Abstract

Dynamic fluorescence molecular tomography (FMT) plays an important role in drug delivery research. However, the majority of current reconstruction methods focus on solving the stationary FMT problems. If the stationary reconstruction methods are applied to the time-varying fluorescence measurements, the reconstructed results may suffer from a high level of artifacts. In addition, based on the stationary methods, only one tomographic image can be obtained after scanning one circle projection data. As a result, the movement of fluorophore in imaged object may not be detected due to the relative long data acquisition time (typically >1 min). In this paper, we apply extended kalman filter (EKF) technique to solve the non-stationary fluorescence tomography problem. Especially, to improve the EKF reconstruction performance, the generalized inverse of kalman gain is calculated by a second-order iterative method. The numerical simulation, phantom, and in vivo experiments are performed to evaluate the performance of the method. The experimental results indicate that by using the proposed EKF-based second-order iterative (EKF-SOI) method, we cannot only clearly resolve the time-varying distributions of fluorophore within imaged object, but also greatly improve the reconstruction time resolution (~2.5 sec/frame) which makes it possible to detect the movement of fluorophore during the imaging processes.

© 2016 Optical Society of America

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References

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2013 (1)

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L. V. Wang and S. Hu, “Photoacoustic tomography: In vivo imaging from organelles to organs,” Science 335(6075), 1458–1462 (2012).
[Crossref] [PubMed]

X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (2012).
[Crossref] [PubMed]

2011 (5)

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

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[Crossref]

X. Liu, F. Liu, and J. Bai, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58(6), 1602–1611 (2011).
[Crossref] [PubMed]

A. Sarantopoulos, G. Themelis, and V. Ntziachristos, “Imaging the bio-distribution of fluorescent probes using multispectral epi-illumination cryoslicing imaging,” Mol. Imaging Biol. 13(5), 874–885 (2011).
[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]

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[Crossref]

2009 (1)

2008 (1)

M. C. Pierce, D. J. Javier, and R. Richards-Kortum, “Optical contrast agents and imaging systems for detection and diagnosis of cancer,” Int. J. Cancer 123(9), 1979–1990 (2008).
[Crossref] [PubMed]

2007 (4)

N. Deliolanis, T. Lasser, D. Hyde, A. Soubret, J. Ripoll, and V. Ntziachristos, “Free-space fluorescence molecular tomography utilizing 360 degrees geometry projections,” Opt. Lett. 32(4), 382–384 (2007).
[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]

X. Song, X. Xiong, and J. Bai, “A fast reconstruction algorithm for fluorescence optical diffusion tomography based on preiteration,” Int. J. Biomed. Imaging 2007, 23219 (2007).
[Crossref] [PubMed]

E. M. C. Hillman and A. Moore, “All-optical anatomical co-registration for molecular imaging of small animals using dynamic contrast,” Nat. Photonics 1(9), 526–530 (2007).
[Crossref] [PubMed]

2005 (4)

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]

S. Patwardhan, S. Bloch, S. Achilefu, and J. Culver, “Time-dependent whole-body fluorescence tomography of probe bio-distributions in mice,” Opt. Express 13(7), 2564–2577 (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]

S. Patwardhan, S. Bloch, S. Achilefu, and J. Culver, “Time-dependent whole-body fluorescence tomography of probe bio-distributions in mice,” Opt. Express 13(7), 2564–2577 (2005).
[Crossref] [PubMed]

2004 (3)

A. Joshi, W. Bangerth, and E. Sevick-Muraca, “Adaptive finite element based tomography for fluorescence optical imaging in tissue,” Opt. Express 12(22), 5402–5417 (2004).
[Crossref] [PubMed]

X. Gao, Y. Cui, R. M. Levenson, L. W. K. Chung, and S. Nie, “In vivo cancer targeting and imaging with semiconductor quantum dots,” Nat. Biotechnol. 22(8), 969–976 (2004).
[Crossref] [PubMed]

H. Wang, C. Wang, and W. Yin, “A pre-iteration method for the inverse problem in electrical impedance tomography,” IEEE Trans. Instrum. Meas. 53(4), 1093–1096 (2004).
[Crossref]

2003 (2)

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

1999 (1)

1996 (1)

Achilefu, S.

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. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

Bai, J.

