Abstract

In functional near-infrared spectroscopy (fNIRS), the conventional indirect approaches first separately recover the spatial distribution of the changes in the optical properties at every time point, and then extract the activation levels by a time-course analysis process at every site. In the tomographic implementation of fNIRS, i.e., diffuse optical tomography (DOT), these approaches not only suffer from the ill-posedness of the optical inversions and error propagation between the two successive steps, but also fail to achieve satisfactory temporal resolution due to the requirement for a complete data set. To cope with the above adversities of the indirect approaches, we propose herein a direct approach to tomographically reconstructing the activation levels by incorporating a Kalman scheme. Dynamic simulative and phantom experiments were conducted for the performance validation of the proposed approach, demonstrating its potentials to improve the calculated images and to relax the speed limitation of the instruments.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2018 (2)

Y. Zhang, L. Huang, N. Zhang, H. Tian, and J. Zhu, “Experimental study on the sensitive depth of backwards detected light in turbid media,” Opt. Express 26(11), 14700–14709 (2018).
[Crossref] [PubMed]

X. Ding, B. Wang, D. Liu, Y. Zhang, J. He, H. Zhao, and F. Gao, “A three-wavelength multi-channel brain functional imager based on digital lock-in photon-counting technique,” SPIE Proc. 10480, 104800S (2018).

2017 (1)

B. Wang, W. Wan, Y. Wang, W. Ma, L. Zhang, J. Li, Z. Zhou, H. Zhao, and F. Gao, “An Lp (0 ≤ p ≤ 1)-norm regularized image reconstruction scheme for breast DOT with non-negative-constraint,” Biomed. Eng. Online 16(1), 32 (2017).
[Crossref] [PubMed]

2016 (2)

T. Sato, I. Nambu, K. Takeda, T. Aihara, O. Yamashita, Y. Isogaya, Y. Inoue, Y. Otaka, Y. Wada, M. Kawato, M. A. Sato, and R. Osu, “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” Neuroimage 141, 120–132 (2016).
[Crossref] [PubMed]

W. Chen, X. Wang, B. Wang, Y. Wang, Y. Zhang, H. Zhao, and F. Gao, “Lock-in-photon-counting-based highly-sensitive and large-dynamic imaging system for continuous-wave diffuse optical tomography,” Biomed. Opt. Express 7(2), 499–511 (2016).
[Crossref] [PubMed]

2015 (2)

2014 (9)

S. Tak and J. C. Ye, “Statistical analysis of fNIRS data: a comprehensive review,” Neuroimage 85(Pt 1), 72–91 (2014).
[Crossref] [PubMed]

C. Habermehl, J. Steinbrink, K. R. Müller, and S. Haufe, “Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography,” J. Biomed. Opt. 19(9), 96006 (2014).
[Crossref] [PubMed]

F. Scholkmann, S. Kleiser, A. J. Metz, R. Zimmermann, J. Mata Pavia, U. Wolf, and M. Wolf, “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” Neuroimage 85(Pt 1), 6–27 (2014).
[Crossref] [PubMed]

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

C. Chen, F. Tian, H. Liu, and J. Huang, “Diffuse optical tomography enhanced by clustered sparsity for functional brain imaging,” IEEE Trans. Med. Imaging 33(12), 2323–2331 (2014).
[Crossref] [PubMed]

J. Prakash, C. B. Shaw, R. Manjappa, R. Kanhirodan, and P. K. Yalavarthy, “Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction,” IEEE J. Sel. Top. Quant. 20, 74 (2014).

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex,” Biomed. Opt. Express 5(11), 3882–3900 (2014).
[Crossref] [PubMed]

2013 (2)

F. Scarpa, S. Brigadoi, S. Cutini, P. Scatturin, M. Zorzi, R. Dell’acqua, and G. Sparacino, “A reference-channel based methodology to improve estimation of event-related hemodynamic response from fNIRS measurements,” Neuroimage 72, 106–119 (2013).
[Crossref] [PubMed]

T. Shimokawa, T. Kosaka, O. Yamashita, N. Hiroe, T. Amita, Y. Inoue, and M. A. Sato, “Extended hierarchical Bayesian diffuse optical tomography for removing scalp artifact,” Biomed. Opt. Express 4(11), 2411–2432 (2013).
[Crossref] [PubMed]

2012 (3)

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[Crossref] [PubMed]

T. Shimokawa, T. Kosaka, O. Yamashita, N. Hiroe, T. Amita, Y. Inoue, and M. A. Sato, “Hierarchical Bayesian estimation improves depth accuracy and spatial resolution of diffuse optical tomography,” Opt. Express 20(18), 20427–20446 (2012).
[Crossref] [PubMed]

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

2010 (3)

S. Lloyd-Fox, A. Blasi, and C. E. Elwell, “Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy,” Neurosci. Biobehav. Rev. 34(3), 269–284 (2010).
[Crossref] [PubMed]

