Abstract

Fluorescence molecular tomography (FMT) is an important in vivo molecular imaging technique and has been widely studied in preclinical research. Many methods perform well in the reconstruction of a single fluorescent target but may fail in reconstructing multiple targets because of the severe ill-posedness of the FMT inverse problem. In this paper the original synchronization-inspired clustering algorithm (OSC) is introduced into FMT for resolving multiple targets from the reconstruction result. Based on OSC, a synchronization-based clustering algorithm for FMT (SC-FMT) is developed to further improve location accuracy. Both algorithms utilize the minimum spanning tree to automatically identify the number of the reconstructed targets without prior information and human intervention. A serial of numerical simulation results demonstrates that SC-FMT and OSC can resolve multiple targets robustly and automatically, which also shows the potential of the proposed postprocessing algorithms in FMT reconstruction.

© 2018 Optical Society of America

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References

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    [Crossref]

2016 (2)

2015 (5)

H. Guo, J. Yu, X. He, Y. Hou, F. Dong, and S. Zhang, “Improved sparse reconstruction for fluorescence molecular tomography with L1/2 regularization,” Biomed. Opt. Express 6, 1648–1664 (2015).
[Crossref]

K. Wang, Q. Wang, Q. Luo, and X. Yang, “Fluorescence molecular tomography in the second near-infrared window,” Opt. Express 23, 12669–12679 (2015).
[Crossref]

X. He, F. Dong, J. Yu, H. Guo, and Y. Hou, “Reconstruction algorithm for fluorescence molecular tomography using sorted L-one penalized estimation,” J. Opt. Soc. Am. A 32, 1928–1935 (2015).
[Crossref]

Y. An, J. Liu, G. Zhang, J. Ye, Y. Du, Y. Mao, and J. Tian, “A novel region reconstruction method for fluorescence molecular tomography,” IEEE Trans. Biomed. Eng. 62, 1818–1826 (2015).
[Crossref]

J. L. Zhang, J. W. Shi, S. M. Zuo, F. Liu, J. Bai, and J. W. Luo, “Fast reconstruction in fluorescence molecular tomography using data compression of intra- and inter-projections,” Chin. Opt. Lett. 13, 52–56 (2015).

2014 (3)

H. Guo, Y. Hou, X. He, J. Yu, J. Cheng, and X. Pu, “Adaptive hp finite element method for fluorescence molecular tomography with simplified spherical harmonics approximation,” J. Innov. Opt. Health Sci. 7, 1350057 (2014).
[Crossref]

D. Zhu, Y. Zhao, R. Baikejiang, Z. Yuan, and C. Li, “Comparison of regularization methods in fluorescence molecular tomography,” Photonics 1, 95–109 (2014).
[Crossref]

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39, 4156–4159 (2014).
[Crossref]

2013 (3)

H. Yi, D. Chen, W. Li, S. Zhu, X. Wang, J. Tian, and J. Liang, “Reconstruction algorithms based on L1-norm and L2-norm for two imaging models of fluorescence molecular tomography: a comparative study,” J. Biomed. Opt. 18, 056013 (2013).
[Crossref]

J. Shao, X. He, C. Böhm, Q. Yang, and C. Plant, “Synchronization-inspired partitioning and hierarchical clustering,” IEEE Trans. Knowl. Data Eng. 25, 893–905 (2013).
[Crossref]

A. Ale, V. Ermolayev, N. C. Deliolanis, and V. Ntziachristos, “Fluorescence background subtraction technique for hybrid fluorescence molecular tomography/x-ray computed tomography imaging of a mouse model of early stage lung cancer,” J. Biomed. Opt. 18, 769–771 (2013).
[Crossref]

2012 (3)

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

A. Ale, V. Ermolayev, E. Herzog, C. Cohrs, M. H. de Angelis, and V. Ntziachristos, “FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-x-ray computed tomography,” Nat. Methods 9, 615–620 (2012).
[Crossref]

J. Feng, C. Qin, K. Jia, S. Zhu, K. Liu, D. Han, X. Yang, Q. Gao, and J. Tian, “Total variation regularization for bioluminescence tomography with the split Bregman method,” Appl. Opt. 51, 4501–4512 (2012).
[Crossref]

2011 (1)

F. Stuker, C. Baltes, K. Dikaiou, D. Vats, L. Carrarl, E. Charbon, J. Ripoll, and M. Rudin, “Hybrid small animal imaging system combining magnetic resonance imaging with fluorescence tomography using single photon avalanche diode detectors,” IEEE. Trans. Med. Imaging 30, 1265–1273 (2011).
[Crossref]

