Abstract

Mesoscopic fluorescence molecular tomography (MFMT) is a novel imaging technique that aims at obtaining the 3-D distribution of molecular probes inside biological tissues at depths of a few millimeters. To achieve high resolution, around 100-150μm scale in turbid samples, dense spatial sampling strategies are required. However, a large number of optodes leads to sizable forward and inverse problems that can be challenging to compute efficiently. In this work, we propose a two-step data reduction strategy to accelerate the inverse problem and improve robustness. First, data selection is performed via signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) criteria. Then principal component analysis (PCA) is applied to further reduce the size of the sensitivity matrix. We perform numerical simulations and phantom experiments to validate the effectiveness of the proposed strategy. In both in silico and in vitro cases, we are able to significantly improve the quality of MFMT reconstructions while reducing the computation times by close to a factor of two.

© 2017 Optical Society of America

Full Article  |  PDF Article
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References

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2017 (1)

Q. Pian, R. Yao, N. Sinsuebphon, and X. Intes, “Compressive Hyperspectral Time-resolved Wide-Field Fluorescence Lifetime Imaging,” Nat. Photonics.  11411–417 (2017).

2016 (2)

M. S. Ozturk, C.-W. Chen, R. Ji, L. Zhao, B.-N. B. Nguyen, J. P. Fisher, Y. Chen, and X. Intes, “Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues,” Ann. Biomed. Eng. 44(3), 667–679 (2016).
[Crossref] [PubMed]

Q. Tang, V. Tsytsarev, A. Frank, Y. Wu, C. W. Chen, R. S. Erzurumlu, and Y. Chen, “In Vivo Mesoscopic Voltage-Sensitive Dye Imaging of Brain Activation,” Sci. Rep. 6(1), 25269 (2016).
[Crossref] [PubMed]

2015 (4)

2014 (2)

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

M. S. Ozturk, D. Rohrbach, U. Sunar, and X. Intes, “Mesoscopic fluorescence tomography of a photosensitizer (HPPH) 3D biodistribution in skin cancer,” Acad. Radiol. 21(2), 271–280 (2014).
[Crossref] [PubMed]

2013 (3)

2012 (1)

L. Zhao, V. K. Lee, S.-S. Yoo, G. Dai, and X. Intes, “The integration of 3-D cell printing and mesoscopic fluorescence molecular tomography of vascular constructs within thick hydrogel scaffolds,” Biomaterials 33(21), 5325–5332 (2012).
[Crossref] [PubMed]

2011 (3)

S. Björn, K. H. Englmeier, V. Ntziachristos, and R. Schulz, “Reconstruction of fluorescence distribution hidden in biological tissue using mesoscopic epifluorescence tomography,” J. Biomed. Opt. 16(4), 046005 (2011).
[Crossref] [PubMed]

J. Chen and X. Intes, “Comparison of Monte Carlo methods for fluorescence molecular tomography-computational efficiency,” Med. Phys. 38(10), 5788–5798 (2011).
[Crossref] [PubMed]

J. S. Silva, J. Cancela, and L. Teixeira, “Fast volumetric registration method for tumor follow-up in pulmonary CT exams,” J. Appl. Clin. Med. Phys. 12(2), 3450 (2011).
[Crossref] [PubMed]

2010 (1)

2009 (4)

2004 (1)

2000 (1)

Arridge, S. R.

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25(12), 123010 (2009).
[Crossref]

Bai, J.

Beaumont, E.

Beck, A.

A. Beck and M. Teboulle, “A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems,” SIAM J. Imaging Sci. 2(1), 183–202 (2009).
[Crossref]

Björn, S.

S. Björn, K. H. Englmeier, V. Ntziachristos, and R. Schulz, “Reconstruction of fluorescence distribution hidden in biological tissue using mesoscopic epifluorescence tomography,” J. Biomed. Opt. 16(4), 046005 (2011).
[Crossref] [PubMed]

Boas, D.

Boas, D. A.

Cable, A.

