Abstract

The purpose of this study is to propose a strategy for organ reconstruction in fluorescence molecular tomography (FMT) without prior information from other imaging modalities, and to overcome the high cost and ionizing radiation caused by the traditional structural prior strategy. The proposed strategy is designed as an iterative architecture to solve the inverse problem of FMT. In each iteration, a short time Fourier transform (STFT) based algorithm is used to extract the self-prior information in the space-frequency energy spectrum with the assumption that the regions with higher fluorescence concentration have larger energy intensity, then the cost function of the inverse problem is modified by the self-prior information, and lastly an iterative Laplacian regularization algorithm is conducted to solve the updated inverse problem and obtains the reconstruction results. Simulations and in vivo experiments on liver reconstruction are carried out to test the performance of the self-prior strategy on organ reconstruction. The organ reconstruction results obtained by the proposed self-prior strategy are closer to the ground truth than those obtained by the iterative Tikhonov regularization (ITKR) method (traditional non-prior strategy). Significant improvements are shown in the evaluation indexes of relative locational error (RLE), relative error (RE) and contrast-to-noise ratio (CNR). The self-prior strategy improves the organ reconstruction results compared with the non-prior strategy and also overcomes the shortcomings of the traditional structural prior strategy. Various applications such as metabolic imaging and pharmacokinetic study can be aided by this strategy.

© 2017 Optical Society of America

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2016 (2)

2015 (1)

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[Crossref] [PubMed]

2014 (5)

G. Zhang, F. Liu, H. Pu, W. He, J. Luo, and J. Bai, “A Direct Method With Structural Priors for Imaging Pharmacokinetic Parameters in Dynamic Fluorescence Molecular Tomography,” IEEE Trans. Biomed. Eng. 61(3), 986–990 (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).

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]

Y. Zhang, B. Zhang, F. Liu, J. Luo, and J. Bai, “In vivo tomographic imaging with fluorescence and MRI using tumor-targeted dual-labeled nanoparticles,” Int. J. Nanomedicine 9, 33–41 (2014).
[PubMed]

W. He, G. Zhang, H. Pu, F. Liu, X. Cao, J. Luo, and J. Bai, “Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography,” J. Biomed. Opt. 19(5), 056012 (2014).
[Crossref] [PubMed]

2013 (5)

S. C. Davis, K. S. Samkoe, K. M. Tichauer, K. J. Sexton, J. R. Gunn, S. J. Deharvengt, T. Hasan, and B. W. Pogue, “Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(22), 9025–9030 (2013).
[Crossref] [PubMed]

N. C. Deliolanis and V. Ntziachristos, “Fluorescence molecular tomography of brain tumors in mice,” Cold Spring Harb. Protoc. 2013(5), 438–443 (2013).
[Crossref] [PubMed]

G. Zhang, F. Liu, B. Zhang, Y. He, J. Luo, and J. Bai, “Imaging of pharmacokinetic rates of indocyanine green in mouse liver with a hybrid fluorescence molecular tomography/x-ray computed tomography system,” J. Biomed. Opt. 18(4), 040505 (2013).
[Crossref] [PubMed]

J. Shi, X. Cao, F. Liu, B. Zhang, J. Luo, and J. Bai, “Greedy reconstruction algorithm for fluorescence molecular tomography by means of truncated singular value decomposition conversion,” J. Opt. Soc. Am. A 30(3), 437–447 (2013).
[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]

2012 (1)

F. Liu, X. Cao, W. He, J. Song, Z. Dai, B. Zhang, J. Luo, Y. Li, and J. Bai, “Monitoring of tumor response to cisplatin by subsurface fluorescence molecular tomography,” J. Biomed. Opt. 17(4), 040504 (2012).
[Crossref] [PubMed]

2010 (2)

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]

L. Cao and J. Peter, “Bayesian reconstruction strategy of fluorescence-mediated tomography using an integrated SPECT-CT-OT system,” Phys. Med. Biol. 55(9), 2693–2708 (2010).
[Crossref] [PubMed]

2008 (1)

S. C. Davis, B. W. Pogue, R. Springett, C. Leussler, P. Mazurkewitz, S. B. Tuttle, S. L. Gibbs-Strauss, S. S. Jiang, H. Dehghani, and K. D. Paulsen, “Magnetic resonance-coupled fluorescence tomography scanner for molecular imaging of tissue,” Rev. Sci. Instrum. 79(6), 064302 (2008).
[Crossref] [PubMed]

2007 (3)

1999 (1)

S. R. Arridge, “Optical tomography in medical imaging,” Inverse Probl. 15(2), R41–R93 (1999).
[Crossref]

1996 (1)

1995 (1)

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The Finite Element Method for the Propagation of Light in Scattering Media: Boundary and Source Conditions,” Med. Phys. 22(11), 1779–1792 (1995).
[Crossref] [PubMed]

1963 (1)

A. Tikhonov, “Solving ill-conditioned and singular linear systems: A tutorial on regularization,” Soviet Math. Dokl. 5, 1035–1038 (1963).