X. S. Hu, K. S. Hong, S. S. Ge, and M. Y. Jeong, “Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy,” Biomed. Eng. Online 9(1), 82 (2010).
[Crossref] [PubMed]

N. M. Gregg, B. R. White, B. W. Zeff, A. J. Berger, and J. P. Culver, “Brain specificity of diffuse optical imaging: improvements from superficial signal regression and tomography,” Front. Neuroenergetics 2(14), 14 (2010).
[Crossref] [PubMed]

2009 (2)

2008 (1)

2007 (2)

J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, “Activation detection in diffuse optical imaging by means of the general linear model,” Med. Image Anal. 11(6), 616–629 (2007).
[Crossref] [PubMed]

N. Cao, A. Nehorai, and M. Jacobs, “Image reconstruction for diffuse optical tomography using sparsity regularization and expectation-maximization algorithm,” Opt. Express 15(21), 13695–13708 (2007).
[Crossref] [PubMed]

2005 (1)

Y. Zhang, D. H. Brooks, and D. A. Boas, “A haemodynamic response function model in spatio-temporal diffuse optical tomography,” Phys. Med. Biol. 50(19), 4625–4644 (2005).
[Crossref] [PubMed]

2004 (1)

F. Gao, H. Zhao, Y. Tanikawa, and Y. Yamada, “Optical tomographic mapping of cerebral haemodynamics by means of time-domain detection: methodology and phantom validation,” Phys. Med. Biol. 49(6), 1055–1078 (2004).
[Crossref] [PubMed]

2003 (1)

G. Taga, K. Asakawa, A. Maki, Y. Konishi, and H. Koizumi, “Brain imaging in awake infants by near-infrared optical topography,” Proc. Natl. Acad. Sci. U.S.A. 100(19), 10722–10727 (2003).
[Crossref] [PubMed]

Aihara, T.

T. Sato, I. Nambu, K. Takeda, T. Aihara, O. Yamashita, Y. Isogaya, Y. Inoue, Y. Otaka, Y. Wada, M. Kawato, M. A. Sato, and R. Osu, “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” Neuroimage 141, 120–132 (2016).
[Crossref] [PubMed]

Amita, T.

Asakawa, K.

G. Taga, K. Asakawa, A. Maki, Y. Konishi, and H. Koizumi, “Brain imaging in awake infants by near-infrared optical topography,” Proc. Natl. Acad. Sci. U.S.A. 100(19), 10722–10727 (2003).
[Crossref] [PubMed]

Atsumori, H.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Becker, W.

W. Becker, Advanced Time-Correlated Single Photon Counting Techniques (Springer Science & Business Media) (2005).

Benali, H.

J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, “Activation detection in diffuse optical imaging by means of the general linear model,” Med. Image Anal. 11(6), 616–629 (2007).
[Crossref] [PubMed]

Berger, A. J.

N. M. Gregg, B. R. White, B. W. Zeff, A. J. Berger, and J. P. Culver, “Brain specificity of diffuse optical imaging: improvements from superficial signal regression and tomography,” Front. Neuroenergetics 2(14), 14 (2010).
[Crossref] [PubMed]

Blasi, A.

S. Lloyd-Fox, A. Blasi, and C. E. Elwell, “Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy,” Neurosci. Biobehav. Rev. 34(3), 269–284 (2010).
[Crossref] [PubMed]

Boas, D. A.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

T. J. Huppert, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt. 48(10), D280–D298 (2009).
[Crossref] [PubMed]

Q. Fang and D. A. Boas, “Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units,” Opt. Express 17(22), 20178–20190 (2009).
[PubMed]

Y. Zhang, D. H. Brooks, and D. A. Boas, “A haemodynamic response function model in spatio-temporal diffuse optical tomography,” Phys. Med. Biol. 50(19), 4625–4644 (2005).
[Crossref] [PubMed]

Bonomini, V.

Brigadoi, S.

F. Scarpa, S. Brigadoi, S. Cutini, P. Scatturin, M. Zorzi, R. Dell’acqua, and G. Sparacino, “A reference-channel based methodology to improve estimation of event-related hemodynamic response from fNIRS measurements,” Neuroimage 72, 106–119 (2013).
[Crossref] [PubMed]

Brooks, D. H.

Y. Zhang, D. H. Brooks, and D. A. Boas, “A haemodynamic response function model in spatio-temporal diffuse optical tomography,” Phys. Med. Biol. 50(19), 4625–4644 (2005).
[Crossref] [PubMed]

Cao, N.

Chapuisat, S.

J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, “Activation detection in diffuse optical imaging by means of the general linear model,” Med. Image Anal. 11(6), 616–629 (2007).
[Crossref] [PubMed]

Chen, C.

C. Chen, F. Tian, H. Liu, and J. Huang, “Diffuse optical tomography enhanced by clustered sparsity for functional brain imaging,” IEEE Trans. Med. Imaging 33(12), 2323–2331 (2014).
[Crossref] [PubMed]

Chen, W.