X. Zhang, F. Liu, S. Zuo, J. Zhang, J. Bai, and J. Luo, “Fast reconstruction of fluorophore concentration variation based on the derivation of the diffusion equation,” J. Opt. Soc. Am. A 32(11), 1993–2001 (2015).
[Crossref] [PubMed]

X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (2012).
[Crossref] [PubMed]

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

X. Liu, F. Liu, and J. Bai, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58(6), 1602–1611 (2011).
[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. Song, X. Xiong, and J. Bai, “A fast reconstruction algorithm for fluorescence optical diffusion tomography based on preiteration,” Int. J. Biomed. Imaging 2007, 23219 (2007).
[Crossref] [PubMed]

Bangerth, W.

Bloch, S.

Boas, D.

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

Boas, D. A.

Brooks, D.

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]

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]

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, Y.

Chung, L. W. K.

X. Gao, Y. Cui, R. M. Levenson, L. W. K. Chung, and S. Nie, “In vivo cancer targeting and imaging with semiconductor quantum dots,” Nat. Biotechnol. 22(8), 969–976 (2004).
[Crossref] [PubMed]

Cui, Y.

X. Gao, Y. Cui, R. M. Levenson, L. W. K. Chung, and S. Nie, “In vivo cancer targeting and imaging with semiconductor quantum dots,” Nat. Biotechnol. 22(8), 969–976 (2004).
[Crossref] [PubMed]

Culver, J.

Deliolanis, N.

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]

Dougherty, D. E.

Eppstein, M. J.

Franceschini, M. A.

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

Gao, X.

X. Gao, Y. Cui, R. M. Levenson, L. W. K. Chung, and S. Nie, “In vivo cancer targeting and imaging with semiconductor quantum dots,” Nat. Biotechnol. 22(8), 969–976 (2004).
[Crossref] [PubMed]

Guo, X.

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

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]

Hillman, E. M. C.

E. M. C. Hillman and A. Moore, “All-optical anatomical co-registration for molecular imaging of small animals using dynamic contrast,” Nat. Photonics 1(9), 526–530 (2007).
[Crossref] [PubMed]

Hiltunen, P.

P. Hiltunen, S. Sarkka, I. Nissila, A. Lajunen, and J. Lampinen, “State space regularization in the nonstationary inverse problem for diffuse optical tomography,” Inverse Probl. 27(2), 025009 (2011).
[Crossref]

Hu, G.

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

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]

Hu, S.

L. V. Wang and S. Hu, “Photoacoustic tomography: In vivo imaging from organelles to organs,” Science 335(6075), 1458–1462 (2012).
[Crossref] [PubMed]

Hyde, D.

Javier, D. J.

M. C. Pierce, D. J. Javier, and R. Richards-Kortum, “Optical contrast agents and imaging systems for detection and diagnosis of cancer,” Int. J. Cancer 123(9), 1979–1990 (2008).
[Crossref] [PubMed]

Joshi, A.

Kaipio, J. P.

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

Kolehmainen, V.

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

Lajunen, A.

P. Hiltunen, S. Sarkka, I. Nissila, A. Lajunen, and J. Lampinen, “State space regularization in the nonstationary inverse problem for diffuse optical tomography,” Inverse Probl. 27(2), 025009 (2011).
[Crossref]

Lampinen, J.

P. Hiltunen, S. Sarkka, I. Nissila, A. Lajunen, and J. Lampinen, “State space regularization in the nonstationary inverse problem for diffuse optical tomography,” Inverse Probl. 27(2), 025009 (2011).
[Crossref]

Lasser, T.

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]

Levenson, R. M.

X. Gao, Y. Cui, R. M. Levenson, L. W. K. Chung, and S. Nie, “In vivo cancer targeting and imaging with semiconductor quantum dots,” Nat. Biotechnol. 22(8), 969–976 (2004).
[Crossref] [PubMed]

Li, X. D.

Liao, Q.

Liu, F.

X. Zhang, F. Liu, S. Zuo, J. Zhang, J. Bai, and J. Luo, “Fast reconstruction of fluorophore concentration variation based on the derivation of the diffusion equation,” J. Opt. Soc. Am. A 32(11), 1993–2001 (2015).
[Crossref] [PubMed]

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

X. Liu, F. Liu, and J. Bai, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58(6), 1602–1611 (2011).
[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. Liu, Q. Liao, and H. Wang, “In vivo x-ray luminescence tomographic imaging with single-view data,” Opt. Lett. 38(22), 4530–4533 (2013).
[Crossref] [PubMed]

X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (2012).
[Crossref] [PubMed]

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

X. Liu, F. Liu, and J. Bai, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58(6), 1602–1611 (2011).
[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]

Luo, J.

