. Luo, and J. Bai, “Depth compensation in fluorescence molecular tomography using an adaptive support driven reweighted L1-minimization algorithm,” Proc. SPIE 9216, 921603 (2014).
[Crossref]

2013 (1)

2012 (3)

2011 (1)

K. O. Vasquez, C. Casavant, and J. D. Peterson, “Quantitative whole body biodistribution of fluorescent-labeled agents by non-invasive tomographic imaging,” PLoS ONE 6(6), e20594 (2011).
[Crossref] [PubMed]

2010 (7)

J. C. Baritaux, K. Hassler, and M. Unser, “An efficient numerical method for general Lp regularization in fluorescence molecular tomography,” IEEE Trans. Med. Imaging 29(4), 1075–1087 (2010).
[Crossref] [PubMed]

Y. Wang and W. Yin, “Sparse signal reconstruction via iterative support detection,” SIAM Journal on Imaging Sciences 3(3), 462–491 (2010).
[Crossref]

D. Wipf and S. Nagarajan, “Iterative reweighted and methods for finding sparse solutions,” IEEE J. Sel. Top. Sig. Processing 4(2), 317–329 (2010).
[Crossref]

F. Liu, X. Liu, D. Wang, B. Zhang, and J. Bai, “A parallel excitation based fluorescence molecular tomography system for whole-body simultaneous imaging of small animals,” Ann. Biomed. Eng. 38(11), 3440–3448 (2010).
[Crossref] [PubMed]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

D. Wang, X. Liu, Y. Chen, and J. Bai, “In-vivo fluorescence molecular tomography based on optimal small animal surface reconstruction,” Chin. Opt. Lett. 8(1), 82–85 (2010).
[Crossref]

X. Chen, X. Gao, D. Chen, X. Ma, X. Zhao, M. Shen, X. Li, X. Qu, J. Liang, J. Ripoll, and J. Tian, “3D reconstruction of light flux distribution on arbitrary surfaces from 2D multi-photographic images,” Opt. Express 18(19), 19876–19893 (2010).
[Crossref] [PubMed]

2008 (2)

E. J. Candes, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighted L1 minimization,” Journal of Fourier Analysis and Applications 14(5–6), 877–905 (2008).
[Crossref]

J. Haller, D. Hyde, N. Deliolanis, R. de Kleine, M. Niedre, and V. Ntziachristos, “Visualization of pulmonary inflammation using noninvasive fluorescence molecular imaging,” J. Appl. Physiol. 104(3), 795–802 (2008).
[Crossref] [PubMed]

2007 (1)

2005 (3)

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[Crossref] [PubMed]

A. Soubret, J. Ripoll, and V. Ntziachristos, “Accuracy of fluorescent tomography in the presence of heterogeneities: study of the normalized Born ratio,” IEEE Trans. Med. Imaging 24(10), 1377–1386 (2005).
[Crossref] [PubMed]

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(17), 4225–4241 (2005).
[Crossref] [PubMed]

2004 (2)

V. Ntziachristos, E. A. Schellenberger, J. Ripoll, D. Yessayan, E. Graves, A. Bogdanov, L. Josephson, and R. Weissleder, “Visualization of antitumor treatment by means of fluorescence molecular tomography with an annexin V-Cy5.5 conjugate,” Proc. Natl. Acad. Sci. U.S.A. 101(33), 12294–12299 (2004).
[Crossref] [PubMed]

S. R. Cherry, “In vivo molecular and genomic imaging: new challenges for imaging physics,” Phys. Med. Biol. 49(3), R13–R48 (2004).
[Crossref] [PubMed]

2002 (1)

1999 (2)

Adibi, A.

Ahn, S.

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).
[Crossref] [PubMed]

Alexandrakis, G.

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(17), 4225–4241 (2005).
[Crossref] [PubMed]

Arridge, S. R.