Cohen-Adad, J.

J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, “Activation detection in diffuse optical imaging by means of the general linear model,” Med. Image Anal. 11(6), 616–629 (2007).
[Crossref] [PubMed]

Contini, D.

Culver, J. P.

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Fast and efficient image reconstruction for high density diffuse optical imaging of the human brain,” Biomed. Opt. Express 6(11), 4567–4584 (2015).
[Crossref] [PubMed]

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex,” Biomed. Opt. Express 5(11), 3882–3900 (2014).
[Crossref] [PubMed]

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[Crossref] [PubMed]

N. M. Gregg, B. R. White, B. W. Zeff, A. J. Berger, and J. P. Culver, “Brain specificity of diffuse optical imaging: improvements from superficial signal regression and tomography,” Front. Neuroenergetics 2(14), 14 (2010).
[Crossref] [PubMed]

Cutini, S.

F. Scarpa, S. Brigadoi, S. Cutini, P. Scatturin, M. Zorzi, R. Dell’acqua, and G. Sparacino, “A reference-channel based methodology to improve estimation of event-related hemodynamic response from fNIRS measurements,” Neuroimage 72, 106–119 (2013).
[Crossref] [PubMed]

Dehghani, H.

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Fast and efficient image reconstruction for high density diffuse optical imaging of the human brain,” Biomed. Opt. Express 6(11), 4567–4584 (2015).
[Crossref] [PubMed]

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex,” Biomed. Opt. Express 5(11), 3882–3900 (2014).
[Crossref] [PubMed]

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[Crossref] [PubMed]

Dell’acqua, R.

F. Scarpa, S. Brigadoi, S. Cutini, P. Scatturin, M. Zorzi, R. Dell’acqua, and G. Sparacino, “A reference-channel based methodology to improve estimation of event-related hemodynamic response from fNIRS measurements,” Neuroimage 72, 106–119 (2013).
[Crossref] [PubMed]

Diamond, S. G.

Ding, X.

X. Ding, B. Wang, D. Liu, Y. Zhang, J. He, H. Zhao, and F. Gao, “A three-wavelength multi-channel brain functional imager based on digital lock-in photon-counting technique,” SPIE Proc. 10480, 104800S (2018).

Doyon, J.

J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, “Activation detection in diffuse optical imaging by means of the general linear model,” Med. Image Anal. 11(6), 616–629 (2007).
[Crossref] [PubMed]

Eggebrecht, A. T.

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Fast and efficient image reconstruction for high density diffuse optical imaging of the human brain,” Biomed. Opt. Express 6(11), 4567–4584 (2015).
[Crossref] [PubMed]

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex,” Biomed. Opt. Express 5(11), 3882–3900 (2014).
[Crossref] [PubMed]

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[Crossref] [PubMed]

Elwell, C. E.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

S. Lloyd-Fox, A. Blasi, and C. E. Elwell, “Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy,” Neurosci. Biobehav. Rev. 34(3), 269–284 (2010).
[Crossref] [PubMed]

Fang, Q.

Ferradal, S. L.

Ferrari, M.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

Franceschini, M. A.

Funane, T.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Gao, F.

X. Ding, B. Wang, D. Liu, Y. Zhang, J. He, H. Zhao, and F. Gao, “A three-wavelength multi-channel brain functional imager based on digital lock-in photon-counting technique,” SPIE Proc. 10480, 104800S (2018).

B. Wang, W. Wan, Y. Wang, W. Ma, L. Zhang, J. Li, Z. Zhou, H. Zhao, and F. Gao, “An Lp (0 ≤ p ≤ 1)-norm regularized image reconstruction scheme for breast DOT with non-negative-constraint,” Biomed. Eng. Online 16(1), 32 (2017).
[Crossref] [PubMed]

W. Chen, X. Wang, B. Wang, Y. Wang, Y. Zhang, H. Zhao, and F. Gao, “Lock-in-photon-counting-based highly-sensitive and large-dynamic imaging system for continuous-wave diffuse optical tomography,” Biomed. Opt. Express 7(2), 499–511 (2016).
[Crossref] [PubMed]

F. Gao, H. Zhao, L. Zhang, Y. Tanikawa, A. Marjono, and Y. Yamada, “A self-normalized, full time-resolved method for fluorescence diffuse optical tomography,” Opt. Express 16(17), 13104–13121 (2008).
[Crossref] [PubMed]

F. Gao, H. Zhao, Y. Tanikawa, and Y. Yamada, “Optical tomographic mapping of cerebral haemodynamics by means of time-domain detection: methodology and phantom validation,” Phys. Med. Biol. 49(6), 1055–1078 (2004).
[Crossref] [PubMed]

Ge, S. S.