Miller, E.

Moore, A.

E. M. C. Hillman and A. Moore, “All-optical anatomical co-registration for molecular imaging of small animals using dynamic contrast,” Nat. Photonics 1(9), 526–530 (2007).
[Crossref] [PubMed]

Nie, S.

X. Gao, Y. Cui, R. M. Levenson, L. W. K. Chung, and S. Nie, “In vivo cancer targeting and imaging with semiconductor quantum dots,” Nat. Biotechnol. 22(8), 969–976 (2004).
[Crossref] [PubMed]

Nissila, I.

P. Hiltunen, S. Sarkka, I. Nissila, A. Lajunen, and J. Lampinen, “State space regularization in the nonstationary inverse problem for diffuse optical tomography,” Inverse Probl. 27(2), 025009 (2011).
[Crossref]

Ntziachristos, V.

A. Sarantopoulos, G. Themelis, and V. Ntziachristos, “Imaging the bio-distribution of fluorescent probes using multispectral epi-illumination cryoslicing imaging,” Mol. Imaging Biol. 13(5), 874–885 (2011).
[Crossref] [PubMed]

D. Hyde, R. Schulz, D. Brooks, E. Miller, and V. Ntziachristos, “Performance dependence of hybrid x-ray computed tomography/fluorescence molecular tomography on the optical forward problem,” J. Opt. Soc. Am. A 26(4), 919–923 (2009).
[Crossref] [PubMed]

N. Deliolanis, T. Lasser, D. Hyde, A. Soubret, J. Ripoll, and V. Ntziachristos, “Free-space fluorescence molecular tomography utilizing 360 degrees geometry projections,” Opt. Lett. 32(4), 382–384 (2007).
[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]

O’Leary, M. A.

Patwardhan, S.

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]

Pierce, M. C.

M. C. Pierce, D. J. Javier, and R. Richards-Kortum, “Optical contrast agents and imaging systems for detection and diagnosis of cancer,” Int. J. Cancer 123(9), 1979–1990 (2008).
[Crossref] [PubMed]

Prince, S.

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[Crossref] [PubMed]

V. Kolehmainen, S. Prince, S. R. Arridge, and J. P. Kaipio, “State-estimation approach to the nonstationary optical tomography problem,” J. Opt. Soc. Am. A 20(5), 876–889 (2003).
[Crossref] [PubMed]

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]

Richards-Kortum, R.

M. C. Pierce, D. J. Javier, and R. Richards-Kortum, “Optical contrast agents and imaging systems for detection and diagnosis of cancer,” Int. J. Cancer 123(9), 1979–1990 (2008).
[Crossref] [PubMed]

Ripoll, J.

N. Deliolanis, T. Lasser, D. Hyde, A. Soubret, J. Ripoll, and V. Ntziachristos, “Free-space fluorescence molecular tomography utilizing 360 degrees geometry projections,” Opt. Lett. 32(4), 382–384 (2007).
[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]

Sarantopoulos, A.

A. Sarantopoulos, G. Themelis, and V. Ntziachristos, “Imaging the bio-distribution of fluorescent probes using multispectral epi-illumination cryoslicing imaging,” Mol. Imaging Biol. 13(5), 874–885 (2011).
[Crossref] [PubMed]

Sarkka, S.

P. Hiltunen, S. Sarkka, I. Nissila, A. Lajunen, and J. Lampinen, “State space regularization in the nonstationary inverse problem for diffuse optical tomography,” Inverse Probl. 27(2), 025009 (2011).
[Crossref]

Schulz, R.

Sevick-Muraca, E.

Sevick-Muraca, E. M.

Song, X.

X. Song, X. Xiong, and J. Bai, “A fast reconstruction algorithm for fluorescence optical diffusion tomography based on preiteration,” Int. J. Biomed. Imaging 2007, 23219 (2007).
[Crossref] [PubMed]

Soubret, A.

N. Deliolanis, T. Lasser, D. Hyde, A. Soubret, J. Ripoll, and V. Ntziachristos, “Free-space fluorescence molecular tomography utilizing 360 degrees geometry projections,” Opt. Lett. 32(4), 382–384 (2007).
[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]

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]

Themelis, G.