S. R. Arridge, “Optical tomography in medical imaging,” Inverse Probl. 15(2), 41–93 (1999).
[Crossref]

Bai, J.

J. Shi, F. Liu, G. Zhang, J. Luo, and J. Bai, “Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm,” J. Biomed. Opt. 19(4), 046018 (2014).
[Crossref] [PubMed]

J. Shi, F. Liu, J. Luo, and J. Bai, “Depth compensation in fluorescence molecular tomography using an adaptive support driven reweighted L1-minimization algorithm,” Proc. SPIE 9216, 921603 (2014).
[Crossref]

J. Shi, B. Zhang, F. Liu, J. Luo, and J. Bai, “Efficient L1 regularization-based reconstruction for fluorescent molecular tomography using restarted nonlinear conjugate gradient,” Opt. Lett. 38(18), 3696–3699 (2013).
[Crossref] [PubMed]

F. Liu, M. Li, B. Zhang, J. Luo, and J. Bai, “Weighted depth compensation algorithm for fluorescence molecular tomography reconstruction,” Appl. Opt. 51(36), 8883–8892 (2012).
[Crossref] [PubMed]

D. Wang, X. Liu, Y. Chen, and J. Bai, “In-vivo fluorescence molecular tomography based on optimal small animal surface reconstruction,” Chin. Opt. Lett. 8(1), 82–85 (2010).
[Crossref]

F. Liu, X. Liu, D. Wang, B. Zhang, and J. Bai, “A parallel excitation based fluorescence molecular tomography system for whole-body simultaneous imaging of small animals,” Ann. Biomed. Eng. 38(11), 3440–3448 (2010).
[Crossref] [PubMed]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Baritaux, J. C.

J. C. Baritaux, K. Hassler, and M. Unser, “An efficient numerical method for general Lp regularization in fluorescence molecular tomography,” IEEE Trans. Med. Imaging 29(4), 1075–1087 (2010).
[Crossref] [PubMed]

Behrooz, A.

Bogdanov, A.

V. Ntziachristos, E. A. Schellenberger, J. Ripoll, D. Yessayan, E. Graves, A. Bogdanov, L. Josephson, and R. Weissleder, “Visualization of antitumor treatment by means of fluorescence molecular tomography with an annexin V-Cy5.5 conjugate,” Proc. Natl. Acad. Sci. U.S.A. 101(33), 12294–12299 (2004).
[Crossref] [PubMed]

Boyd, S. P.

E. J. Candes, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighted L1 minimization,” Journal of Fourier Analysis and Applications 14(5–6), 877–905 (2008).
[Crossref]

Candes, E. J.

E. J. Candes, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighted L1 minimization,” Journal of Fourier Analysis and Applications 14(5–6), 877–905 (2008).
[Crossref]

Casavant, C.

K. O. Vasquez, C. Casavant, and J. D. Peterson, “Quantitative whole body biodistribution of fluorescent-labeled agents by non-invasive tomographic imaging,” PLoS ONE 6(6), e20594 (2011).
[Crossref] [PubMed]

Chatziioannou, A. F.

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(17), 4225–4241 (2005).
[Crossref] [PubMed]

Chen, D.

Chen, X.

Chen, Y.

Cherry, S. R.

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).
[Crossref] [PubMed]

S. R. Cherry, “In vivo molecular and genomic imaging: new challenges for imaging physics,” Phys. Med. Biol. 49(3), R13–R48 (2004).
[Crossref] [PubMed]

Darne, C.

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
[Crossref] [PubMed]

de Kleine, R.

J. Haller, D. Hyde, N. Deliolanis, R. de Kleine, M. Niedre, and V. Ntziachristos, “Visualization of pulmonary inflammation using noninvasive fluorescence molecular imaging,” J. Appl. Physiol. 104(3), 795–802 (2008).
[Crossref] [PubMed]

Deliolanis, N.