2010 (2)

2009 (3)

X. Zhang, C. Badea, and G. Johnson, “Three-dimensional reconstruction in free-space whole-body fluorescence tomography of mice using optically reconstructed surface and atlas anatomy,” J. Biomed. Opt. 14, 064010 (2009).
[Crossref]

D. Wang, X. Liu, Y. Chen, and J. Bai, “A novel finite-element-based algorithm for fluorescence molecular tomography of heterogeneous media,” IEEE Trans. Inf. Technol. Biomed. 13, 766–773 (2009).
[Crossref]

A. D. Zacharopoulos, P. Svenmarker, J. Axlesson, M. Schweiger, S. R. Arridge, and S. Andersson-Engels, “A matrix-free algorithm for multiple wavelength fluorescence tomography,” Opt. Express 17, 3025–3035 (2009).
[Crossref]

2008 (2)

H. Jiang and Y. Tan, “Diffuse optical tomography guided quantitative fluorescence molecular tomography,” Appl. Opt. 47, 2011–2016 (2008).
[Crossref]

R. Weissleder and M. J. Pittet, “Imaging in the era of molecular oncology,” Nature 452, 580–589 (2008).
[Crossref]

2007 (1)

2006 (1)

V. Ntziachristos, “Fluorescence molecular imaging,” Annu. Rev. Biomed. Eng. 8, 1–33 (2006).
[Crossref]

2005 (2)

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. Cong and G. Wang, “A finite-element-based reconstruction method for 3D fluorescence tomography,” Opt. Express 13, 9847–9857 (2005).
[Crossref]

Ahn, S.

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

Ale, A.

A. Ale, V. Ermolayev, N. C. Deliolanis, and V. Ntziachristos, “Fluorescence background subtraction technique for hybrid fluorescence molecular tomography/x-ray computed tomography imaging of a mouse model of early stage lung cancer,” J. Biomed. Opt. 18, 769–771 (2013).
[Crossref]

A. Ale, V. Ermolayev, E. Herzog, C. Cohrs, M. H. de Angelis, and V. Ntziachristos, “FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-x-ray computed tomography,” Nat. Methods 9, 615–620 (2012).
[Crossref]

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, 4225–4241 (2005).
[Crossref]

An, Y.

Y. An, J. Liu, G. Zhang, J. Ye, Y. Du, Y. Mao, and J. Tian, “A novel region reconstruction method for fluorescence molecular tomography,” IEEE Trans. Biomed. Eng. 62, 1818–1826 (2015).
[Crossref]

Andersson-Engels, S.

Arridge, S. R.

Axlesson, J.

Badea, C.

X. Zhang, C. Badea, and G. Johnson, “Three-dimensional reconstruction in free-space whole-body fluorescence tomography of mice using optically reconstructed surface and atlas anatomy,” J. Biomed. Opt. 14, 064010 (2009).
[Crossref]

Bai, J.

J. L. Zhang, J. W. Shi, S. M. Zuo, F. Liu, J. Bai, and J. W. Luo, “Fast reconstruction in fluorescence molecular tomography using data compression of intra- and inter-projections,” Chin. Opt. Lett. 13, 52–56 (2015).

D. Wang, X. Liu, Y. Chen, and J. Bai, “A novel finite-element-based algorithm for fluorescence molecular tomography of heterogeneous media,” IEEE Trans. Inf. Technol. Biomed. 13, 766–773 (2009).
[Crossref]

X. Song, D. Wang, N. Chen, J. Bai, and H. Wang, “Reconstruction for free-space fluorescence tomography using a novel hybrid adaptive finite element algorithm,” Opt. Express 15, 18300–18317 (2007).
[Crossref]

Baikejiang, R.

D. Zhu, Y. Zhao, R. Baikejiang, Z. Yuan, and C. Li, “Comparison of regularization methods in fluorescence molecular tomography,” Photonics 1, 95–109 (2014).
[Crossref]

Baltes, C.

F. Stuker, C. Baltes, K. Dikaiou, D. Vats, L. Carrarl, E. Charbon, J. Ripoll, and M. Rudin, “Hybrid small animal imaging system combining magnetic resonance imaging with fluorescence tomography using single photon avalanche diode detectors,” IEEE. Trans. Med. Imaging 30, 1265–1273 (2011).
[Crossref]

Böhm, C.