A. Sarantopoulos, G. Themelis, and V. Ntziachristos, “Imaging the bio-distribution of fluorescent probes using multispectral epi-illumination cryoslicing imaging,” Mol. Imaging Biol. 13(5), 874–885 (2011).
[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]

Troy, T. L.

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]

Wang, C.

H. Wang, C. Wang, and W. Yin, “A pre-iteration method for the inverse problem in electrical impedance tomography,” IEEE Trans. Instrum. Meas. 53(4), 1093–1096 (2004).
[Crossref]

Wang, D.

Wang, H.

X. Liu, Q. Liao, and H. Wang, “In vivo x-ray luminescence tomographic imaging with single-view data,” Opt. Lett. 38(22), 4530–4533 (2013).
[Crossref] [PubMed]

H. Wang, C. Wang, and W. Yin, “A pre-iteration method for the inverse problem in electrical impedance tomography,” IEEE Trans. Instrum. Meas. 53(4), 1093–1096 (2004).
[Crossref]

Wang, L. V.

L. V. Wang and S. Hu, “Photoacoustic tomography: In vivo imaging from organelles to organs,” Science 335(6075), 1458–1462 (2012).
[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]

Xiong, X.

X. Song, X. Xiong, and J. Bai, “A fast reconstruction algorithm for fluorescence optical diffusion tomography based on preiteration,” Int. J. Biomed. Imaging 2007, 23219 (2007).
[Crossref] [PubMed]

Yin, W.

H. Wang, C. Wang, and W. Yin, “A pre-iteration method for the inverse problem in electrical impedance tomography,” IEEE Trans. Instrum. Meas. 53(4), 1093–1096 (2004).
[Crossref]

Yodh, A. G.

Zhang, B.

X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (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, H.

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

Zhang, J.

Zhang, X.

Zhang, Y.

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

Zuo, S.

Appl. Opt. (1)

Chin. Opt. Lett. (1)

IEEE Trans. Biomed. Eng. (3)

X. Liu, F. Liu, and J. Bai, “A linear correction for principal component analysis of dynamic fluorescence diffuse optical tomography images,” IEEE Trans. Biomed. Eng. 58(6), 1602–1611 (2011).
[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]

X. Liu, X. Guo, F. Liu, Y. Zhang, H. Zhang, G. Hu, and J. Bai, “Imaging of indocyanine green perfusion inmouse liver with fuorescence diffuse optical tomography,” IEEE Trans. Biomed. Eng. 58(8), 2139–2143 (2011).
[Crossref]

IEEE Trans. Instrum. Meas. (1)

H. Wang, C. Wang, and W. Yin, “A pre-iteration method for the inverse problem in electrical impedance tomography,” IEEE Trans. Instrum. Meas. 53(4), 1093–1096 (2004).
[Crossref]

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]

X. Liu, B. Zhang, J. Luo, and J. Bai, “4-D reconstruction for dynamic fluorescence diffuse optical tomography,” IEEE Trans. Med. Imaging 31(11), 2120–2132 (2012).
[Crossref] [PubMed]

Int. J. Biomed. Imaging (1)

X. Song, X. Xiong, and J. Bai, “A fast reconstruction algorithm for fluorescence optical diffusion tomography based on preiteration,” Int. J. Biomed. Imaging 2007, 23219 (2007).
[Crossref] [PubMed]

Int. J. Cancer (1)

M. C. Pierce, D. J. Javier, and R. Richards-Kortum, “Optical contrast agents and imaging systems for detection and diagnosis of cancer,” Int. J. Cancer 123(9), 1979–1990 (2008).
[Crossref] [PubMed]

Inverse Probl. (1)

P. Hiltunen, S. Sarkka, I. Nissila, A. Lajunen, and J. Lampinen, “State space regularization in the nonstationary inverse problem for diffuse optical tomography,” Inverse Probl. 27(2), 025009 (2011).
[Crossref]

J. Opt. Soc. Am. A (3)

Mol. Imaging Biol. (1)

A. Sarantopoulos, G. Themelis, and V. Ntziachristos, “Imaging the bio-distribution of fluorescent probes using multispectral epi-illumination cryoslicing imaging,” Mol. Imaging Biol. 13(5), 874–885 (2011).
[Crossref] [PubMed]

Nat. Biotechnol. (1)

X. Gao, Y. Cui, R. M. Levenson, L. W. K. Chung, and S. Nie, “In vivo cancer targeting and imaging with semiconductor quantum dots,” Nat. Biotechnol. 22(8), 969–976 (2004).
[Crossref] [PubMed]