J. Haller, D. Hyde, N. Deliolanis, R. de Kleine, M. Niedre, and V. Ntziachristos, “Visualization of pulmonary inflammation using noninvasive fluorescence molecular imaging,” J. Appl. Physiol. 104(3), 795–802 (2008).
[Crossref] [PubMed]

Dutta, J.

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).
[Crossref] [PubMed]

Eftekhar, A. A.

Gao, X.

Graves, E.

V. Ntziachristos, E. A. Schellenberger, J. Ripoll, D. Yessayan, E. Graves, A. Bogdanov, L. Josephson, and R. Weissleder, “Visualization of antitumor treatment by means of fluorescence molecular tomography with an annexin V-Cy5.5 conjugate,” Proc. Natl. Acad. Sci. U.S.A. 101(33), 12294–12299 (2004).
[Crossref] [PubMed]

Guo, X.

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Haller, J.

J. Haller, D. Hyde, N. Deliolanis, R. de Kleine, M. Niedre, and V. Ntziachristos, “Visualization of pulmonary inflammation using noninvasive fluorescence molecular imaging,” J. Appl. Physiol. 104(3), 795–802 (2008).
[Crossref] [PubMed]

Hassler, K.

J. C. Baritaux, K. Hassler, and M. Unser, “An efficient numerical method for general Lp regularization in fluorescence molecular tomography,” IEEE Trans. Med. Imaging 29(4), 1075–1087 (2010).
[Crossref] [PubMed]

Hu, G.

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Huang, J.

Hyde, D.

J. Haller, D. Hyde, N. Deliolanis, R. de Kleine, M. Niedre, and V. Ntziachristos, “Visualization of pulmonary inflammation using noninvasive fluorescence molecular imaging,” J. Appl. Physiol. 104(3), 795–802 (2008).
[Crossref] [PubMed]

Josephson, L.

V. Ntziachristos, E. A. Schellenberger, J. Ripoll, D. Yessayan, E. Graves, A. Bogdanov, L. Josephson, and R. Weissleder, “Visualization of antitumor treatment by means of fluorescence molecular tomography with an annexin V-Cy5.5 conjugate,” Proc. Natl. Acad. Sci. U.S.A. 101(33), 12294–12299 (2004).
[Crossref] [PubMed]

Leahy, R. M.

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).
[Crossref] [PubMed]

Li, C.

D. Zhu and C. Li, “Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement,” Phys. Med. Biol. 59(12), 2901–2912 (2014).
[Crossref] [PubMed]

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).
[Crossref] [PubMed]

Li, M.

Li, X.

Liang, J.

Liu, F.

J. Shi, F. Liu, G. Zhang, J. Luo, and J. Bai, “Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm,” J. Biomed. Opt. 19(4), 046018 (2014).
[Crossref] [PubMed]

J. Shi, F. Liu, J. Luo, and J. Bai, “Depth compensation in fluorescence molecular tomography using an adaptive support driven reweighted L1-minimization algorithm,” Proc. SPIE 9216, 921603 (2014).
[Crossref]

J. Shi, B. Zhang, F. Liu, J. Luo, and J. Bai, “Efficient L1 regularization-based reconstruction for fluorescent molecular tomography using restarted nonlinear conjugate gradient,” Opt. Lett. 38(18), 3696–3699 (2013).
[Crossref] [PubMed]

F. Liu, M. Li, B. Zhang, J. Luo, and J. Bai, “Weighted depth compensation algorithm for fluorescence molecular tomography reconstruction,” Appl. Opt. 51(36), 8883–8892 (2012).
[Crossref] [PubMed]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

F. Liu, X. Liu, D. Wang, B. Zhang, and J. Bai, “A parallel excitation based fluorescence molecular tomography system for whole-body simultaneous imaging of small animals,” Ann. Biomed. Eng. 38(11), 3440–3448 (2010).
[Crossref] [PubMed]

Liu, X.