J. Shao, X. He, C. Böhm, Q. Yang, and C. Plant, “Synchronization-inspired partitioning and hierarchical clustering,” IEEE Trans. Knowl. Data Eng. 25, 893–905 (2013).
[Crossref]

C. Böhm, C. Plant, J. Shao, and Q. Yang, “Clustering by synchronization,” in 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010), pp. 583–592.

Brooks, D. H.

Cai, C.

Cai, W.

Carrarl, L.

F. Stuker, C. Baltes, K. Dikaiou, D. Vats, L. Carrarl, E. Charbon, J. Ripoll, and M. Rudin, “Hybrid small animal imaging system combining magnetic resonance imaging with fluorescence tomography using single photon avalanche diode detectors,” IEEE. Trans. Med. Imaging 30, 1265–1273 (2011).
[Crossref]

Charbon, E.

F. Stuker, C. Baltes, K. Dikaiou, D. Vats, L. Carrarl, E. Charbon, J. Ripoll, and M. Rudin, “Hybrid small animal imaging system combining magnetic resonance imaging with fluorescence tomography using single photon avalanche diode detectors,” IEEE. Trans. Med. Imaging 30, 1265–1273 (2011).
[Crossref]

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, 4225–4241 (2005).
[Crossref]

Chen, D.

H. Yi, D. Chen, W. Li, S. Zhu, X. Wang, J. Tian, and J. Liang, “Reconstruction algorithms based on L1-norm and L2-norm for two imaging models of fluorescence molecular tomography: a comparative study,” J. Biomed. Opt. 18, 056013 (2013).
[Crossref]

X. He, J. Liang, X. Wang, J. Yu, X. Qu, X. Wang, Y. Hou, D. Chen, F. Liu, and J. Tian, “Sparse reconstruction for quantitative bioluminescence tomography based on the incomplete variables truncated conjugate gradient method,” Opt. Express 18, 24825–24841 (2010).
[Crossref]

Chen, N.

Chen, Y.

D. Wang, X. Liu, Y. Chen, and J. Bai, “A novel finite-element-based algorithm for fluorescence molecular tomography of heterogeneous media,” IEEE Trans. Inf. Technol. Biomed. 13, 766–773 (2009).
[Crossref]

Cheng, J.

H. Guo, Y. Hou, X. He, J. Yu, J. Cheng, and X. Pu, “Adaptive hp finite element method for fluorescence molecular tomography with simplified spherical harmonics approximation,” J. Innov. Opt. Health Sci. 7, 1350057 (2014).
[Crossref]

Cherry, S.

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

Cohrs, C.

A. Ale, V. Ermolayev, E. Herzog, C. Cohrs, M. H. de Angelis, and V. Ntziachristos, “FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-x-ray computed tomography,” Nat. Methods 9, 615–620 (2012).
[Crossref]

Cong, A.

Cong, W.

de Angelis, M. H.

A. Ale, V. Ermolayev, E. Herzog, C. Cohrs, M. H. de Angelis, and V. Ntziachristos, “FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-x-ray computed tomography,” Nat. Methods 9, 615–620 (2012).
[Crossref]

Deliolanis, N. C.

A. Ale, V. Ermolayev, N. C. Deliolanis, and V. Ntziachristos, “Fluorescence background subtraction technique for hybrid fluorescence molecular tomography/x-ray computed tomography imaging of a mouse model of early stage lung cancer,” J. Biomed. Opt. 18, 769–771 (2013).
[Crossref]

Dikaiou, K.

F. Stuker, C. Baltes, K. Dikaiou, D. Vats, L. Carrarl, E. Charbon, J. Ripoll, and M. Rudin, “Hybrid small animal imaging system combining magnetic resonance imaging with fluorescence tomography using single photon avalanche diode detectors,” IEEE. Trans. Med. Imaging 30, 1265–1273 (2011).
[Crossref]

Dong, F.

Du, Y.

Y. An, J. Liu, G. Zhang, J. Ye, Y. Du, Y. Mao, and J. Tian, “A novel region reconstruction method for fluorescence molecular tomography,” IEEE Trans. Biomed. Eng. 62, 1818–1826 (2015).
[Crossref]

Dutta, J.

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

Ermolayev, V.