Cancela, J.

J. S. Silva, J. Cancela, and L. Teixeira, “Fast volumetric registration method for tumor follow-up in pulmonary CT exams,” J. Appl. Clin. Med. Phys. 12(2), 3450 (2011).
[Crossref] [PubMed]

Cao, X.

Chen, C. W.

Q. Tang, V. Tsytsarev, A. Frank, Y. Wu, C. W. Chen, R. S. Erzurumlu, and Y. Chen, “In Vivo Mesoscopic Voltage-Sensitive Dye Imaging of Brain Activation,” Sci. Rep. 6(1), 25269 (2016).
[Crossref] [PubMed]

Chen, C.-W.

M. S. Ozturk, C.-W. Chen, R. Ji, L. Zhao, B.-N. B. Nguyen, J. P. Fisher, Y. Chen, and X. Intes, “Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues,” Ann. Biomed. Eng. 44(3), 667–679 (2016).
[Crossref] [PubMed]

Chen, J.

J. Chen and X. Intes, “Comparison of Monte Carlo methods for fluorescence molecular tomography-computational efficiency,” Med. Phys. 38(10), 5788–5798 (2011).
[Crossref] [PubMed]

Chen, Y.

M. S. Ozturk, C.-W. Chen, R. Ji, L. Zhao, B.-N. B. Nguyen, J. P. Fisher, Y. Chen, and X. Intes, “Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues,” Ann. Biomed. Eng. 44(3), 667–679 (2016).
[Crossref] [PubMed]

Q. Tang, V. Tsytsarev, A. Frank, Y. Wu, C. W. Chen, R. S. Erzurumlu, and Y. Chen, “In Vivo Mesoscopic Voltage-Sensitive Dye Imaging of Brain Activation,” Sci. Rep. 6(1), 25269 (2016).
[Crossref] [PubMed]

S. Yuan, Q. Li, J. Jiang, A. Cable, and Y. Chen, “Three-dimensional coregistered optical coherence tomography and line-scanning fluorescence laminar optical tomography,” Opt. Lett. 34(11), 1615–1617 (2009).
[Crossref] [PubMed]

Cong, W.

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-Resolution Mesoscopic Fluorescence Molecular Tomography Based on Compressive Sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

Dai, G.

M. S. Ozturk, V. K. Lee, L. Zhao, G. Dai, and X. Intes, “Mesoscopic fluorescence molecular tomography of reporter genes in bioprinted thick tissue,” J. Biomed. Opt. 18(10), 100501 (2013).
[Crossref] [PubMed]

L. Zhao, V. K. Lee, S.-S. Yoo, G. Dai, and X. Intes, “The integration of 3-D cell printing and mesoscopic fluorescence molecular tomography of vascular constructs within thick hydrogel scaffolds,” Biomaterials 33(21), 5325–5332 (2012).
[Crossref] [PubMed]

Dale, A. M.

Dubeau, S.

Dunn, A.

Dunn, A. K.

Englmeier, K. H.

S. Björn, K. H. Englmeier, V. Ntziachristos, and R. Schulz, “Reconstruction of fluorescence distribution hidden in biological tissue using mesoscopic epifluorescence tomography,” J. Biomed. Opt. 16(4), 046005 (2011).
[Crossref] [PubMed]

Erzurumlu, R. S.

Q. Tang, V. Tsytsarev, A. Frank, Y. Wu, C. W. Chen, R. S. Erzurumlu, and Y. Chen, “In Vivo Mesoscopic Voltage-Sensitive Dye Imaging of Brain Activation,” Sci. Rep. 6(1), 25269 (2016).
[Crossref] [PubMed]

Fang, Q.

Fisher, J. P.

M. S. Ozturk, C.-W. Chen, R. Ji, L. Zhao, B.-N. B. Nguyen, J. P. Fisher, Y. Chen, and X. Intes, “Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues,” Ann. Biomed. Eng. 44(3), 667–679 (2016).
[Crossref] [PubMed]

Frank, A.