X. S. Hu, K. S. Hong, S. S. Ge, and M. Y. Jeong, “Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy,” Biomed. Eng. Online 9(1), 82 (2010).
[Crossref] [PubMed]

Gregg, N. M.

N. M. Gregg, B. R. White, B. W. Zeff, A. J. Berger, and J. P. Culver, “Brain specificity of diffuse optical imaging: improvements from superficial signal regression and tomography,” Front. Neuroenergetics 2(14), 14 (2010).
[Crossref] [PubMed]

Habermehl, C.

C. Habermehl, J. Steinbrink, K. R. Müller, and S. Haufe, “Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography,” J. Biomed. Opt. 19(9), 96006 (2014).
[Crossref] [PubMed]

Hassanpour, M. S.

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

Haufe, S.

C. Habermehl, J. Steinbrink, K. R. Müller, and S. Haufe, “Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography,” J. Biomed. Opt. 19(9), 96006 (2014).
[Crossref] [PubMed]

He, J.

X. Ding, B. Wang, D. Liu, Y. Zhang, J. He, H. Zhao, and F. Gao, “A three-wavelength multi-channel brain functional imager based on digital lock-in photon-counting technique,” SPIE Proc. 10480, 104800S (2018).

Hershey, T.

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

Hiroe, N.

Hong, K. S.

X. S. Hu, K. S. Hong, S. S. Ge, and M. Y. Jeong, “Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy,” Biomed. Eng. Online 9(1), 82 (2010).
[Crossref] [PubMed]

Hu, X. S.

X. S. Hu, K. S. Hong, S. S. Ge, and M. Y. Jeong, “Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy,” Biomed. Eng. Online 9(1), 82 (2010).
[Crossref] [PubMed]

Huang, J.

C. Chen, F. Tian, H. Liu, and J. Huang, “Diffuse optical tomography enhanced by clustered sparsity for functional brain imaging,” IEEE Trans. Med. Imaging 33(12), 2323–2331 (2014).
[Crossref] [PubMed]

Huang, L.

Huppert, T. J.

Ieva, F.

Inoue, Y.

Isogaya, Y.

T. Sato, I. Nambu, K. Takeda, T. Aihara, O. Yamashita, Y. Isogaya, Y. Inoue, Y. Otaka, Y. Wada, M. Kawato, M. A. Sato, and R. Osu, “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” Neuroimage 141, 120–132 (2016).
[Crossref] [PubMed]

Jacobs, M.

Jeong, M. Y.

X. S. Hu, K. S. Hong, S. S. Ge, and M. Y. Jeong, “Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy,” Biomed. Eng. Online 9(1), 82 (2010).
[Crossref] [PubMed]

Kanhirodan, R.

J. Prakash, C. B. Shaw, R. Manjappa, R. Kanhirodan, and P. K. Yalavarthy, “Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction,” IEEE J. Sel. Top. Quant. 20, 74 (2014).

Katura, T.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Kawato, M.

T. Sato, I. Nambu, K. Takeda, T. Aihara, O. Yamashita, Y. Isogaya, Y. Inoue, Y. Otaka, Y. Wada, M. Kawato, M. A. Sato, and R. Osu, “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” Neuroimage 141, 120–132 (2016).
[Crossref] [PubMed]

Kiguchi, M.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Kleiser, S.

F. Scholkmann, S. Kleiser, A. J. Metz, R. Zimmermann, J. Mata Pavia, U. Wolf, and M. Wolf, “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” Neuroimage 85(Pt 1), 6–27 (2014).
[Crossref] [PubMed]

Koizumi, H.

G. Taga, K. Asakawa, A. Maki, Y. Konishi, and H. Koizumi, “Brain imaging in awake infants by near-infrared optical topography,” Proc. Natl. Acad. Sci. U.S.A. 100(19), 10722–10727 (2003).
[Crossref] [PubMed]

Konishi, Y.

G. Taga, K. Asakawa, A. Maki, Y. Konishi, and H. Koizumi, “Brain imaging in awake infants by near-infrared optical topography,” Proc. Natl. Acad. Sci. U.S.A. 100(19), 10722–10727 (2003).
[Crossref] [PubMed]

Kosaka, T.

Lesage, F.

J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, “Activation detection in diffuse optical imaging by means of the general linear model,” Med. Image Anal. 11(6), 616–629 (2007).
[Crossref] [PubMed]

Li, J.

B. Wang, W. Wan, Y. Wang, W. Ma, L. Zhang, J. Li, Z. Zhou, H. Zhao, and F. Gao, “An Lp (0 ≤ p ≤ 1)-norm regularized image reconstruction scheme for breast DOT with non-negative-constraint,” Biomed. Eng. Online 16(1), 32 (2017).
[Crossref] [PubMed]

Lina, J. M.

J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, “Activation detection in diffuse optical imaging by means of the general linear model,” Med. Image Anal. 11(6), 616–629 (2007).
[Crossref] [PubMed]

Liu, D.