Arridge, S. R.

S. R. Arridge, “Optical tomography in medical imaging,” Inverse Probl. 15(2), R41–R93 (1999).
[Crossref]

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The Finite Element Method for the Propagation of Light in Scattering Media: Boundary and Source Conditions,” Med. Phys. 22(11), 1779–1792 (1995).
[Crossref] [PubMed]

Bai, J.

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[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).

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]

Y. Zhang, B. Zhang, F. Liu, J. Luo, and J. Bai, “In vivo tomographic imaging with fluorescence and MRI using tumor-targeted dual-labeled nanoparticles,” Int. J. Nanomedicine 9, 33–41 (2014).
[PubMed]

G. Zhang, F. Liu, H. Pu, W. He, J. Luo, and J. Bai, “A Direct Method With Structural Priors for Imaging Pharmacokinetic Parameters in Dynamic Fluorescence Molecular Tomography,” IEEE Trans. Biomed. Eng. 61(3), 986–990 (2014).
[Crossref] [PubMed]

W. He, G. Zhang, H. Pu, F. Liu, X. Cao, J. Luo, and J. Bai, “Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography,” J. Biomed. Opt. 19(5), 056012 (2014).
[Crossref] [PubMed]

J. Shi, X. Cao, F. Liu, B. Zhang, J. Luo, and J. Bai, “Greedy reconstruction algorithm for fluorescence molecular tomography by means of truncated singular value decomposition conversion,” J. Opt. Soc. Am. A 30(3), 437–447 (2013).
[Crossref] [PubMed]

G. Zhang, F. Liu, B. Zhang, Y. He, J. Luo, and J. Bai, “Imaging of pharmacokinetic rates of indocyanine green in mouse liver with a hybrid fluorescence molecular tomography/x-ray computed tomography system,” J. Biomed. Opt. 18(4), 040505 (2013).
[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, X. Cao, W. He, J. Song, Z. Dai, B. Zhang, J. Luo, Y. Li, and J. Bai, “Monitoring of tumor response to cisplatin by subsurface fluorescence molecular tomography,” J. Biomed. Opt. 17(4), 040504 (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]

Boas, D. A.

Cai, C.

Cao, L.

L. Cao and J. Peter, “Bayesian reconstruction strategy of fluorescence-mediated tomography using an integrated SPECT-CT-OT system,” Phys. Med. Biol. 55(9), 2693–2708 (2010).
[Crossref] [PubMed]

Cao, X.

W. He, G. Zhang, H. Pu, F. Liu, X. Cao, J. Luo, and J. Bai, “Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography,” J. Biomed. Opt. 19(5), 056012 (2014).
[Crossref] [PubMed]

J. Shi, X. Cao, F. Liu, B. Zhang, J. Luo, and J. Bai, “Greedy reconstruction algorithm for fluorescence molecular tomography by means of truncated singular value decomposition conversion,” J. Opt. Soc. Am. A 30(3), 437–447 (2013).
[Crossref] [PubMed]

F. Liu, X. Cao, W. He, J. Song, Z. Dai, B. Zhang, J. Luo, Y. Li, and J. Bai, “Monitoring of tumor response to cisplatin by subsurface fluorescence molecular tomography,” J. Biomed. Opt. 17(4), 040504 (2012).
[Crossref] [PubMed]

Carpenter, C. M.

Chance, B.

Chatziioannou, A. F.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: a 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

Chen, M.

Dai, Z.

F. Liu, X. Cao, W. He, J. Song, Z. Dai, B. Zhang, J. Luo, Y. Li, and J. Bai, “Monitoring of tumor response to cisplatin by subsurface fluorescence molecular tomography,” J. Biomed. Opt. 17(4), 040504 (2012).
[Crossref] [PubMed]

Davis, S. C.