F. Liu, X. Liu, D. Wang, B. Zhang, and J. Bai, “A parallel excitation based fluorescence molecular tomography system for whole-body simultaneous imaging of small animals,” Ann. Biomed. Eng. 38(11), 3440–3448 (2010).
[Crossref] [PubMed]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

D. Wang, X. Liu, Y. Chen, and J. Bai, “In-vivo fluorescence molecular tomography based on optimal small animal surface reconstruction,” Chin. Opt. Lett. 8(1), 82–85 (2010).
[Crossref]

Lu, Y.

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
[Crossref] [PubMed]

Luo, J.

J. Shi, F. Liu, J. Luo, and J. Bai, “Depth compensation in fluorescence molecular tomography using an adaptive support driven reweighted L1-minimization algorithm,” Proc. SPIE 9216, 921603 (2014).
[Crossref]

J. Shi, F. Liu, G. Zhang, J. Luo, and J. Bai, “Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm,” J. Biomed. Opt. 19(4), 046018 (2014).
[Crossref] [PubMed]

J. Shi, B. Zhang, F. Liu, J. Luo, and J. Bai, “Efficient L1 regularization-based reconstruction for fluorescent molecular tomography using restarted nonlinear conjugate gradient,” Opt. Lett. 38(18), 3696–3699 (2013).
[Crossref] [PubMed]

F. Liu, M. Li, B. Zhang, J. Luo, and J. Bai, “Weighted depth compensation algorithm for fluorescence molecular tomography reconstruction,” Appl. Opt. 51(36), 8883–8892 (2012).
[Crossref] [PubMed]

Ma, X.

McBride, T. O.

Mohajerani, P.

Nagarajan, S.

D. Wipf and S. Nagarajan, “Iterative reweighted and methods for finding sparse solutions,” IEEE J. Sel. Top. Sig. Processing 4(2), 317–329 (2010).
[Crossref]

Niedre, M.

J. Haller, D. Hyde, N. Deliolanis, R. de Kleine, M. Niedre, and V. Ntziachristos, “Visualization of pulmonary inflammation using noninvasive fluorescence molecular imaging,” J. Appl. Physiol. 104(3), 795–802 (2008).
[Crossref] [PubMed]

Nieto-Vesperinas, M.

Ntziachristos, V.

J. Haller, D. Hyde, N. Deliolanis, R. de Kleine, M. Niedre, and V. Ntziachristos, “Visualization of pulmonary inflammation using noninvasive fluorescence molecular imaging,” J. Appl. Physiol. 104(3), 795–802 (2008).
[Crossref] [PubMed]

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[Crossref] [PubMed]

A. Soubret, J. Ripoll, and V. Ntziachristos, “Accuracy of fluorescent tomography in the presence of heterogeneities: study of the normalized Born ratio,” IEEE Trans. Med. Imaging 24(10), 1377–1386 (2005).
[Crossref] [PubMed]

V. Ntziachristos, E. A. Schellenberger, J. Ripoll, D. Yessayan, E. Graves, A. Bogdanov, L. Josephson, and R. Weissleder, “Visualization of antitumor treatment by means of fluorescence molecular tomography with an annexin V-Cy5.5 conjugate,” Proc. Natl. Acad. Sci. U.S.A. 101(33), 12294–12299 (2004).
[Crossref] [PubMed]

J. Ripoll, M. Nieto-Vesperinas, R. Weissleder, and V. Ntziachristos, “Fast analytical approximation for arbitrary geometries in diffuse optical tomography,” Opt. Lett. 27(7), 527–529 (2002).
[Crossref] [PubMed]

Osterberg, U. L.

Paulsen, K. D.

Peterson, J. D.

K. O. Vasquez, C. Casavant, and J. D. Peterson, “Quantitative whole body biodistribution of fluorescent-labeled agents by non-invasive tomographic imaging,” PLoS ONE 6(6), e20594 (2011).
[Crossref] [PubMed]

Pogue, B. W.

Prewitt, J.