X. Ding, B. Wang, D. Liu, Y. Zhang, J. He, H. Zhao, and F. Gao, “A three-wavelength multi-channel brain functional imager based on digital lock-in photon-counting technique,” SPIE Proc. 10480, 104800S (2018).

Liu, H.

C. Chen, F. Tian, H. Liu, and J. Huang, “Diffuse optical tomography enhanced by clustered sparsity for functional brain imaging,” IEEE Trans. Med. Imaging 33(12), 2323–2331 (2014).
[Crossref] [PubMed]

Lloyd-Fox, S.

S. Lloyd-Fox, A. Blasi, and C. E. Elwell, “Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy,” Neurosci. Biobehav. Rev. 34(3), 269–284 (2010).
[Crossref] [PubMed]

Ma, W.

B. Wang, W. Wan, Y. Wang, W. Ma, L. Zhang, J. Li, Z. Zhou, H. Zhao, and F. Gao, “An Lp (0 ≤ p ≤ 1)-norm regularized image reconstruction scheme for breast DOT with non-negative-constraint,” Biomed. Eng. Online 16(1), 32 (2017).
[Crossref] [PubMed]

Maki, A.

G. Taga, K. Asakawa, A. Maki, Y. Konishi, and H. Koizumi, “Brain imaging in awake infants by near-infrared optical topography,” Proc. Natl. Acad. Sci. U.S.A. 100(19), 10722–10727 (2003).
[Crossref] [PubMed]

Manjappa, R.

J. Prakash, C. B. Shaw, R. Manjappa, R. Kanhirodan, and P. K. Yalavarthy, “Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction,” IEEE J. Sel. Top. Quant. 20, 74 (2014).

Marjono, A.

Mata Pavia, J.

F. Scholkmann, S. Kleiser, A. J. Metz, R. Zimmermann, J. Mata Pavia, U. Wolf, and M. Wolf, “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” Neuroimage 85(Pt 1), 6–27 (2014).
[Crossref] [PubMed]

Metz, A. J.

F. Scholkmann, S. Kleiser, A. J. Metz, R. Zimmermann, J. Mata Pavia, U. Wolf, and M. Wolf, “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” Neuroimage 85(Pt 1), 6–27 (2014).
[Crossref] [PubMed]

Müller, K. R.

C. Habermehl, J. Steinbrink, K. R. Müller, and S. Haufe, “Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography,” J. Biomed. Opt. 19(9), 96006 (2014).
[Crossref] [PubMed]

Nambu, I.

T. Sato, I. Nambu, K. Takeda, T. Aihara, O. Yamashita, Y. Isogaya, Y. Inoue, Y. Otaka, Y. Wada, M. Kawato, M. A. Sato, and R. Osu, “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” Neuroimage 141, 120–132 (2016).
[Crossref] [PubMed]

Nehorai, A.

Obata, A. N.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Okada, E.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Osu, R.

T. Sato, I. Nambu, K. Takeda, T. Aihara, O. Yamashita, Y. Isogaya, Y. Inoue, Y. Otaka, Y. Wada, M. Kawato, M. A. Sato, and R. Osu, “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” Neuroimage 141, 120–132 (2016).
[Crossref] [PubMed]

Otaka, Y.

T. Sato, I. Nambu, K. Takeda, T. Aihara, O. Yamashita, Y. Isogaya, Y. Inoue, Y. Otaka, Y. Wada, M. Kawato, M. A. Sato, and R. Osu, “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” Neuroimage 141, 120–132 (2016).
[Crossref] [PubMed]

Paganoni, A.

Prakash, J.

J. Prakash, C. B. Shaw, R. Manjappa, R. Kanhirodan, and P. K. Yalavarthy, “Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction,” IEEE J. Sel. Top. Quant. 20, 74 (2014).

Quaresima, V.

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
[Crossref] [PubMed]

Re, R.

Robichaux-Viehoever, A.

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

Rossignol, S.

J. Cohen-Adad, S. Chapuisat, J. Doyon, S. Rossignol, J. M. Lina, H. Benali, and F. Lesage, “Activation detection in diffuse optical imaging by means of the general linear model,” Med. Image Anal. 11(6), 616–629 (2007).
[Crossref] [PubMed]

Sato, H.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

Sato, M. A.

Sato, T.

T. Sato, I. Nambu, K. Takeda, T. Aihara, O. Yamashita, Y. Isogaya, Y. Inoue, Y. Otaka, Y. Wada, M. Kawato, M. A. Sato, and R. Osu, “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” Neuroimage 141, 120–132 (2016).
[Crossref] [PubMed]

Scarpa, F.

F. Scarpa, S. Brigadoi, S. Cutini, P. Scatturin, M. Zorzi, R. Dell’acqua, and G. Sparacino, “A reference-channel based methodology to improve estimation of event-related hemodynamic response from fNIRS measurements,” Neuroimage 72, 106–119 (2013).
[Crossref] [PubMed]

Scatturin, P.