S. C. Davis, K. S. Samkoe, K. M. Tichauer, K. J. Sexton, J. R. Gunn, S. J. Deharvengt, T. Hasan, and B. W. Pogue, “Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(22), 9025–9030 (2013).
[Crossref] [PubMed]

S. C. Davis, B. W. Pogue, R. Springett, C. Leussler, P. Mazurkewitz, S. B. Tuttle, S. L. Gibbs-Strauss, S. S. Jiang, H. Dehghani, and K. D. Paulsen, “Magnetic resonance-coupled fluorescence tomography scanner for molecular imaging of tissue,” Rev. Sci. Instrum. 79(6), 064302 (2008).
[Crossref] [PubMed]

S. C. Davis, H. Dehghani, J. Wang, S. Jiang, B. W. Pogue, and K. D. Paulsen, “Image-guided diffuse optical fluorescence tomography implemented with Laplacian-type regularization,” Opt. Express 15(7), 4066–4082 (2007).
[Crossref] [PubMed]

Deharvengt, S. J.

S. C. Davis, K. S. Samkoe, K. M. Tichauer, K. J. Sexton, J. R. Gunn, S. J. Deharvengt, T. Hasan, and B. W. Pogue, “Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(22), 9025–9030 (2013).
[Crossref] [PubMed]

Dehghani, H.

Deliolanis, N. C.

N. C. Deliolanis and V. Ntziachristos, “Fluorescence molecular tomography of brain tumors in mice,” Cold Spring Harb. Protoc. 2013(5), 438–443 (2013).
[Crossref] [PubMed]

Delpy, D. T.

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The Finite Element Method for the Propagation of Light in Scattering Media: Boundary and Source Conditions,” Med. Phys. 22(11), 1779–1792 (1995).
[Crossref] [PubMed]

Dogdas, B.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: a 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

Gibbs-Strauss, S. L.

S. C. Davis, B. W. Pogue, R. Springett, C. Leussler, P. Mazurkewitz, S. B. Tuttle, S. L. Gibbs-Strauss, S. S. Jiang, H. Dehghani, and K. D. Paulsen, “Magnetic resonance-coupled fluorescence tomography scanner for molecular imaging of tissue,” Rev. Sci. Instrum. 79(6), 064302 (2008).
[Crossref] [PubMed]

Guang, H.

Gunn, J. R.

S. C. Davis, K. S. Samkoe, K. M. Tichauer, K. J. Sexton, J. R. Gunn, S. J. Deharvengt, T. Hasan, and B. W. Pogue, “Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(22), 9025–9030 (2013).
[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]

Hasan, T.

S. C. Davis, K. S. Samkoe, K. M. Tichauer, K. J. Sexton, J. R. Gunn, S. J. Deharvengt, T. Hasan, and B. W. Pogue, “Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(22), 9025–9030 (2013).
[Crossref] [PubMed]

He, W.

W. He, G. Zhang, H. Pu, F. Liu, X. Cao, J. Luo, and J. Bai, “Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography,” J. Biomed. Opt. 19(5), 056012 (2014).
[Crossref] [PubMed]

G. Zhang, F. Liu, H. Pu, W. He, J. Luo, and J. Bai, “A Direct Method With Structural Priors for Imaging Pharmacokinetic Parameters in Dynamic Fluorescence Molecular Tomography,” IEEE Trans. Biomed. Eng. 61(3), 986–990 (2014).
[Crossref] [PubMed]

F. Liu, X. Cao, W. He, J. Song, Z. Dai, B. Zhang, J. Luo, Y. Li, and J. Bai, “Monitoring of tumor response to cisplatin by subsurface fluorescence molecular tomography,” J. Biomed. Opt. 17(4), 040504 (2012).
[Crossref] [PubMed]

He, Y.

G. Zhang, F. Liu, B. Zhang, Y. He, J. Luo, and J. Bai, “Imaging of pharmacokinetic rates of indocyanine green in mouse liver with a hybrid fluorescence molecular tomography/x-ray computed tomography system,” J. Biomed. Opt. 18(4), 040505 (2013).
[Crossref] [PubMed]

Hiraoka, M.

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The Finite Element Method for the Propagation of Light in Scattering Media: Boundary and Source Conditions,” Med. Phys. 22(11), 1779–1792 (1995).
[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]

Jiang, S.

Jiang, S. S.

S. C. Davis, B. W. Pogue, R. Springett, C. Leussler, P. Mazurkewitz, S. B. Tuttle, S. L. Gibbs-Strauss, S. S. Jiang, H. Dehghani, and K. D. Paulsen, “Magnetic resonance-coupled fluorescence tomography scanner for molecular imaging of tissue,” Rev. Sci. Instrum. 79(6), 064302 (2008).
[Crossref] [PubMed]

Leahy, R. M.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: a 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

Leussler, C.