Qu, X.

Rannou, F. R.

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(17), 4225–4241 (2005).
[Crossref] [PubMed]

Ripoll, J.

X. Chen, X. Gao, D. Chen, X. Ma, X. Zhao, M. Shen, X. Li, X. Qu, J. Liang, J. Ripoll, and J. Tian, “3D reconstruction of light flux distribution on arbitrary surfaces from 2D multi-photographic images,” Opt. Express 18(19), 19876–19893 (2010).
[Crossref] [PubMed]

A. Soubret, J. Ripoll, and V. Ntziachristos, “Accuracy of fluorescent tomography in the presence of heterogeneities: study of the normalized Born ratio,” IEEE Trans. Med. Imaging 24(10), 1377–1386 (2005).
[Crossref] [PubMed]

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[Crossref] [PubMed]

V. Ntziachristos, E. A. Schellenberger, J. Ripoll, D. Yessayan, E. Graves, A. Bogdanov, L. Josephson, and R. Weissleder, “Visualization of antitumor treatment by means of fluorescence molecular tomography with an annexin V-Cy5.5 conjugate,” Proc. Natl. Acad. Sci. U.S.A. 101(33), 12294–12299 (2004).
[Crossref] [PubMed]

J. Ripoll, M. Nieto-Vesperinas, R. Weissleder, and V. Ntziachristos, “Fast analytical approximation for arbitrary geometries in diffuse optical tomography,” Opt. Lett. 27(7), 527–529 (2002).
[Crossref] [PubMed]

Schellenberger, E. A.

V. Ntziachristos, E. A. Schellenberger, J. Ripoll, D. Yessayan, E. Graves, A. Bogdanov, L. Josephson, and R. Weissleder, “Visualization of antitumor treatment by means of fluorescence molecular tomography with an annexin V-Cy5.5 conjugate,” Proc. Natl. Acad. Sci. U.S.A. 101(33), 12294–12299 (2004).
[Crossref] [PubMed]

Sevick-Muraca, E. M.

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
[Crossref] [PubMed]

Shaw, C. B.

Shen, M.

Shi, J.

J. Shi, F. Liu, J. Luo, and J. Bai, “Depth compensation in fluorescence molecular tomography using an adaptive support driven reweighted L1-minimization algorithm,” Proc. SPIE 9216, 921603 (2014).
[Crossref]

J. Shi, F. Liu, G. Zhang, J. Luo, and J. Bai, “Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm,” J. Biomed. Opt. 19(4), 046018 (2014).
[Crossref] [PubMed]

J. Shi, B. Zhang, F. Liu, J. Luo, and J. Bai, “Efficient L1 regularization-based reconstruction for fluorescent molecular tomography using restarted nonlinear conjugate gradient,” Opt. Lett. 38(18), 3696–3699 (2013).
[Crossref] [PubMed]

Soubret, A.

A. Soubret, J. Ripoll, and V. Ntziachristos, “Accuracy of fluorescent tomography in the presence of heterogeneities: study of the normalized Born ratio,” IEEE Trans. Med. Imaging 24(10), 1377–1386 (2005).
[Crossref] [PubMed]

Tian, F.

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Tian, J.

Unser, M.

J. C. Baritaux, K. Hassler, and M. Unser, “An efficient numerical method for general Lp regularization in fluorescence molecular tomography,” IEEE Trans. Med. Imaging 29(4), 1075–1087 (2010).
[Crossref] [PubMed]

Vasquez, K. O.

K. O. Vasquez, C. Casavant, and J. D. Peterson, “Quantitative whole body biodistribution of fluorescent-labeled agents by non-invasive tomographic imaging,” PLoS ONE 6(6), e20594 (2011).
[Crossref] [PubMed]

Wakin, M. B.

E. J. Candes, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighted L1 minimization,” Journal of Fourier Analysis and Applications 14(5–6), 877–905 (2008).
[Crossref]

Wang, D.