Nat. Photonics (1)

E. M. C. Hillman and A. Moore, “All-optical anatomical co-registration for molecular imaging of small animals using dynamic contrast,” Nat. Photonics 1(9), 526–530 (2007).
[Crossref] [PubMed]

Opt. Express (3)

Opt. Lett. (3)

Phys. Med. Biol. (3)

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]

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]

S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography,” Phys. Med. Biol. 48(11), 1491–1504 (2003).
[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]

Science (1)

L. V. Wang and S. Hu, “Photoacoustic tomography: In vivo imaging from organelles to organs,” Science 335(6075), 1458–1462 (2012).
[Crossref] [PubMed]

Other (1)

M. Grewal and A. Andrews, Kalman Filtering: Theory and Practice Using MATLAB (New York: Wiley, 2001).

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Figures (13)

Fig. 1
Fig. 1 The hybrid FMT/XCT imaging system.
Fig. 2
Fig. 2 Schematic diagram of the simulation model. (a) The mouse 3-D geometry model used in simulation studies. The red points in (a) represent the excitation point source positions, located at the height of 0.9 cm and 1.7 cm, respectively. For each excitation location, the fluorescence is measured from the opposite side within 150 o field of view. (b) The coronal slice of the 3-D digital mouse at x = 0.35 cm. (c) The intensity curve of ICG in mouse kidneys, which is acquired from [24]. Inset in (c) shows that ICG intensities for 24 fluorescence projections corresponding to 24 angles from circle 1 (acquired during 0 min to 1 min). Different colors correspond to actual boundaries of different organs (red: livers; green: kidneys; stomach: cyan; pancreas: magenta; spleen: blue; bones: yellow; white: surface).
Fig. 3
Fig. 3 Simulating the movement of fluorophore in mouse during FMT imaging time ( t 1 t 24 ). In the imaging processes, we needed to collect a complete set of data (24 projection images), which will be used as the input data of the conventional reconstruction methods. Briefly, In t 1 t 6 , the fluorophore is assumed to locate the position 1. In t 7 t 14 , the fluorophore moves to the position 2. From t 15 to t 24 , the fluorophore changes to the position 3.
Fig. 4
Fig. 4 Setup for phantom experiment. (a) The phantom used in the experiment, made of a glass cylinder (outer diameter of 3.0 cm) containing the matching fluid (intralipid, ink, and water), with μ a =0 .3 cm -1 and μ s ' = 10 .0 cm -1 . A glass tube (outer diameter of 4.6 mm and height of 7 mm) [see red regions in (a)] filled with 1 μM ICG is immerged inside the phantom and used as fluorescence target. The back point in (a) describes the excitation source location. (b) ICG concentration curve which is used to imitate the time-varying measurements during FMT imaging processes. Here, the ICG concentration curve is the same as that used in Fig. 2(c).
Fig. 5
Fig. 5 Comparison of the reconstructed results obtained by the conventional stationary reconstruction method and the proposed reconstruction method based EKF. Here, the 24 projection data from the first circle were used as the input data of the reconstruction methods. When collecting one frame data, the fluorophore's concentrations vary significantly. (a) and (d) The reconstruction results obtained by the conventional stationary method. (b) and (e) The reconstruction result obtained by the proposed EKF-SOI method, which are imaged at 60s (corresponding to the 24th projection angle). (c) and (f) The reconstruction result obtained by the EKF-DIM method at 60s. Different colors correspond to actual boundaries of different organs (red: livers; green: kidneys; stomach: cyan; pancreas: magenta; spleen: blue; bones: yellow; white: surface).
Fig. 6
Fig. 6 The reconstructed FMT images (total in 24 frames, with a time interval 2.5 s) based on one circle of projection data (24 projection data), which are imaged at different time points (2.5s, 5s, 7.5s, 30s, 45s, and 60s). (a) The reconstructed 2-D tomographic sequence at different time points. (b) The 3-D rendering of the reconstructed images. These tomographic images are obtained by the proposed EKF-SOI method. All images are displayed in the same range, respectively.
Fig. 7
