H. Zhang, D. Zeng, H. Zhang, J. Wang, Z. R. Liang, and J. H. Ma, “Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review,” Med. Phys. 44(3), 1168–1185 (2017).

[Crossref]

W. Q. Lu, J. M. Duan, Z. W. Qiu, Z. K. Pan, Q. W. Liu, and L. Bai, “Implementation of high-order variational models made easy for image processing,” Math. Meth. Appl. Sci. 39(14), 4208–4233 (2016).

[Crossref]

M. Bhatt, K. R. Ayyalasomayajula, and P. K. Yalavarthy, “Generalized Beer–Lambert model for near-infrared light propagation in thick biological tissues,” J. Biomed. Opt. 21(7), 076012 (2016).

[Crossref]

J. M. Duan, Z. K. Pan, B. C. Zhang, W. Q. Liu, and X. C. Tai, “Fast algorithm for color texture image inpainting using the non-local CTV model,” J Glob Optim 62(4), 853–876 (2015).

[Crossref]

X. Bresson, X. C. Tai, T. F. Chan, and A. Szlam, “Multi-class transductive learning based on l1 relaxations of Cheeger cut and Mumford-Shah-Potts model,” J Math Imaging Vis 49(1), 191–201 (2014).

[Crossref]

W. B. Baker, A. B. Parthasarathy, D. R. Busch, R. C. Mesquita, H. Joel, and A. G. Yodh, “Modified Beer-Lambert law for blood flow,” Biomed. Opt. Express 5(11), 4053–4075 (2014).

[Crossref]

J. M. Duan, Z. K. Pan, W. Q. Liu, and X. C. Tai, “Color texture image inpainting using the non local CTV model,” JSIP 04(03), 43–51 (2013).

[Crossref]

E. Merkurjev, T. Kostic, and A. L. Bertozzi, “An MBO scheme on graphs for classification and image processing,” SIAM J. Imaging Sci. 6(4), 1903–1930 (2013).

[Crossref]

A. L. Bertozzi and A. Flenner, “Diffuse interface models on graphs for classification of high dimensional data,” Multiscale Model. Simul. 10(3), 1090–1118 (2012).

[Crossref]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. X. Chen, Y. X. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fmri cortical mapping,” NeuroImage 61(4), 1120–1128 (2012).

[Crossref]

M. Gunzburger and R. B. Lehoucq, “A nonlocal vector calculus with application to nonlocal boundary value problems,” Multiscale Model. Simul. 8(5), 1581–1598 (2010).

[Crossref]

A. D. Klose, “The forward and inverse problem in tissue optics based on the radiative transfer equation: a brief review,” J. Quant. Spectrosc. Radiat. Transfer 111(11), 1852–1853 (2010).

[Crossref]

P. González-Rodríguez and A. D. Kim, “Comparison of light scattering models for diffuse optical tomography,” Opt. Express 17(11), 8756–8774 (2009).

[Crossref]

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[Crossref]

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Meth. Engng. 25(6), 711–732 (2009).

[Crossref]

G. Gilboa and S. Osher, “Nonlocal operators with applications to image processing,” Multiscale Model. Simul. 7(3), 1005–1028 (2009).

[Crossref]

H. Dehghani, B. R. White, B. W. Zeff, A. Tizzard, and J. P. Culver, “Depth sensitivity and image reconstruction analysis of dense imaging arrays for mapping brain function with diffuse optical tomography,” Appl. Opt. 48(10), D137–D143 (2009).

[Crossref]

L. Kocsis, P. Herman, and A. Eke, “The modified Beer–Lambert law revisited,” Phys. Med. Biol. 51(5), N91–N98 (2006).

[Crossref]

D. A. Boas, A. M. Dale, and M. A. Franceschini, “Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy,” NeuroImage 23, S275–S288 (2004).

[Crossref]

D. A. Boas, T. Gaudette, G. Strangman, X. F. Cheng, J. J. A.. Marota, and J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” NeuroImage 13(1), 76–90 (2001).

[Crossref]

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

[Crossref]

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The finite element method for the propagation of light in scattering media: boundary and source conditions,” Med. Phys. 22(11), 1779–1792 (1995).

[Crossref]

T. J. Farrell, M. S. Patterson, and B. Wilson, “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo,” Med. Phys. 19(4), 879–888 (1992).