F. Liu, X. Liu, D. Wang, B. Zhang, and J. Bai, “A parallel excitation based fluorescence molecular tomography system for whole-body simultaneous imaging of small animals,” Ann. Biomed. Eng. 38(11), 3440–3448 (2010).
[Crossref] [PubMed]

D. Wang, X. Liu, Y. Chen, and J. Bai, “In-vivo fluorescence molecular tomography based on optimal small animal surface reconstruction,” Chin. Opt. Lett. 8(1), 82–85 (2010).
[Crossref]

Wang, L. V.

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[Crossref] [PubMed]

Wang, X.

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Wang, Y.

Y. Wang and W. Yin, “Sparse signal reconstruction via iterative support detection,” SIAM Journal on Imaging Sciences 3(3), 462–491 (2010).
[Crossref]

Weissleder, R.

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[Crossref] [PubMed]

V. Ntziachristos, E. A. Schellenberger, J. Ripoll, D. Yessayan, E. Graves, A. Bogdanov, L. Josephson, and R. Weissleder, “Visualization of antitumor treatment by means of fluorescence molecular tomography with an annexin V-Cy5.5 conjugate,” Proc. Natl. Acad. Sci. U.S.A. 101(33), 12294–12299 (2004).
[Crossref] [PubMed]

J. Ripoll, M. Nieto-Vesperinas, R. Weissleder, and V. Ntziachristos, “Fast analytical approximation for arbitrary geometries in diffuse optical tomography,” Opt. Lett. 27(7), 527–529 (2002).
[Crossref] [PubMed]

Wipf, D.

D. Wipf and S. Nagarajan, “Iterative reweighted and methods for finding sparse solutions,” IEEE J. Sel. Top. Sig. Processing 4(2), 317–329 (2010).
[Crossref]

Yalavarthy, P. K.

Yessayan, D.

V. Ntziachristos, E. A. Schellenberger, J. Ripoll, D. Yessayan, E. Graves, A. Bogdanov, L. Josephson, and R. Weissleder, “Visualization of antitumor treatment by means of fluorescence molecular tomography with an annexin V-Cy5.5 conjugate,” Proc. Natl. Acad. Sci. U.S.A. 101(33), 12294–12299 (2004).
[Crossref] [PubMed]

Yin, W.

Y. Wang and W. Yin, “Sparse signal reconstruction via iterative support detection,” SIAM Journal on Imaging Sciences 3(3), 462–491 (2010).
[Crossref]

Zhang, B.

J. Shi, B. Zhang, F. Liu, J. Luo, and J. Bai, “Efficient L1 regularization-based reconstruction for fluorescent molecular tomography using restarted nonlinear conjugate gradient,” Opt. Lett. 38(18), 3696–3699 (2013).
[Crossref] [PubMed]

F. Liu, M. Li, B. Zhang, J. Luo, and J. Bai, “Weighted depth compensation algorithm for fluorescence molecular tomography reconstruction,” Appl. Opt. 51(36), 8883–8892 (2012).
[Crossref] [PubMed]

F. Liu, X. Liu, D. Wang, B. Zhang, and J. Bai, “A parallel excitation based fluorescence molecular tomography system for whole-body simultaneous imaging of small animals,” Ann. Biomed. Eng. 38(11), 3440–3448 (2010).
[Crossref] [PubMed]

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

Zhang, G.

J. Shi, F. Liu, G. Zhang, J. Luo, and J. Bai, “Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm,” J. Biomed. Opt. 19(4), 046018 (2014).
[Crossref] [PubMed]

Zhao, X.

Zhou, H. M.

Zhu, D.