F. Scarpa, S. Brigadoi, S. Cutini, P. Scatturin, M. Zorzi, R. Dell’acqua, and G. Sparacino, “A reference-channel based methodology to improve estimation of event-related hemodynamic response from fNIRS measurements,” Neuroimage 72, 106–119 (2013).
[Crossref] [PubMed]

Scholkmann, F.

F. Scholkmann, S. Kleiser, A. J. Metz, R. Zimmermann, J. Mata Pavia, U. Wolf, and M. Wolf, “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” Neuroimage 85(Pt 1), 6–27 (2014).
[Crossref] [PubMed]

Shaw, C. B.

J. Prakash, C. B. Shaw, R. Manjappa, R. Kanhirodan, and P. K. Yalavarthy, “Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction,” IEEE J. Sel. Top. Quant. 20, 74 (2014).

Shimokawa, T.

Snyder, A. Z.

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

Sparacino, G.

F. Scarpa, S. Brigadoi, S. Cutini, P. Scatturin, M. Zorzi, R. Dell’acqua, and G. Sparacino, “A reference-channel based methodology to improve estimation of event-related hemodynamic response from fNIRS measurements,” Neuroimage 72, 106–119 (2013).
[Crossref] [PubMed]

Spinelli, L.

Steinbrink, J.

C. Habermehl, J. Steinbrink, K. R. Müller, and S. Haufe, “Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography,” J. Biomed. Opt. 19(9), 96006 (2014).
[Crossref] [PubMed]

Taga, G.

D. A. Boas, C. E. Elwell, M. Ferrari, and G. Taga, “Twenty years of functional near-infrared spectroscopy: introduction for the special issue,” Neuroimage 85(Pt 1), 1–5 (2014).
[Crossref] [PubMed]

G. Taga, K. Asakawa, A. Maki, Y. Konishi, and H. Koizumi, “Brain imaging in awake infants by near-infrared optical topography,” Proc. Natl. Acad. Sci. U.S.A. 100(19), 10722–10727 (2003).
[Crossref] [PubMed]

Tak, S.

S. Tak and J. C. Ye, “Statistical analysis of fNIRS data: a comprehensive review,” Neuroimage 85(Pt 1), 72–91 (2014).
[Crossref] [PubMed]

Takeda, K.

T. Sato, I. Nambu, K. Takeda, T. Aihara, O. Yamashita, Y. Isogaya, Y. Inoue, Y. Otaka, Y. Wada, M. Kawato, M. A. Sato, and R. Osu, “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” Neuroimage 141, 120–132 (2016).
[Crossref] [PubMed]

Tanikawa, Y.

T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, and M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” Neuroimage 85(Pt 1), 150–165 (2014).
[Crossref] [PubMed]

F. Gao, H. Zhao, L. Zhang, Y. Tanikawa, A. Marjono, and Y. Yamada, “A self-normalized, full time-resolved method for fluorescence diffuse optical tomography,” Opt. Express 16(17), 13104–13121 (2008).
[Crossref] [PubMed]

F. Gao, H. Zhao, Y. Tanikawa, and Y. Yamada, “Optical tomographic mapping of cerebral haemodynamics by means of time-domain detection: methodology and phantom validation,” Phys. Med. Biol. 49(6), 1055–1078 (2004).
[Crossref] [PubMed]

Tian, F.

C. Chen, F. Tian, H. Liu, and J. Huang, “Diffuse optical tomography enhanced by clustered sparsity for functional brain imaging,” IEEE Trans. Med. Imaging 33(12), 2323–2331 (2014).
[Crossref] [PubMed]

Tian, H.

Torricelli, A.

Wada, Y.

T. Sato, I. Nambu, K. Takeda, T. Aihara, O. Yamashita, Y. Isogaya, Y. Inoue, Y. Otaka, Y. Wada, M. Kawato, M. A. Sato, and R. Osu, “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” Neuroimage 141, 120–132 (2016).
[Crossref] [PubMed]

Wan, W.

B. Wang, W. Wan, Y. Wang, W. Ma, L. Zhang, J. Li, Z. Zhou, H. Zhao, and F. Gao, “An Lp (0 ≤ p ≤ 1)-norm regularized image reconstruction scheme for breast DOT with non-negative-constraint,” Biomed. Eng. Online 16(1), 32 (2017).
[Crossref] [PubMed]

Wang, B.

X. Ding, B. Wang, D. Liu, Y. Zhang, J. He, H. Zhao, and F. Gao, “A three-wavelength multi-channel brain functional imager based on digital lock-in photon-counting technique,” SPIE Proc. 10480, 104800S (2018).