A. Ale, V. Ermolayev, N. C. Deliolanis, and V. Ntziachristos, “Fluorescence background subtraction technique for hybrid fluorescence molecular tomography/x-ray computed tomography imaging of a mouse model of early stage lung cancer,” J. Biomed. Opt. 18, 769–771 (2013).
[Crossref]

A. Ale, V. Ermolayev, E. Herzog, C. Cohrs, M. H. de Angelis, and V. Ntziachristos, “FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-x-ray computed tomography,” Nat. Methods 9, 615–620 (2012).
[Crossref]

Feng, J.

Gao, Q.

Guo, H.

Han, D.

He, X.

Herzog, E.

A. Ale, V. Ermolayev, E. Herzog, C. Cohrs, M. H. de Angelis, and V. Ntziachristos, “FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-x-ray computed tomography,” Nat. Methods 9, 615–620 (2012).
[Crossref]

Hou, Y.

Intes, X.

Jia, K.

Jiang, H.

Johnson, G.

X. Zhang, C. Badea, and G. Johnson, “Three-dimensional reconstruction in free-space whole-body fluorescence tomography of mice using optically reconstructed surface and atlas anatomy,” J. Biomed. Opt. 14, 064010 (2009).
[Crossref]

Kruskal, J.

J. Kruskal, “On the shortest spanning tree of a graph and the traveling salesman problem,” in Proceedings of the American Mathematical Society (1956), pp. 48–50.

Leahy, R.

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

Li, C.

D. Zhu, Y. Zhao, R. Baikejiang, Z. Yuan, and C. Li, “Comparison of regularization methods in fluorescence molecular tomography,” Photonics 1, 95–109 (2014).
[Crossref]

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

Li, W.

H. Yi, D. Chen, W. Li, S. Zhu, X. Wang, J. Tian, and J. Liang, “Reconstruction algorithms based on L1-norm and L2-norm for two imaging models of fluorescence molecular tomography: a comparative study,” J. Biomed. Opt. 18, 056013 (2013).
[Crossref]

Liang, J.

H. Yi, D. Chen, W. Li, S. Zhu, X. Wang, J. Tian, and J. Liang, “Reconstruction algorithms based on L1-norm and L2-norm for two imaging models of fluorescence molecular tomography: a comparative study,” J. Biomed. Opt. 18, 056013 (2013).
[Crossref]

X. He, J. Liang, X. Wang, J. Yu, X. Qu, X. Wang, Y. Hou, D. Chen, F. Liu, and J. Tian, “Sparse reconstruction for quantitative bioluminescence tomography based on the incomplete variables truncated conjugate gradient method,” Opt. Express 18, 24825–24841 (2010).
[Crossref]

Liu, F.

J. L. Zhang, J. W. Shi, S. M. Zuo, F. Liu, J. Bai, and J. W. Luo, “Fast reconstruction in fluorescence molecular tomography using data compression of intra- and inter-projections,” Chin. Opt. Lett. 13, 52–56 (2015).

X. He, J. Liang, X. Wang, J. Yu, X. Qu, X. Wang, Y. Hou, D. Chen, F. Liu, and J. Tian, “Sparse reconstruction for quantitative bioluminescence tomography based on the incomplete variables truncated conjugate gradient method,” Opt. Express 18, 24825–24841 (2010).
[Crossref]

Liu, J.

Y. An, J. Liu, G. Zhang, J. Ye, Y. Du, Y. Mao, and J. Tian, “A novel region reconstruction method for fluorescence molecular tomography,” IEEE Trans. Biomed. Eng. 62, 1818–1826 (2015).
[Crossref]

Liu, K.

Liu, X.

D. Wang, X. Liu, Y. Chen, and J. Bai, “A novel finite-element-based algorithm for fluorescence molecular tomography of heterogeneous media,” IEEE Trans. Inf. Technol. Biomed. 13, 766–773 (2009).
[Crossref]

Luo, J.

Luo, J. W.

J. L. Zhang, J. W. Shi, S. M. Zuo, F. Liu, J. Bai, and J. W. Luo, “Fast reconstruction in fluorescence molecular tomography using data compression of intra- and inter-projections,” Chin. Opt. Lett. 13, 52–56 (2015).

Luo, Q.

Lv, Y.

Ma, X.

Mao, Y.

Y. An, J. Liu, G. Zhang, J. Ye, Y. Du, Y. Mao, and J. Tian, “A novel region reconstruction method for fluorescence molecular tomography,” IEEE Trans. Biomed. Eng. 62, 1818–1826 (2015).
[Crossref]

Niedre, M.