D. Zhu and C. Li, “Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement,” Phys. Med. Biol. 59(12), 2901–2912 (2014).
[Crossref] [PubMed]

Ann. Biomed. Eng. (1)

F. Liu, X. Liu, D. Wang, B. Zhang, and J. Bai, “A parallel excitation based fluorescence molecular tomography system for whole-body simultaneous imaging of small animals,” Ann. Biomed. Eng. 38(11), 3440–3448 (2010).
[Crossref] [PubMed]

Appl. Opt. (4)

Chin. Opt. Lett. (1)

IEEE J. Sel. Top. Sig. Processing (1)

D. Wipf and S. Nagarajan, “Iterative reweighted and methods for finding sparse solutions,” IEEE J. Sel. Top. Sig. Processing 4(2), 317–329 (2010).
[Crossref]

IEEE Trans. Biomed. Eng. (1)

X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, “A combined fluorescence and microcomputed tomography system for small animal imaging,” IEEE Trans. Biomed. Eng. 57(12), 2876–2883 (2010).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (2)

A. Soubret, J. Ripoll, and V. Ntziachristos, “Accuracy of fluorescent tomography in the presence of heterogeneities: study of the normalized Born ratio,” IEEE Trans. Med. Imaging 24(10), 1377–1386 (2005).
[Crossref] [PubMed]

J. C. Baritaux, K. Hassler, and M. Unser, “An efficient numerical method for general Lp regularization in fluorescence molecular tomography,” IEEE Trans. Med. Imaging 29(4), 1075–1087 (2010).
[Crossref] [PubMed]

Inverse Probl. (1)

S. R. Arridge, “Optical tomography in medical imaging,” Inverse Probl. 15(2), 41–93 (1999).
[Crossref]

J. Appl. Physiol. (1)

J. Haller, D. Hyde, N. Deliolanis, R. de Kleine, M. Niedre, and V. Ntziachristos, “Visualization of pulmonary inflammation using noninvasive fluorescence molecular imaging,” J. Appl. Physiol. 104(3), 795–802 (2008).
[Crossref] [PubMed]

J. Biomed. Opt. (1)

J. Shi, F. Liu, G. Zhang, J. Luo, and J. Bai, “Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm,” J. Biomed. Opt. 19(4), 046018 (2014).
[Crossref] [PubMed]

J. Opt. Soc. Am. A (1)

Journal of Fourier Analysis and Applications (1)

E. J. Candes, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighted L1 minimization,” Journal of Fourier Analysis and Applications 14(5–6), 877–905 (2008).
[Crossref]

Nat. Biotechnol. (1)

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[Crossref] [PubMed]

Opt. Express (1)

Opt. Lett. (2)

Phys. Med. Biol. (5)

C. Darne, Y. Lu, and E. M. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update,” Phys. Med. Biol. 59(1), R1–R64 (2014).
[Crossref] [PubMed]

S. R. Cherry, “In vivo molecular and genomic imaging: new challenges for imaging physics,” Phys. Med. Biol. 49(3), R13–R48 (2004).
[Crossref] [PubMed]

D. Zhu and C. Li, “Nonconvex regularizations in fluorescence molecular tomography for sparsity enhancement,” Phys. Med. Biol. 59(12), 2901–2912 (2014).
[Crossref] [PubMed]

J. Dutta, S. Ahn, C. Li, S. R. Cherry, and R. M. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57(6), 1459–1476 (2012).
[Crossref] [PubMed]

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(17), 4225–4241 (2005).
[Crossref] [PubMed]

PLoS ONE (1)

K. O. Vasquez, C. Casavant, and J. D. Peterson, “Quantitative whole body biodistribution of fluorescent-labeled agents by non-invasive tomographic imaging,” PLoS ONE 6(6), e20594 (2011).
[Crossref] [PubMed]

Proc. Natl. Acad. Sci. U.S.A. (1)

V. Ntziachristos, E. A. Schellenberger, J. Ripoll, D. Yessayan, E. Graves, A. Bogdanov, L. Josephson, and R. Weissleder, “Visualization of antitumor treatment by means of fluorescence molecular tomography with an annexin V-Cy5.5 conjugate,” Proc. Natl. Acad. Sci. U.S.A. 101(33), 12294–12299 (2004).
[Crossref] [PubMed]