Q. Tang, V. Tsytsarev, A. Frank, Y. Wu, C. W. Chen, R. S. Erzurumlu, and Y. Chen, “In Vivo Mesoscopic Voltage-Sensitive Dye Imaging of Brain Activation,” Sci. Rep. 6(1), 25269 (2016).
[Crossref] [PubMed]

Guevara, E.

Hillman, E. M.

Intes, X.

Q. Pian, R. Yao, N. Sinsuebphon, and X. Intes, “Compressive Hyperspectral Time-resolved Wide-Field Fluorescence Lifetime Imaging,” Nat. Photonics.  11411–417 (2017).

M. S. Ozturk, C.-W. Chen, R. Ji, L. Zhao, B.-N. B. Nguyen, J. P. Fisher, Y. Chen, and X. Intes, “Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues,” Ann. Biomed. Eng. 44(3), 667–679 (2016).
[Crossref] [PubMed]

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-Resolution Mesoscopic Fluorescence Molecular Tomography Based on Compressive Sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

R. Yao, Q. Pian, and X. Intes, “Wide-field fluorescence molecular tomography with compressive sensing based preconditioning,” Biomed. Opt. Express 6(12), 4887–4898 (2015).
[Crossref] [PubMed]

Q. Pian, R. Yao, L. Zhao, and X. Intes, “Hyperspectral time-resolved wide-field fluorescence molecular tomography based on structured light and single-pixel detection,” Opt. Lett. 40(3), 431–434 (2015).
[Crossref] [PubMed]

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

M. S. Ozturk, D. Rohrbach, U. Sunar, and X. Intes, “Mesoscopic fluorescence tomography of a photosensitizer (HPPH) 3D biodistribution in skin cancer,” Acad. Radiol. 21(2), 271–280 (2014).
[Crossref] [PubMed]

M. S. Ozturk, V. K. Lee, L. Zhao, G. Dai, and X. Intes, “Mesoscopic fluorescence molecular tomography of reporter genes in bioprinted thick tissue,” J. Biomed. Opt. 18(10), 100501 (2013).
[Crossref] [PubMed]

L. Zhao, V. K. Lee, S.-S. Yoo, G. Dai, and X. Intes, “The integration of 3-D cell printing and mesoscopic fluorescence molecular tomography of vascular constructs within thick hydrogel scaffolds,” Biomaterials 33(21), 5325–5332 (2012).
[Crossref] [PubMed]

J. Chen and X. Intes, “Comparison of Monte Carlo methods for fluorescence molecular tomography-computational efficiency,” Med. Phys. 38(10), 5788–5798 (2011).
[Crossref] [PubMed]

Ji, R.

M. S. Ozturk, C.-W. Chen, R. Ji, L. Zhao, B.-N. B. Nguyen, J. P. Fisher, Y. Chen, and X. Intes, “Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues,” Ann. Biomed. Eng. 44(3), 667–679 (2016).
[Crossref] [PubMed]

Jiang, J.

Lee, V. K.

M. S. Ozturk, V. K. Lee, L. Zhao, G. Dai, and X. Intes, “Mesoscopic fluorescence molecular tomography of reporter genes in bioprinted thick tissue,” J. Biomed. Opt. 18(10), 100501 (2013).
[Crossref] [PubMed]

L. Zhao, V. K. Lee, S.-S. Yoo, G. Dai, and X. Intes, “The integration of 3-D cell printing and mesoscopic fluorescence molecular tomography of vascular constructs within thick hydrogel scaffolds,” Biomaterials 33(21), 5325–5332 (2012).
[Crossref] [PubMed]

Lesage, F.

Li, Q.

Liu, F.

Luo, J.

Mohajerani, P.

Nguyen, B.-N. B.

M. S. Ozturk, C.-W. Chen, R. Ji, L. Zhao, B.-N. B. Nguyen, J. P. Fisher, Y. Chen, and X. Intes, “Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues,” Ann. Biomed. Eng. 44(3), 667–679 (2016).
[Crossref] [PubMed]

Ntziachristos, V.