Fig. 7 Comparison of the tomographic images obtained by the stationary, EKF-SOI, and EKF-DIM reconstruction method, respectively. Especially, during the acquisition time (0-60s) that is needed to collect a complete set of measurements for traditional reconstruction method, the fluorophore moved three times within the mouse. The detailed information can be found in Section 3.2.2. (a) The reconstruction result obtained by the stationary method. (b)-(e) The reconstruction result obtained by the proposed EKF-SOI method, which are imaged at 2.5s, 15s, 35s, and 60s, respectively. (f)-(i) The reconstruction result obtained by EKF-DIM method, imaged at the above time points. The white circles in (a) and black circles in (b)-(i) depict the actual position of fluorophore within the mouse.
Fig. 8
Fig. 8 The 3-D rendering of the reconstructed images at different time points (2.5s, 15s, 35s, and 60s). These reconstructed images are obtained by the proposed EKF-SOI reconstruction method based on one circle of projection data. All images are displayed in the same range.
Fig. 9
Fig. 9 The reconstruction results from the phantom obtained by the conventional stationary and EKF methods, where the time-varying measurements from 24 angles were used as the input data of the reconstruction. (a) The reconstruction results obtained by the conventional method based on circle of projection data (24 projection data). (b) The reconstruction result obtained by the proposed EKF-SOI method, which is imaged at imaged at 17.5s (corresponding to the 7th projection angle). (c) The reconstruction result obtained by the EKF-DIM method, imaged at 17.5s. The green curves depict the phantom boundary obtained by back-projecting the 72 white light images [25], which are used to validate the accuracy of registration.
Fig. 10
Fig. 10 The reconstructed FMT image sequence (total in 24 frames) from the phantom experiments based on one circle of projection data (24 projection data). The first shows the reconstructed 2-D tomographic sequence at different time points (2.5s, 15s, 30s, 45s, and 60s). The second row shows the 3-D rendering of the reconstructed images at the above time points. All images are displayed in the same range, respectively. Note that by the proposed EKF-SOI method, we can obtain entire tomographic images through all time points during FMT imaging processes.
Fig. 11
Fig. 11 Comparisons of the reconstruction results in the in vivo experiment obtained by the stationary, EKF-SOI, and EKF-DIM methods, respectively. (a) The reconstruction results obtained by the conventional method based on circle of projection data (24 projection data). (b) and (c) The reconstruction results obtained by the EKF-SOI and EKF-DIM methods, which are imaged at imaged at 60 s (corresponding to the 24th projection angle).
Fig. 12
Fig. 12 The reconstruction results from the in vivo experiment. (a) The x-ray computed tomography image of liver region. (b)-(d) The FMT reconstruction results obtained by the conventional methods, which are imaged at 0~1 min, 1~2 min, and 2~3 min after the injection of ICG, respectively. (e)-(i) The FMT reconstructed results obtained by the proposed EKF-SOI method based on the first projection data, which are imaged at 2.5s, 5.0s, 15s, 17.5s, and 30s, respectively, after the injection of ICG. (j)-(m) The EKF-SOI reconstructed results from the second circle, which are imaged at 62.5s, 65s, 92.5s, and 120s, respectively.
Fig. 13
Fig. 13 The 3-D rendering of the reconstructed images at different time points (2.5s, 5s, 45s, 60s, and 62.5s) from the in vivo experiment. These reconstructed images are obtained by the proposed EKF-SOI method. All images are displayed in the same range.

Equations (15)

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Φ ( r d , r s ) = Θ V G λ f l ( r d , r p ) ρ ( r p ) G λ e x c ( r p , r s ) d r p
[ D ( r ) G ( r ) ] + μ a ( r ) G ( r ) = δ ( r r s )
Φ = ( ρ )
ρ t = t 1 ρ t 1 + n t 1
Φ t = t ( ρ t ) + m t
Φ t t ( ρ t * ) + J t ( ρ t * ) ( ρ t ρ t * ) + m t
State prediction :                               ρ t ( ) = t 1 ρ t 1
Variance prediction :                   C t ( ) = t 1 C t 1 t 1 T + Q t 1
Kalman gain :                                         G t = C t ( ) J t T ( J t C t ( ) J t T + R t ) 1
State update :                                           ρ t = ρ t ( ) + G t ( Φ t t ( ρ t ( ) ) )
Variance update :                                 C t = ( I G t J t ) C t ( )
ρ t 1 | N = ρ t 1 + t 1 ( ρ t | N ρ t ( ) )
t 1 = C t 1 G t 1 T C t ( ) 1
S k + 1 = S k ( 2 I A S k )
Φ ^ k = Φ k × (1+ ξ k )

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