B. Wang, W. Wan, Y. Wang, W. Ma, L. Zhang, J. Li, Z. Zhou, H. Zhao, and F. Gao, “An Lp (0 ≤ p ≤ 1)-norm regularized image reconstruction scheme for breast DOT with non-negative-constraint,” Biomed. Eng. Online 16(1), 32 (2017).
[Crossref] [PubMed]

W. Chen, X. Wang, B. Wang, Y. Wang, Y. Zhang, H. Zhao, and F. Gao, “Lock-in-photon-counting-based highly-sensitive and large-dynamic imaging system for continuous-wave diffuse optical tomography,” Biomed. Opt. Express 7(2), 499–511 (2016).
[Crossref] [PubMed]

Wang, X.

Wang, Y.

B. Wang, W. Wan, Y. Wang, W. Ma, L. Zhang, J. Li, Z. Zhou, H. Zhao, and F. Gao, “An Lp (0 ≤ p ≤ 1)-norm regularized image reconstruction scheme for breast DOT with non-negative-constraint,” Biomed. Eng. Online 16(1), 32 (2017).
[Crossref] [PubMed]

W. Chen, X. Wang, B. Wang, Y. Wang, Y. Zhang, H. Zhao, and F. Gao, “Lock-in-photon-counting-based highly-sensitive and large-dynamic imaging system for continuous-wave diffuse optical tomography,” Biomed. Opt. Express 7(2), 499–511 (2016).
[Crossref] [PubMed]

White, B. R.

N. M. Gregg, B. R. White, B. W. Zeff, A. J. Berger, and J. P. Culver, “Brain specificity of diffuse optical imaging: improvements from superficial signal regression and tomography,” Front. Neuroenergetics 2(14), 14 (2010).
[Crossref] [PubMed]

Wolf, M.

F. Scholkmann, S. Kleiser, A. J. Metz, R. Zimmermann, J. Mata Pavia, U. Wolf, and M. Wolf, “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” Neuroimage 85(Pt 1), 6–27 (2014).
[Crossref] [PubMed]

Wolf, U.

F. Scholkmann, S. Kleiser, A. J. Metz, R. Zimmermann, J. Mata Pavia, U. Wolf, and M. Wolf, “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” Neuroimage 85(Pt 1), 6–27 (2014).
[Crossref] [PubMed]

Wu, X.

Yalavarthy, P. K.

J. Prakash, C. B. Shaw, R. Manjappa, R. Kanhirodan, and P. K. Yalavarthy, “Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction,” IEEE J. Sel. Top. Quant. 20, 74 (2014).

Yamada, Y.

F. Gao, H. Zhao, L. Zhang, Y. Tanikawa, A. Marjono, and Y. Yamada, “A self-normalized, full time-resolved method for fluorescence diffuse optical tomography,” Opt. Express 16(17), 13104–13121 (2008).
[Crossref] [PubMed]

F. Gao, H. Zhao, Y. Tanikawa, and Y. Yamada, “Optical tomographic mapping of cerebral haemodynamics by means of time-domain detection: methodology and phantom validation,” Phys. Med. Biol. 49(6), 1055–1078 (2004).
[Crossref] [PubMed]

Yamashita, O.

Ye, J. C.

S. Tak and J. C. Ye, “Statistical analysis of fNIRS data: a comprehensive review,” Neuroimage 85(Pt 1), 72–91 (2014).
[Crossref] [PubMed]

Zeff, B. W.

N. M. Gregg, B. R. White, B. W. Zeff, A. J. Berger, and J. P. Culver, “Brain specificity of diffuse optical imaging: improvements from superficial signal regression and tomography,” Front. Neuroenergetics 2(14), 14 (2010).
[Crossref] [PubMed]

Zhan, Y.

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[Crossref] [PubMed]

Zhang, L.

B. Wang, W. Wan, Y. Wang, W. Ma, L. Zhang, J. Li, Z. Zhou, H. Zhao, and F. Gao, “An Lp (0 ≤ p ≤ 1)-norm regularized image reconstruction scheme for breast DOT with non-negative-constraint,” Biomed. Eng. Online 16(1), 32 (2017).
[Crossref] [PubMed]

F. Gao, H. Zhao, L. Zhang, Y. Tanikawa, A. Marjono, and Y. Yamada, “A self-normalized, full time-resolved method for fluorescence diffuse optical tomography,” Opt. Express 16(17), 13104–13121 (2008).
[Crossref] [PubMed]

Zhang, N.

Zhang, Y.

Y. Zhang, L. Huang, N. Zhang, H. Tian, and J. Zhu, “Experimental study on the sensitive depth of backwards detected light in turbid media,” Opt. Express 26(11), 14700–14709 (2018).
[Crossref] [PubMed]

X. Ding, B. Wang, D. Liu, Y. Zhang, J. He, H. Zhao, and F. Gao, “A three-wavelength multi-channel brain functional imager based on digital lock-in photon-counting technique,” SPIE Proc. 10480, 104800S (2018).