P. Mohajerani and V. Ntziachristos, “Compression of Born ratio for fluorescence molecular tomography/x-ray computed tomography hybrid imaging: methodology and in vivo validation,” Opt. Lett. 38(13), 2324–2326 (2013).
[Crossref] [PubMed]

S. Björn, K. H. Englmeier, V. Ntziachristos, and R. Schulz, “Reconstruction of fluorescence distribution hidden in biological tissue using mesoscopic epifluorescence tomography,” J. Biomed. Opt. 16(4), 046005 (2011).
[Crossref] [PubMed]

Ouakli, N.

Ozturk, M. S.

M. S. Ozturk, C.-W. Chen, R. Ji, L. Zhao, B.-N. B. Nguyen, J. P. Fisher, Y. Chen, and X. Intes, “Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues,” Ann. Biomed. Eng. 44(3), 667–679 (2016).
[Crossref] [PubMed]

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-Resolution Mesoscopic Fluorescence Molecular Tomography Based on Compressive Sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

M. S. Ozturk, D. Rohrbach, U. Sunar, and X. Intes, “Mesoscopic fluorescence tomography of a photosensitizer (HPPH) 3D biodistribution in skin cancer,” Acad. Radiol. 21(2), 271–280 (2014).
[Crossref] [PubMed]

M. S. Ozturk, V. K. Lee, L. Zhao, G. Dai, and X. Intes, “Mesoscopic fluorescence molecular tomography of reporter genes in bioprinted thick tissue,” J. Biomed. Opt. 18(10), 100501 (2013).
[Crossref] [PubMed]

Pian, Q.

Rohrbach, D.

M. S. Ozturk, D. Rohrbach, U. Sunar, and X. Intes, “Mesoscopic fluorescence tomography of a photosensitizer (HPPH) 3D biodistribution in skin cancer,” Acad. Radiol. 21(2), 271–280 (2014).
[Crossref] [PubMed]

Schotland, J. C.

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25(12), 123010 (2009).
[Crossref]

Schulz, R.

S. Björn, K. H. Englmeier, V. Ntziachristos, and R. Schulz, “Reconstruction of fluorescence distribution hidden in biological tissue using mesoscopic epifluorescence tomography,” J. Biomed. Opt. 16(4), 046005 (2011).
[Crossref] [PubMed]

Shi, J.

Silva, J. S.

J. S. Silva, J. Cancela, and L. Teixeira, “Fast volumetric registration method for tumor follow-up in pulmonary CT exams,” J. Appl. Clin. Med. Phys. 12(2), 3450 (2011).
[Crossref] [PubMed]

Sinsuebphon, N.

Q. Pian, R. Yao, N. Sinsuebphon, and X. Intes, “Compressive Hyperspectral Time-resolved Wide-Field Fluorescence Lifetime Imaging,” Nat. Photonics.  11411–417 (2017).

Sunar, U.

M. S. Ozturk, D. Rohrbach, U. Sunar, and X. Intes, “Mesoscopic fluorescence tomography of a photosensitizer (HPPH) 3D biodistribution in skin cancer,” Acad. Radiol. 21(2), 271–280 (2014).
[Crossref] [PubMed]

Tang, Q.

Q. Tang, V. Tsytsarev, A. Frank, Y. Wu, C. W. Chen, R. S. Erzurumlu, and Y. Chen, “In Vivo Mesoscopic Voltage-Sensitive Dye Imaging of Brain Activation,” Sci. Rep. 6(1), 25269 (2016).
[Crossref] [PubMed]

Teboulle, M.

A. Beck and M. Teboulle, “A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems,” SIAM J. Imaging Sci. 2(1), 183–202 (2009).
[Crossref]

Teixeira, L.

J. S. Silva, J. Cancela, and L. Teixeira, “Fast volumetric registration method for tumor follow-up in pulmonary CT exams,” J. Appl. Clin. Med. Phys. 12(2), 3450 (2011).
[Crossref] [PubMed]

Tsytsarev, V.