S. C. Davis, B. W. Pogue, R. Springett, C. Leussler, P. Mazurkewitz, S. B. Tuttle, S. L. Gibbs-Strauss, S. S. Jiang, H. Dehghani, and K. D. Paulsen, “Magnetic resonance-coupled fluorescence tomography scanner for molecular imaging of tissue,” Rev. Sci. Instrum. 79(6), 064302 (2008).
[Crossref] [PubMed]

Li, X. D.

Li, Y.

F. Liu, X. Cao, W. He, J. Song, Z. Dai, B. Zhang, J. Luo, Y. Li, and J. Bai, “Monitoring of tumor response to cisplatin by subsurface fluorescence molecular tomography,” J. Biomed. Opt. 17(4), 040504 (2012).
[Crossref] [PubMed]

Liu, F.

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[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]

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).

G. Zhang, F. Liu, H. Pu, W. He, J. Luo, and J. Bai, “A Direct Method With Structural Priors for Imaging Pharmacokinetic Parameters in Dynamic Fluorescence Molecular Tomography,” IEEE Trans. Biomed. Eng. 61(3), 986–990 (2014).
[Crossref] [PubMed]

Y. Zhang, B. Zhang, F. Liu, J. Luo, and J. Bai, “In vivo tomographic imaging with fluorescence and MRI using tumor-targeted dual-labeled nanoparticles,” Int. J. Nanomedicine 9, 33–41 (2014).
[PubMed]

W. He, G. Zhang, H. Pu, F. Liu, X. Cao, J. Luo, and J. Bai, “Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography,” J. Biomed. Opt. 19(5), 056012 (2014).
[Crossref] [PubMed]

G. Zhang, F. Liu, B. Zhang, Y. He, J. Luo, and J. Bai, “Imaging of pharmacokinetic rates of indocyanine green in mouse liver with a hybrid fluorescence molecular tomography/x-ray computed tomography system,” J. Biomed. Opt. 18(4), 040505 (2013).
[Crossref] [PubMed]

J. Shi, X. Cao, F. Liu, B. Zhang, J. Luo, and J. Bai, “Greedy reconstruction algorithm for fluorescence molecular tomography by means of truncated singular value decomposition conversion,” J. Opt. Soc. Am. A 30(3), 437–447 (2013).
[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, X. Cao, W. He, J. Song, Z. Dai, B. Zhang, J. Luo, Y. Li, and J. Bai, “Monitoring of tumor response to cisplatin by subsurface fluorescence molecular tomography,” J. Biomed. Opt. 17(4), 040504 (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]

Liu, 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]

Luo, J.

M. Chen, H. Su, Y. Zhou, C. Cai, D. Zhang, and J. Luo, “Automatic selection of regularization parameters for dynamic fluorescence molecular tomography: A comparison of L-curve and U-curve methods,” Biomed. Opt. Express 7, 5021–5041 (2016).

Y. Zhou, H. Guang, H. Pu, J. Zhang, and J. Luo, “Unmixing multiple adjacent fluorescent targets with multispectral excited fluorescence molecular tomography,” Appl. Opt. 55(18), 4843–4849 (2016).
[Crossref] [PubMed]

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[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]

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).

Y. Zhang, B. Zhang, F. Liu, J. Luo, and J. Bai, “In vivo tomographic imaging with fluorescence and MRI using tumor-targeted dual-labeled nanoparticles,” Int. J. Nanomedicine 9, 33–41 (2014).
[PubMed]

G. Zhang, F. Liu, H. Pu, W. He, J. Luo, and J. Bai, “A Direct Method With Structural Priors for Imaging Pharmacokinetic Parameters in Dynamic Fluorescence Molecular Tomography,” IEEE Trans. Biomed. Eng. 61(3), 986–990 (2014).
[Crossref] [PubMed]

W. He, G. Zhang, H. Pu, F. Liu, X. Cao, J. Luo, and J. Bai, “Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography,” J. Biomed. Opt. 19(5), 056012 (2014).
[Crossref] [PubMed]

G. Zhang, F. Liu, B. Zhang, Y. He, J. Luo, and J. Bai, “Imaging of pharmacokinetic rates of indocyanine green in mouse liver with a hybrid fluorescence molecular tomography/x-ray computed tomography system,” J. Biomed. Opt. 18(4), 040505 (2013).
[Crossref] [PubMed]

J. Shi, X. Cao, F. Liu, B. Zhang, J. Luo, and J. Bai, “Greedy reconstruction algorithm for fluorescence molecular tomography by means of truncated singular value decomposition conversion,” J. Opt. Soc. Am. A 30(3), 437–447 (2013).
[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, X. Cao, W. He, J. Song, Z. Dai, B. Zhang, J. Luo, Y. Li, and J. Bai, “Monitoring of tumor response to cisplatin by subsurface fluorescence molecular tomography,” J. Biomed. Opt. 17(4), 040504 (2012).
[Crossref] [PubMed]

Mazurkewitz, P.