Ntziachristos, V.

A. Ale, V. Ermolayev, N. C. Deliolanis, and V. Ntziachristos, “Fluorescence background subtraction technique for hybrid fluorescence molecular tomography/x-ray computed tomography imaging of a mouse model of early stage lung cancer,” J. Biomed. Opt. 18, 769–771 (2013).
[Crossref]

A. Ale, V. Ermolayev, E. Herzog, C. Cohrs, M. H. de Angelis, and V. Ntziachristos, “FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-x-ray computed tomography,” Nat. Methods 9, 615–620 (2012).
[Crossref]

V. Ntziachristos, “Fluorescence molecular imaging,” Annu. Rev. Biomed. Eng. 8, 1–33 (2006).
[Crossref]

Pera, V.

Pittet, M. J.

R. Weissleder and M. J. Pittet, “Imaging in the era of molecular oncology,” Nature 452, 580–589 (2008).
[Crossref]

Plant, C.

J. Shao, X. He, C. Böhm, Q. Yang, and C. Plant, “Synchronization-inspired partitioning and hierarchical clustering,” IEEE Trans. Knowl. Data Eng. 25, 893–905 (2013).
[Crossref]

C. Böhm, C. Plant, J. Shao, and Q. Yang, “Clustering by synchronization,” in 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010), pp. 583–592.

Pu, X.

H. Guo, Y. Hou, X. He, J. Yu, J. Cheng, and X. Pu, “Adaptive hp finite element method for fluorescence molecular tomography with simplified spherical harmonics approximation,” J. Innov. Opt. Health Sci. 7, 1350057 (2014).
[Crossref]

Qin, C.

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, 4225–4241 (2005).
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H. Yi, D. Chen, W. Li, S. Zhu, X. Wang, J. Tian, and J. Liang, “Reconstruction algorithms based on L1-norm and L2-norm for two imaging models of fluorescence molecular tomography: a comparative study,” J. Biomed. Opt. 18, 056013 (2013).
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Appl. Opt. (3)

Biomed. Opt. Express (3)

Chin. Opt. Lett. (1)

J. L. Zhang, J. W. Shi, S. M. Zuo, F. Liu, J. Bai, and J. W. Luo, “Fast reconstruction in fluorescence molecular tomography using data compression of intra- and inter-projections,” Chin. Opt. Lett. 13, 52–56 (2015).

IEEE Trans. Biomed. Eng. (1)

Y. An, J. Liu, G. Zhang, J. Ye, Y. Du, Y. Mao, and J. Tian, “A novel region reconstruction method for fluorescence molecular tomography,” IEEE Trans. Biomed. Eng. 62, 1818–1826 (2015).
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IEEE Trans. Inf. Technol. Biomed. (1)

D. Wang, X. Liu, Y. Chen, and J. Bai, “A novel finite-element-based algorithm for fluorescence molecular tomography of heterogeneous media,” IEEE Trans. Inf. Technol. Biomed. 13, 766–773 (2009).
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IEEE Trans. Knowl. Data Eng. (1)

J. Shao, X. He, C. Böhm, Q. Yang, and C. Plant, “Synchronization-inspired partitioning and hierarchical clustering,” IEEE Trans. Knowl. Data Eng. 25, 893–905 (2013).
[Crossref]

IEEE. Trans. Med. Imaging (1)

F. Stuker, C. Baltes, K. Dikaiou, D. Vats, L. Carrarl, E. Charbon, J. Ripoll, and M. Rudin, “Hybrid small animal imaging system combining magnetic resonance imaging with fluorescence tomography using single photon avalanche diode detectors,” IEEE. Trans. Med. Imaging 30, 1265–1273 (2011).
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A. Ale, V. Ermolayev, N. C. Deliolanis, and V. Ntziachristos, “Fluorescence background subtraction technique for hybrid fluorescence molecular tomography/x-ray computed tomography imaging of a mouse model of early stage lung cancer,” J. Biomed. Opt. 18, 769–771 (2013).
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H. Yi, D. Chen, W. Li, S. Zhu, X. Wang, J. Tian, and J. Liang, “Reconstruction algorithms based on L1-norm and L2-norm for two imaging models of fluorescence molecular tomography: a comparative study,” J. Biomed. Opt. 18, 056013 (2013).
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H. Guo, Y. Hou, X. He, J. Yu, J. Cheng, and X. Pu, “Adaptive hp finite element method for fluorescence molecular tomography with simplified spherical harmonics approximation,” J. Innov. Opt. Health Sci. 7, 1350057 (2014).
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A. Ale, V. Ermolayev, E. Herzog, C. Cohrs, M. H. de Angelis, and V. Ntziachristos, “FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-x-ray computed tomography,” Nat. Methods 9, 615–620 (2012).
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Nature (1)