Q. Tang, V. Tsytsarev, A. Frank, Y. Wu, C. W. Chen, R. S. Erzurumlu, and Y. Chen, “In Vivo Mesoscopic Voltage-Sensitive Dye Imaging of Brain Activation,” Sci. Rep. 6(1), 25269 (2016).
[Crossref] [PubMed]

Wang, G.

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-Resolution Mesoscopic Fluorescence Molecular Tomography Based on Compressive Sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

Wang, X.

Wu, Y.

Q. Tang, V. Tsytsarev, A. Frank, Y. Wu, C. W. Chen, R. S. Erzurumlu, and Y. Chen, “In Vivo Mesoscopic Voltage-Sensitive Dye Imaging of Brain Activation,” Sci. Rep. 6(1), 25269 (2016).
[Crossref] [PubMed]

Yang, F.

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-Resolution Mesoscopic Fluorescence Molecular Tomography Based on Compressive Sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

Yang, H.

Yao, R.

Yoo, S.-S.

L. Zhao, V. K. Lee, S.-S. Yoo, G. Dai, and X. Intes, “The integration of 3-D cell printing and mesoscopic fluorescence molecular tomography of vascular constructs within thick hydrogel scaffolds,” Biomaterials 33(21), 5325–5332 (2012).
[Crossref] [PubMed]

Yuan, S.

Zhang, B.

Zhang, J.

Zhao, L.

M. S. Ozturk, C.-W. Chen, R. Ji, L. Zhao, B.-N. B. Nguyen, J. P. Fisher, Y. Chen, and X. Intes, “Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues,” Ann. Biomed. Eng. 44(3), 667–679 (2016).
[Crossref] [PubMed]

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-Resolution Mesoscopic Fluorescence Molecular Tomography Based on Compressive Sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

Q. Pian, R. Yao, L. Zhao, and X. Intes, “Hyperspectral time-resolved wide-field fluorescence molecular tomography based on structured light and single-pixel detection,” Opt. Lett. 40(3), 431–434 (2015).
[Crossref] [PubMed]

L. Zhao, H. Yang, W. Cong, G. Wang, and X. Intes, “Lp regularization for early gate fluorescence molecular tomography,” Opt. Lett. 39(14), 4156–4159 (2014).
[Crossref] [PubMed]

M. S. Ozturk, V. K. Lee, L. Zhao, G. Dai, and X. Intes, “Mesoscopic fluorescence molecular tomography of reporter genes in bioprinted thick tissue,” J. Biomed. Opt. 18(10), 100501 (2013).
[Crossref] [PubMed]

L. Zhao, V. K. Lee, S.-S. Yoo, G. Dai, and X. Intes, “The integration of 3-D cell printing and mesoscopic fluorescence molecular tomography of vascular constructs within thick hydrogel scaffolds,” Biomaterials 33(21), 5325–5332 (2012).
[Crossref] [PubMed]

Zuo, S.

Acad. Radiol. (1)

M. S. Ozturk, D. Rohrbach, U. Sunar, and X. Intes, “Mesoscopic fluorescence tomography of a photosensitizer (HPPH) 3D biodistribution in skin cancer,” Acad. Radiol. 21(2), 271–280 (2014).
[Crossref] [PubMed]

Ann. Biomed. Eng. (1)

M. S. Ozturk, C.-W. Chen, R. Ji, L. Zhao, B.-N. B. Nguyen, J. P. Fisher, Y. Chen, and X. Intes, “Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues,” Ann. Biomed. Eng. 44(3), 667–679 (2016).
[Crossref] [PubMed]

Biomaterials (1)

L. Zhao, V. K. Lee, S.-S. Yoo, G. Dai, and X. Intes, “The integration of 3-D cell printing and mesoscopic fluorescence molecular tomography of vascular constructs within thick hydrogel scaffolds,” Biomaterials 33(21), 5325–5332 (2012).
[Crossref] [PubMed]