S. C. Davis, B. W. Pogue, R. Springett, C. Leussler, P. Mazurkewitz, S. B. Tuttle, S. L. Gibbs-Strauss, S. S. Jiang, H. Dehghani, and K. D. Paulsen, “Magnetic resonance-coupled fluorescence tomography scanner for molecular imaging of tissue,” Rev. Sci. Instrum. 79(6), 064302 (2008).
[Crossref] [PubMed]

Ntziachristos, V.

N. C. Deliolanis and V. Ntziachristos, “Fluorescence molecular tomography of brain tumors in mice,” Cold Spring Harb. Protoc. 2013(5), 438–443 (2013).
[Crossref] [PubMed]

O’Leary, M. A.

Paulsen, K. D.

Peter, J.

L. Cao and J. Peter, “Bayesian reconstruction strategy of fluorescence-mediated tomography using an integrated SPECT-CT-OT system,” Phys. Med. Biol. 55(9), 2693–2708 (2010).
[Crossref] [PubMed]

Pogue, B. W.

S. C. Davis, K. S. Samkoe, K. M. Tichauer, K. J. Sexton, J. R. Gunn, S. J. Deharvengt, T. Hasan, and B. W. Pogue, “Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(22), 9025–9030 (2013).
[Crossref] [PubMed]

S. C. Davis, B. W. Pogue, R. Springett, C. Leussler, P. Mazurkewitz, S. B. Tuttle, S. L. Gibbs-Strauss, S. S. Jiang, H. Dehghani, and K. D. Paulsen, “Magnetic resonance-coupled fluorescence tomography scanner for molecular imaging of tissue,” Rev. Sci. Instrum. 79(6), 064302 (2008).
[Crossref] [PubMed]

S. C. Davis, H. Dehghani, J. Wang, S. Jiang, B. W. Pogue, and K. D. Paulsen, “Image-guided diffuse optical fluorescence tomography implemented with Laplacian-type regularization,” Opt. Express 15(7), 4066–4082 (2007).
[Crossref] [PubMed]

P. K. Yalavarthy, B. W. Pogue, H. Dehghani, C. M. Carpenter, S. Jiang, and K. D. Paulsen, “Structural information within regularization matrices improves near infrared diffuse optical tomography,” Opt. Express 15(13), 8043–8058 (2007).
[Crossref] [PubMed]

Pu, H.

Y. Zhou, H. Guang, H. Pu, J. Zhang, and J. Luo, “Unmixing multiple adjacent fluorescent targets with multispectral excited fluorescence molecular tomography,” Appl. Opt. 55(18), 4843–4849 (2016).
[Crossref] [PubMed]

W. He, G. Zhang, H. Pu, F. Liu, X. Cao, J. Luo, and J. Bai, “Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography,” J. Biomed. Opt. 19(5), 056012 (2014).
[Crossref] [PubMed]

G. Zhang, F. Liu, H. Pu, W. He, J. Luo, and J. Bai, “A Direct Method With Structural Priors for Imaging Pharmacokinetic Parameters in Dynamic Fluorescence Molecular Tomography,” IEEE Trans. Biomed. Eng. 61(3), 986–990 (2014).
[Crossref] [PubMed]

Samkoe, K. S.

S. C. Davis, K. S. Samkoe, K. M. Tichauer, K. J. Sexton, J. R. Gunn, S. J. Deharvengt, T. Hasan, and B. W. Pogue, “Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(22), 9025–9030 (2013).
[Crossref] [PubMed]

Schweiger, M.

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The Finite Element Method for the Propagation of Light in Scattering Media: Boundary and Source Conditions,” Med. Phys. 22(11), 1779–1792 (1995).
[Crossref] [PubMed]

Sexton, K. J.

S. C. Davis, K. S. Samkoe, K. M. Tichauer, K. J. Sexton, J. R. Gunn, S. J. Deharvengt, T. Hasan, and B. W. Pogue, “Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(22), 9025–9030 (2013).
[Crossref] [PubMed]

Shi, J.

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[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).

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]

J. Shi, X. Cao, F. Liu, B. Zhang, J. Luo, and J. Bai, “Greedy reconstruction algorithm for fluorescence molecular tomography by means of truncated singular value decomposition conversion,” J. Opt. Soc. Am. A 30(3), 437–447 (2013).
[Crossref] [PubMed]

Song, J.