Proc. SPIE (1)

J. Shi, F. Liu, J. Luo, and J. Bai, “Depth compensation in fluorescence molecular tomography using an adaptive support driven reweighted L1-minimization algorithm,” Proc. SPIE 9216, 921603 (2014).
[Crossref]

SIAM Journal on Imaging Sciences (1)

Y. Wang and W. Yin, “Sparse signal reconstruction via iterative support detection,” SIAM Journal on Imaging Sciences 3(3), 462–491 (2010).
[Crossref]

Other (1)

H. Mansour and O. Yilmaz, “Support driven reweighted L1 minimization,” IEEE International Conference on Speech and Signal Processing (ICASSP), IEEE Press: Piscataway NJ, 2012: 3309–3312.

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Figures (7)

Fig. 1
Fig. 1 Sketch of the free-space full-angle FMT system.
Fig. 2
Fig. 2 Reconstruction results of the physical phantom experiments with an EED of 0.6 cm between two fluorophores. (a) Cross section of the true fluorophore distribution at the Z = 2.6 cm plane, (b) 3D rendering of true fluorescent targets, where the red circle denotes the position of the cross section. Slice (c) and stereo (d) are the reconstruction results obtained from Tikhonov. Slice (e) and stereo (f) correspond to re-L1-NCG. Slice (g) and stereo (h) correspond to ASDR-L1. Each image was normalized by its maximum of the reconstruction results.
Fig. 3
Fig. 3 Reconstruction results of the physical phantom experiment with an EED of 0.4 cm between two fluorophores. (a) Cross section of the true fluorophore distribution at the Z = 2.6 cm plane, (b) 3D rendering of true fluorescent targets, where the red circle denotes the position of the cross section. Slice (c) and stereo (d) are the reconstruction results obtained from Tikhonov. Slice (e) and stereo (f) correspond to re-L1-NCG. Slice (g) and stereo (h) correspond to ASDR-L1. Each image was normalized by its maximum of the reconstruction results.
Fig. 4
Fig. 4 Intensity profiles along the Y axis in the cross sections of (a) Figs. 2 and (b) 3.
Fig. 5
Fig. 5 Mouse experiments with two implanted fluorescence targets. (a) Fusion of the white-light and fluorescence images. Region between the two red dotted lines is used for reconstruction. (b) 3D solid geometry of the abdomen.
Fig. 6
Fig. 6 Reconstruction results of the mouse experiments. Columns (a)-(c) represent the slice and stereo results reconstructed using Tikhonov, re-L1-NCG and ASDR-L1, respectively. Each slice is composed of the reconstructed FMT slice fused with CT slice. Each image was normalized by its maximum of the reconstruction results.
Fig. 7
Fig. 7 Intensity profiles along the white dotted lines in the cross sections of Fig. 6.

Tables (2)

Tables Icon

Table 1 Relative quantitation indexes calculated from Eq. (9) for the physical phantom experiments.

Tables Icon

Table 2 Relative quantitation indexes calculated from Eq. (9) for the mouse experiments.

Equations (9)

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ϕ f ( r d , r s )= ϕ em ( r d , r s ) ϕ ex ( r d , r s )
ϕ f ( r d , r s )= S 0 υ G( r s , r d , λ 1 ) D f G( r d ,r, λ 2 ) x(r)G( r s ,r, λ 1 ) d 3 r
I k
min X0 WX Φ f 2 2 +λ X 0
min X0 WX Φ f 2 2 +λ R k X 1
X reL1 =arg min X0 (f(X)= W new X Φ fnew 2 2 +λ R k X 1 )
W nor (i,j)={ W i 2 1 i=j 0 ij
W new =W W nor ; Φ fnew = Φ f /max( Φ f )
RQ= | x 1 max x 2 max | | x 1 max + x 2 max | /2

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