Biomed. Opt. Express (2)

Chin. Opt. Lett. (1)

IEEE Trans. Biomed. Eng. (1)

F. Yang, M. S. Ozturk, L. Zhao, W. Cong, G. Wang, and X. Intes, “High-Resolution Mesoscopic Fluorescence Molecular Tomography Based on Compressive Sensing,” IEEE Trans. Biomed. Eng. 62(1), 248–255 (2015).
[Crossref] [PubMed]

Inverse Probl. (1)

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25(12), 123010 (2009).
[Crossref]

J. Appl. Clin. Med. Phys. (1)

J. S. Silva, J. Cancela, and L. Teixeira, “Fast volumetric registration method for tumor follow-up in pulmonary CT exams,” J. Appl. Clin. Med. Phys. 12(2), 3450 (2011).
[Crossref] [PubMed]

J. Biomed. Opt. (2)

S. Björn, K. H. Englmeier, V. Ntziachristos, and R. Schulz, “Reconstruction of fluorescence distribution hidden in biological tissue using mesoscopic epifluorescence tomography,” J. Biomed. Opt. 16(4), 046005 (2011).
[Crossref] [PubMed]

M. S. Ozturk, V. K. Lee, L. Zhao, G. Dai, and X. Intes, “Mesoscopic fluorescence molecular tomography of reporter genes in bioprinted thick tissue,” J. Biomed. Opt. 18(10), 100501 (2013).
[Crossref] [PubMed]

Med. Phys. (1)

J. Chen and X. Intes, “Comparison of Monte Carlo methods for fluorescence molecular tomography-computational efficiency,” Med. Phys. 38(10), 5788–5798 (2011).
[Crossref] [PubMed]

Nat. Photonics (1)

Q. Pian, R. Yao, N. Sinsuebphon, and X. Intes, “Compressive Hyperspectral Time-resolved Wide-Field Fluorescence Lifetime Imaging,” Nat. Photonics.  11411–417 (2017).

Opt. Express (2)

Opt. Lett. (6)

Sci. Rep. (1)

Q. Tang, V. Tsytsarev, A. Frank, Y. Wu, C. W. Chen, R. S. Erzurumlu, and Y. Chen, “In Vivo Mesoscopic Voltage-Sensitive Dye Imaging of Brain Activation,” Sci. Rep. 6(1), 25269 (2016).
[Crossref] [PubMed]

SIAM J. Imaging Sci. (1)

A. Beck and M. Teboulle, “A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems,” SIAM J. Imaging Sci. 2(1), 183–202 (2009).
[Crossref]

Other (2)

Q. Fang, “Monte Carlo eXtreme (MCX),” http://mcx.space/.

M. S. Ozturk, X. Intes, and V. K. Lee, “Longitudinal Volumetric Assessment of Glioblastoma Brain Tumor in 3D Bio-Printed Environment by Mesoscopic Fluorescence Molecular Tomography,” in Optics and the Brain, (Optical Society of America, 2016), JM3A. 46.

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Figures (8)

Fig. 1
Fig. 1

Optical schematic diagram of the 2nd Generation MFMT system. The de-scanned excitation (Ex) light and 2D detector array (EMCCD) compose the system backbone. Polarizing beam splitter (PBS) and cross-polarizers (P, A) minimize specular reflection from the sample surface along with the fluorescence filter (F). Scan lens (SL) and a tube lens form a conjugate image plane and 4F relay system forms the final image on the EMCCD. In higher binning configurations, the spatial integration of the photons deteriorates the dynamic range so a reflection block (RB) is introduced into the system. One set of images is completed after completing a raster scan.

Fig. 2
Fig. 2

Schematic diagram of locations of source (blue square), detectors binned from EMCCD camera (green squares), scanning positions on the specimen (blue dots), and scanning path (blue arrows) of the 2nd generation MFMT system.