F. Liu, X. Cao, W. He, J. Song, Z. Dai, B. Zhang, J. Luo, Y. Li, and J. Bai, “Monitoring of tumor response to cisplatin by subsurface fluorescence molecular tomography,” J. Biomed. Opt. 17(4), 040504 (2012).
[Crossref] [PubMed]

Springett, R.

S. C. Davis, B. W. Pogue, R. Springett, C. Leussler, P. Mazurkewitz, S. B. Tuttle, S. L. Gibbs-Strauss, S. S. Jiang, H. Dehghani, and K. D. Paulsen, “Magnetic resonance-coupled fluorescence tomography scanner for molecular imaging of tissue,” Rev. Sci. Instrum. 79(6), 064302 (2008).
[Crossref] [PubMed]

Stout, D.

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: a 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

Su, H.

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]

Tichauer, K. M.

S. C. Davis, K. S. Samkoe, K. M. Tichauer, K. J. Sexton, J. R. Gunn, S. J. Deharvengt, T. Hasan, and B. W. Pogue, “Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(22), 9025–9030 (2013).
[Crossref] [PubMed]

Tikhonov, A.

A. Tikhonov, “Solving ill-conditioned and singular linear systems: A tutorial on regularization,” Soviet Math. Dokl. 5, 1035–1038 (1963).

Tuttle, S. B.

S. C. Davis, B. W. Pogue, R. Springett, C. Leussler, P. Mazurkewitz, S. B. Tuttle, S. L. Gibbs-Strauss, S. S. Jiang, H. Dehghani, and K. D. Paulsen, “Magnetic resonance-coupled fluorescence tomography scanner for molecular imaging of tissue,” Rev. Sci. Instrum. 79(6), 064302 (2008).
[Crossref] [PubMed]

Wang, J.

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]

Yalavarthy, P. K.

Yodh, A. G.

Zhang, B.

Y. Zhang, B. Zhang, F. Liu, J. Luo, and J. Bai, “In vivo tomographic imaging with fluorescence and MRI using tumor-targeted dual-labeled nanoparticles,” Int. J. Nanomedicine 9, 33–41 (2014).
[PubMed]

G. Zhang, F. Liu, B. Zhang, Y. He, J. Luo, and J. Bai, “Imaging of pharmacokinetic rates of indocyanine green in mouse liver with a hybrid fluorescence molecular tomography/x-ray computed tomography system,” J. Biomed. Opt. 18(4), 040505 (2013).
[Crossref] [PubMed]

J. Shi, X. Cao, F. Liu, B. Zhang, J. Luo, and J. Bai, “Greedy reconstruction algorithm for fluorescence molecular tomography by means of truncated singular value decomposition conversion,” J. Opt. Soc. Am. A 30(3), 437–447 (2013).
[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, X. Cao, W. He, J. Song, Z. Dai, B. Zhang, J. Luo, Y. Li, and J. Bai, “Monitoring of tumor response to cisplatin by subsurface fluorescence molecular tomography,” J. Biomed. Opt. 17(4), 040504 (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]

Zhang, D.

Zhang, G.

W. He, G. Zhang, H. Pu, F. Liu, X. Cao, J. Luo, and J. Bai, “Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography,” J. Biomed. Opt. 19(5), 056012 (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]

G. Zhang, F. Liu, H. Pu, W. He, J. Luo, and J. Bai, “A Direct Method With Structural Priors for Imaging Pharmacokinetic Parameters in Dynamic Fluorescence Molecular Tomography,” IEEE Trans. Biomed. Eng. 61(3), 986–990 (2014).
[Crossref] [PubMed]

G. Zhang, F. Liu, B. Zhang, Y. He, J. Luo, and J. Bai, “Imaging of pharmacokinetic rates of indocyanine green in mouse liver with a hybrid fluorescence molecular tomography/x-ray computed tomography system,” J. Biomed. Opt. 18(4), 040505 (2013).
[Crossref] [PubMed]

Zhang, J.

Y. Zhou, H. Guang, H. Pu, J. Zhang, and J. Luo, “Unmixing multiple adjacent fluorescent targets with multispectral excited fluorescence molecular tomography,” Appl. Opt. 55(18), 4843–4849 (2016).
[Crossref] [PubMed]

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[Crossref] [PubMed]

Zhang, Y.

Y. Zhang, B. Zhang, F. Liu, J. Luo, and J. Bai, “In vivo tomographic imaging with fluorescence and MRI using tumor-targeted dual-labeled nanoparticles,” Int. J. Nanomedicine 9, 33–41 (2014).
[PubMed]

Zhou, Y.