R. Weissleder and M. J. Pittet, “Imaging in the era of molecular oncology,” Nature 452, 580–589 (2008).
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Opt. Express (5)

Opt. Lett. (1)

Photonics (1)

D. Zhu, Y. Zhao, R. Baikejiang, Z. Yuan, and C. Li, “Comparison of regularization methods in fluorescence molecular tomography,” Photonics 1, 95–109 (2014).
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Phys. Med. Biol. (2)

J. Dutta, S. Ahn, C. Li, S. Cherry, and R. Leahy, “Joint L1 and total variation regularization for fluorescence molecular tomography,” Phys. Med. Biol. 57, 1459–1476 (2012).
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J. Kruskal, “On the shortest spanning tree of a graph and the traveling salesman problem,” in Proceedings of the American Mathematical Society (1956), pp. 48–50.

C. Böhm, C. Plant, J. Shao, and Q. Yang, “Clustering by synchronization,” in 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010), pp. 583–592.

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Figures (8)

Fig. 1.
Fig. 1.

OSC. (a) The initial state of dataset D. (b) The comparison of objects at t=0 and t=1. The black dots are the objects at t=0. The red dots are the objects at t=1. (c) The final result of OSC.

Fig. 2.
Fig. 2.

2D view of the reconstruction result for double targets. Obviously, the fluorescent yields of the nodes around the margins of the different reconstructed targets are smaller than the nodes around the centers.

Fig. 3.
Fig. 3.

Cylindrical phantoms. The left column shows the 3D views of cylindrical phantoms. The red small cylinders are real fluorescent targets. The excitation plane is z=17.5  mm. The right column shows photon distribution on the surface.

Fig. 4.
Fig. 4.

3D view of the results. The left column is the reconstruction results corresponding to Case 1 and the right column is the result with corresponding to Case 2. The red cylinders are real fluorescent sources. The bottom row shows the finite element method (FEM) nodes.

Fig. 5.
Fig. 5.

Variation of the LE obtained by SC-FMT and OSC under different noise levels in homogenous phantom simulation.

Fig. 6.
Fig. 6.

Digital mouse. The left column shows the 3D views of the digital mouse. The organs of the digital mouse are muscle, heart, lungs, liver, kidneys, and stomach, respectively. The red cylinders are real fluorescent sources. In the first row, the fluorescent sources are set in the liver. In the second row, the two sources are set in the lungs and liver, respectively. In the third row, the sources are set in the liver and lungs. The brown planes are excitation planes. The right column shows photon distribution on the surface.

Fig. 7.
Fig. 7.

Results of digital mouse experiments. The left column is the result of Case 3. The middle column is the result of Case 4. The right column is the result of Case 5. The bottom row shows the FEM nodes.

Fig. 8.
Fig. 8.

Variation of the LE obtained by SC-FMT and OSC under different noise levels in digital mouse model simulation.

Tables (3)

Tables Icon

Table 1. Optimal Range of Parameter ϵ in Different Cases

Tables Icon

Table 2. Optical Parameters of the Digital Mouse

Equations (10)

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

{·[Dx(r)ϕx(r)]+μax(r)ϕx(r)=Θδ(rrl)·[Dm(r)ϕm(r)]+μam(r)ϕm(r)=ϕx(r)ημaf(r)(rΩ).
ϕx,m(r)+2qDx,m(r)[v(r)·ϕx,m(r)]=0(rΩ),
AX=ϕ,
minXAXϕ22+λX1,
minzcTz+12zTBzF(z),s.t.  z0,
wi(t+1)=wi(t)+1|Nbϵ(w(t))|·yNbϵ(w(t))sin(yi(t)wi(t)),
Nbϵ(w)={yD|dist(y,w)ϵ},
rc=1Ni=1N1NbϵyNbϵ(w)eyw.
wi(t+1)=wi(t)+1|Nbϵ(w(t))|·yNbϵ(w(t))e(EEwE1)·sin(yi(t)wi(t)).
dij={wiwj2,wiwj2<ρ0,wiwj2ρ,i<j,i,j=1,2,,N,