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Figures (9)

Fig. 1
Fig. 1 The process to illuminate the sources field by field in the multi-field illumination mode. Every channel is denoted by a red arrow whose tail and head denote the source and detector, respectively. The bright and dark red circles are the lit and unlit sources in the field, respectively. The bright and dark blue squares are the detectors working and unworking in the field, respectively.
Fig. 2
Fig. 2 (a) The 3-Dimensional and (b) the longitudinal section of the cuboidal medium. The small red and green cylinders on the top surface of the medium denote the the sources and detectors, respectively. The top layer denotes the scalp. The two big blue cylinders denote the targets inside the medium.
Fig. 3
Fig. 3 The indirect Kalman (Left) and direct Kalman (Middle)-calculated images and their X-profiles (Right): (a) SNR = 10 dB and (b) SNR = 20 dB. To investigate the SR, the activation levels of the both targets were set to 0.5. The black dotted circles in the left and middle columns denoted the true locations of the targets
Fig. 4
Fig. 4 The indirect Kalman (Left) and direct Kalman (Middle)-calculated images and their X-profiles (Right): (a) SNR = 10 dB and (b) SNR = 20 dB. To investigate the GSR of the two approaches, the activation levels of the left and right targets were set to 0.5 and 1, respectively. The black dotted circles in the left and middle columns denoted the true locations of the targets.
Fig. 5
Fig. 5 The schematic of the setup of the dynamic phantoms. The constant flow pumps 1 and 2 were used respectively to pump the solution into and out from the target cylinder located in the tank filled with the background solution. The cup A and B were filled with the target and background solution, respectively. The cup C was used to collect the waste solution pumped out form the target cylinder. Switch A and Switch B were used to select a cup from which the solution was pumped into the target cylinder.
Fig. 6
Fig. 6 The schematic of the NIRS-DOT system based on the lock-in photon counting technique used in the dynamic phantom experiments.
Fig. 7
Fig. 7 The indirect Kalman (Left) and direct Kalman (Middle)-calculated images and their X-profiles (Right): (a) accumulation period = 500 ms and (b) accumulation period = 1000 ms. The activation level of the target was 0.5. The black dotted circles in the left and middle columns denoted the true locations of the targets.
Fig. 8
Fig. 8 The indirect Kalman (Left) and direct Kalman (Middle)-calculated images and their X-profiles (Right): (a) SNR = 10 dB and (b) SNR = 20 dB. The amplitude of the local activated region C was set to 0.1. The black dotted circles in the left and middle columns denoted the true locations of the targets.
Fig. 9
Fig. 9 The indirect Kalman (Left) and direct Kalman (Middle)-calculated images and their X-profiles (Right): (a) SNR = 10 dB and (b) SNR = 20 dB. The amplitude of the local activated region C was set to 1. The black dotted circles in the left and middle columns denoted the true locations of the targets.

Tables (6)

Tables Icon

Table 1 The metrics for the results recovered by the indirect and direct Kalman approaches in the simulative experiments with two targets having the same activation levels of 0.5.

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Table 2 The metrics for the results reconstructed by the indirect and direct Kalman approaches in the simulative experiments with two activated regions having different activation levels.

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Table 3 The process of the operations on the two switches, constant flow pumps, changes in μa and the simulated states of the brain.

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Table 4 The metrics for the results reconstructed by the indirect and direct Kalman approaches in the dynamic phantom experiments with the activation level of 0.5 for accumulation period of 500 ms and 1000 ms.

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Table 5 The metrics for the results reconstructed by the indirect and direct Kalman approaches in the simulative experiments where the amplitude of the local region C was set to 0.1.

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Table 6 The metrics for the results reconstructed by the indirect and direct Kalman approaches in the simulative experiments where the amplitude of the local region C was set to 1.

Equations (12)

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cHRF(k)= α 1 [ Γ n (k, τ 1 , ρ 1 ) α 2 Γ n (k, τ 2 , ρ 2 )]
y(f)=Jx(f)
( X n ) T =H β n
{ [ X n (f)] T =H(f) β n (f)+w(f) β n (f)= β n (f1)+v(f)
Y=JX
Y=JB H T
vec(Y)=(HJ)vec(B)
{ B(k)=IB(k1)+W(k) S(k)=[ H k J(s)]B(k)+V(k)
{ B ^ (k)=IB(k1) P ^ (k)=IP(k1) I T +Q K g (k)= P ^ (k) H k [ ( H k ) T P(k) ( H k ) T +R] 1 B(k)= B ^ (k)+ K g (k)[S(k) ( H k ) T B ^ (k)] P(k)=[Ι K g (k) H k ] P ^ (k)
rRMSE= || β tr β rec | | 2 2 /|| β tr | | 2 2
CNR= |Mean( β ROI )Mean( β ROB )| wVar( β ROI )+(1w)Var( β ROI )
[ y(f) y 1st (f) ]=[ J J scalp1 0 J scalp2 ][ x(f) x scalp (f) ]

Metrics