Appl. Opt. (1)

Biomed. Opt. Express (1)

Cold Spring Harb. Protoc. (1)

N. C. Deliolanis and V. Ntziachristos, “Fluorescence molecular tomography of brain tumors in mice,” Cold Spring Harb. Protoc. 2013(5), 438–443 (2013).
[Crossref] [PubMed]

IEEE Trans. Biomed. Eng. (2)

G. Zhang, F. Liu, H. Pu, W. He, J. Luo, and J. Bai, “A Direct Method With Structural Priors for Imaging Pharmacokinetic Parameters in Dynamic Fluorescence Molecular Tomography,” IEEE Trans. Biomed. Eng. 61(3), 986–990 (2014).
[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]

Int. J. Nanomedicine (1)

Y. Zhang, B. Zhang, F. Liu, J. Luo, and J. Bai, “In vivo tomographic imaging with fluorescence and MRI using tumor-targeted dual-labeled nanoparticles,” Int. J. Nanomedicine 9, 33–41 (2014).
[PubMed]

Inverse Probl. (1)

S. R. Arridge, “Optical tomography in medical imaging,” Inverse Probl. 15(2), R41–R93 (1999).
[Crossref]

J. Biomed. Opt. (5)

W. He, G. Zhang, H. Pu, F. Liu, X. Cao, J. Luo, and J. Bai, “Modified forward model for eliminating the time-varying impact in fluorescence molecular tomography,” J. Biomed. Opt. 19(5), 056012 (2014).
[Crossref] [PubMed]

G. Zhang, F. Liu, B. Zhang, Y. He, J. Luo, and J. Bai, “Imaging of pharmacokinetic rates of indocyanine green in mouse liver with a hybrid fluorescence molecular tomography/x-ray computed tomography system,” J. Biomed. Opt. 18(4), 040505 (2013).
[Crossref] [PubMed]

J. Shi, F. Liu, J. Zhang, J. Luo, and J. Bai, “Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization,” J. Biomed. Opt. 20(5), 055004 (2015).
[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]

F. Liu, X. Cao, W. He, J. Song, Z. Dai, B. Zhang, J. Luo, Y. Li, and J. Bai, “Monitoring of tumor response to cisplatin by subsurface fluorescence molecular tomography,” J. Biomed. Opt. 17(4), 040504 (2012).
[Crossref] [PubMed]

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

Med. Phys. (1)

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The Finite Element Method for the Propagation of Light in Scattering Media: Boundary and Source Conditions,” Med. Phys. 22(11), 1779–1792 (1995).
[Crossref] [PubMed]

Opt. Express (2)

Opt. Lett. (2)

Phys. Med. Biol. (2)

L. Cao and J. Peter, “Bayesian reconstruction strategy of fluorescence-mediated tomography using an integrated SPECT-CT-OT system,” Phys. Med. Biol. 55(9), 2693–2708 (2010).
[Crossref] [PubMed]

B. Dogdas, D. Stout, A. F. Chatziioannou, and R. M. Leahy, “Digimouse: a 3D whole body mouse atlas from CT and cryosection data,” Phys. Med. Biol. 52(3), 577–587 (2007).
[Crossref] [PubMed]

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

S. C. Davis, K. S. Samkoe, K. M. Tichauer, K. J. Sexton, J. R. Gunn, S. J. Deharvengt, T. Hasan, and B. W. Pogue, “Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo,” Proc. Natl. Acad. Sci. U.S.A. 110(22), 9025–9030 (2013).
[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).

Rev. Sci. Instrum. (1)

S. C. Davis, B. W. Pogue, R. Springett, C. Leussler, P. Mazurkewitz, S. B. Tuttle, S. L. Gibbs-Strauss, S. S. Jiang, H. Dehghani, and K. D. Paulsen, “Magnetic resonance-coupled fluorescence tomography scanner for molecular imaging of tissue,” Rev. Sci. Instrum. 79(6), 064302 (2008).
[Crossref] [PubMed]

Soviet Math. Dokl. (1)

A. Tikhonov, “Solving ill-conditioned and singular linear systems: A tutorial on regularization,” Soviet Math. Dokl. 5, 1035–1038 (1963).

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Figures (8)

Fig. 1
Fig. 1

The flowchart of the self-prior strategy.

Fig. 2
Fig. 2

Setup of simulations. (a) 3-D geometry of the Digimouse model used in the simulations with a length of 3.2 cm from the neck to the base of the abdomen. The anatomical information of different organs is depicted with different colors. (b) ICG concentration (with arbitrary unit, a.u.) curves simulating the metabolic process of ICG in different organs. The red circle indicates the data chosen to generate the fluorescent signal used for reconstruction.