Fig. 3
Fig. 3

The numerical phantom designed to mimic vascular structure with a main bio-printed vascular channel and sprouting capillaries. The main trunk has a diameter of 400μm and the off-shoot branches are 200μm in diameter and separated with one voxel spacing to test the resolution of the proposed method. (a), (b) and (c) are the full view, xy view, and xz view of the phantom, respectively. (d) and (e) show the SNR and CNR levels of the synthetic measurements.

Fig. 4
Fig. 4

Reconstruction results and evaluation metrics under random sampling. (a) plots the average metrics of reconstructions with different numbers of remaining measurements. (b) and (c) show two visual reconstruction results with 5,000 and 30,000 measurements left, respectively.

Fig. 5
Fig. 5

Reconstruction results and evaluation metrics under SNR-based data reduction strategy. (a) gives the distribution of the 48 detectors’ SNR level in a case scenario. (b) plots the computation time and measurements left corresponding to different threshold of SNR. (c) plots the 4 metrics versus different threshold of SNR. (d)-(f) show 3 visual reconstructions with retained measurements after filtering by the specific SNR threshold of 1.99, 2.02, and 2.05, respectively.

Fig. 6
Fig. 6

Reconstruction results and evaluation metrics under CNR-based data reduction strategy. (a) gives the distribution of the 48 detectors’ CNR level in a case scenario. (b) plots the computation time and measurements left corresponding to different thresholds of CNR. (c) plots the 4 metrics versus different thresholds of CNR. (d)-(f) show 3 visual reconstructions with retained measurements after filtering by the specific CNR threshold of 6.5, 7.5, and 10, respectively.

Fig. 7
Fig. 7

Reconstruction results and evaluation metrics under PCA-based data reduction strategy in a simulation. (a) gives the relationship between variance explained and different principal components in a simulation. (b) plots the computation time and 4 metrics versus measurements left corresponding to different thresholds of CPV. (c)-(g) show 5 visual reconstructions with retained measurements after filtering by the specific CPV threshold of 67.4, 81.2, 90.7, 95.8, and 97.5, respectively.

Fig. 8
Fig. 8

Phantom reconstruction under the proposed redundant data reduction method. (a) segmented micro-MRI slice and reconstruction across x-y plane. (b) segmented micro-MRI slice and reconstuction across y-z plane. (c) 3D overlaid image of micro-MRI and optimal reconstruction. (d) reconstruction result using full data. (e)reconstruction result based on the remaining data after noise suppression. (f) reconstruction results using the retained data after both noise suppression and PCA processing. (g) and (h) are the distributions of SNR and CNR of 48 detectors.

Tables (3)

Tables Icon

Table 1 Quantification results of the two methods in the numerical simulation (CPV = 95.8%)

Tables Icon

Table 2 Quantification results of the methods adopted for synthetic measurements

Tables Icon

Table 3 Quantification results of the methods for experimental data (threshold of SNR = 2.0/CNR = 6.5/CPV = 95%)

Equations (14)

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W( r s , r d ,r )= G x ( r s ,r )× G m ( r, r d ).
U( r s , r d )= Ω W( r s , r d ,r )η( r )d r 3 .
Ax=b. 
min{ Ax b 2 2 +λ x 1 }
SNR= μ( | S f S b | ) σ( | S b S r | ) .
CNR= max( | S f S b | )min( | S f S b | ) σ( | S b S r | ) .
C=PΛ P T .
P T Ax= P T b.
A x= P k T Ax= P k T b= b .
CPV k = i=1 k λ i i=1 m λ i .
nSSD( A,B )=1 1 N i X j Y k Z [ A( i,j,k )B( i,j,k ) ] 2 .
nSAD( A,B )=1 1 N i X j Y k Z | A( i,j,k )B( i,j,k ) |.
R=  i X j Y k Z A( i,j,k )×B( i,j,k ) i X j Y k Z A ( i,j,k ) 2 × i X j Y k Z B ( i,j,k ) 2 .
nD= 1 1 N i X j Y k Z A ' ( i,j,k ) B ' ( i,j,k ).