Fig. 3
Fig. 3

Setup of in vivo experiments. (a) The white light image of the nude mouse fixed on the rotation stage. The red line indicates the top and bottom of the image domain. (b) The meshed volume of the image domain.

Fig. 4
Fig. 4

Influence of energy threshold on simulation results. (a) (left) 3-D and (right) transverse views of the true distribution of ICG in the liver. The red solid curve indicates the slice of the tomographic images selected. (b)-(e) The transverse view of the reconstruction results with the energy threshold being set as 25%, 45%, 65% and 85% of the maximum value in each space-frequency energy spectrum, respectively.

Fig. 5
Fig. 5

REs and CNRs varied with the number of iterations.

Fig. 6
Fig. 6

Reconstruction results of simulations. (a) 3-D and transverse views of the true distribution of ICG in the liver. (b) The space-frequency energy spectrum of the reconstruction result of the non-prior strategy. (c), (f) and (i) 3-D and transverse views of the reconstruction result of the non-prior strategy at SNRs of 26dB, 20dB and 14dB, respectively. (d), (g) and (j) 3-D and transverse views of the reconstruction result of the self-prior strategy at SNRs of 26dB, 20dB and 14dB, respectively. (e), (h) and (k) 3-D and transverse views of the reconstruction result of the structural prior strategy at SNRs of 26dB, 20dB and 14dB, respectively.

Fig. 7
Fig. 7

Reconstruction results of in vivo experiments. (a) and (d) Reconstructed FMT images in the transverse view by the non-prior strategy and self-prior strategy, respectively. (b) and (e) XCT images in the transverse view. (c) and (f) Merged images of FMT/XCT in the transverse view. (g) XCT image in the coronal view. The red dotted line in (g) indicates the location of the tomographic images in (a)-(f).

Fig. 8
Fig. 8

Reconstruction results of in vivo experiments in the 3-D view. (a) and (c) Reconstructed FMT images in the 3-D view by the non-prior strategy and self-prior strategy, respectively. (b) and (d) Merged images of FMT and XCT image in the 3-D view. (e) and (f) XCT reconstructed image of the liver and merged image of the liver and skeleton in the 3-D view, respectively.

Tables (5)

Tables Icon

Table 1 Optical Properties of Different Regions

Tables Icon

Table 2 ICG Concentrations in Different Organs at the 9th Minute

Tables Icon

Table 3 REs of Reconstruction Results based on Different Energy Thresholds

Tables Icon

Table 4 Reconstruction Results by Different Methods at Different Noise Levels in the simulations

Tables Icon

Table 5 Reconstruction Results by Different Methods in the in vivo experiments

Equations (16)

Equations on this page are rendered with MathJax. Learn more.

[ D e ( r ) Φ e ( r ) ]+ μ ae ( r ) Φ e ( r )=S( r ) [ D m ( r ) Φ m ( r ) ]+ μ am ( r ) Φ m ( r )= Φ e ( r )η μ af ( r )
Φ( r d , r s )=Θ V G m ( r d ,r )x( r ) G e ( r, r s ) d 3 r
Φ m =Wx
x= argmin x { Φ m Wx 2 2 +λ x 2 2 }
{ x k_itkr = x k_itkr1 + ( W H W+λI ) 1 W H r k_itkr1 r k_itkr = Φ m W x k_itkr ,k_itkr=1,2,...
S x t ( n, ω k )= m=0 N1 x t ( m )g( nmT ) e j 2π H mk , k=0,1,...,H1
F t ( n, ω k )= | m=0 N1 x t ( m )g( nmT ) e j 2π H mk | 2 , k=0,1,...,H1 .
K t,i ={ 1,if F t ( i, ω k )> T F ,k=0,1,...,M1 0,else , i=0,1,...,N1
L t,ij ={ 1,ifi=j 1 N in ,if K t,i = K t,j =1 0,else ,i,j=0,1,...,N1
x t+1 = argmin x { Φ m Wx 2 2 +λ L t x 2 2 }
{ x t+1,k_self = x t+1,k_self1 + ( W H W+λ L t H L t ) 1 W H r k_self1 r k_self = Φ m W x t+1,k_self , k_self=1,2,...
x locrecon,i ={ 1,if x recon,i >0 0,if x recon,i <0
x loctrue,i ={ 1,ifnodei R obj 0,ifnodei R obj
RLE= x locrecon x loctrue 2 x loctrue 2
RE= x recon x true 2 x true 2
CNR= μ VOI μ BG ( ω VOI σ VOI 2 + ω BG σ BG 2 ) 1 2

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