[Crossref]

S. R. Arridge, M. Cope, and D. T. Delpy, “The theoretical basis for the determination of optical pathlengths in tissue: temporal and frequency analysis,” Phys. Med. Biol. 37(7), 1531–1560 (1992).

[Crossref]

V. Allen and A. L. McKenzie, “The modified diffusion dipole model,” Phys. Med. Biol. 36(12), 1621–1638 (1991).

[Crossref]

B. C. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux distributions of light in tissue,” Med. Phys. 10(6), 824–830 (1983).

[Crossref]

G. Eason, A. R. Veitch, R. M. Nisbet, and F. W. Turnbull, “The theory of the back-scattering of light by blood,” J. Phys. D: Appl. Phys. 11(10), 1463–1479 (1978).

[Crossref]

W. H. Reed, “New difference schemes for the neutron transport equation,” Nucl. Sci. Eng. 46(2), 309–314 (1971).

[Crossref]

B. C. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux distributions of light in tissue,” Med. Phys. 10(6), 824–830 (1983).

[Crossref]

V. Allen and A. L. McKenzie, “The modified diffusion dipole model,” Phys. Med. Biol. 36(12), 1621–1638 (1991).

[Crossref]

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

[Crossref]

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The finite element method for the propagation of light in scattering media: boundary and source conditions,” Med. Phys. 22(11), 1779–1792 (1995).

[Crossref]

S. R. Arridge, M. Cope, and D. T. Delpy, “The theoretical basis for the determination of optical pathlengths in tissue: temporal and frequency analysis,” Phys. Med. Biol. 37(7), 1531–1560 (1992).

[Crossref]

M. Bhatt, K. R. Ayyalasomayajula, and P. K. Yalavarthy, “Generalized Beer–Lambert model for near-infrared light propagation in thick biological tissues,” J. Biomed. Opt. 21(7), 076012 (2016).

[Crossref]

W. Q. Lu, J. M. Duan, Z. W. Qiu, Z. K. Pan, Q. W. Liu, and L. Bai, “Implementation of high-order variational models made easy for image processing,” Math. Meth. Appl. Sci. 39(14), 4208–4233 (2016).

[Crossref]

E. Merkurjev, T. Kostic, and A. L. Bertozzi, “An MBO scheme on graphs for classification and image processing,” SIAM J. Imaging Sci. 6(4), 1903–1930 (2013).

[Crossref]

A. L. Bertozzi and A. Flenner, “Diffuse interface models on graphs for classification of high dimensional data,” Multiscale Model. Simul. 10(3), 1090–1118 (2012).

[Crossref]

M. Bhatt, K. R. Ayyalasomayajula, and P. K. Yalavarthy, “Generalized Beer–Lambert model for near-infrared light propagation in thick biological tissues,” J. Biomed. Opt. 21(7), 076012 (2016).

[Crossref]

D. A. Boas, A. M. Dale, and M. A. Franceschini, “Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy,” NeuroImage 23, S275–S288 (2004).

[Crossref]

D. A. Boas, T. Gaudette, G. Strangman, X. F. Cheng, J. J. A.. Marota, and J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” NeuroImage 13(1), 76–90 (2001).

[Crossref]

D. A. Boas, “Diffuse photon probes of structural and dynamical properties of turbid media: theory and biomedical applications,” Doctoral dissertation, Graduate School of Arts and Sciences, University of Pennsylvania (1996).

X. Bresson, X. C. Tai, T. F. Chan, and A. Szlam, “Multi-class transductive learning based on l1 relaxations of Cheeger cut and Mumford-Shah-Potts model,” J Math Imaging Vis 49(1), 191–201 (2014).

[Crossref]

A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)2, 60–65 (2005).

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Meth. Engng. 25(6), 711–732 (2009).

[Crossref]

X. Bresson, X. C. Tai, T. F. Chan, and A. Szlam, “Multi-class transductive learning based on l1 relaxations of Cheeger cut and Mumford-Shah-Potts model,” J Math Imaging Vis 49(1), 191–201 (2014).

[Crossref]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. X. Chen, Y. X. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fmri cortical mapping,” NeuroImage 61(4), 1120–1128 (2012).

[Crossref]

D. A. Boas, T. Gaudette, G. Strangman, X. F. Cheng, J. J. A.. Marota, and J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” NeuroImage 13(1), 76–90 (2001).

[Crossref]

A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)2, 60–65 (2005).

S. R. Arridge, M. Cope, and D. T. Delpy, “The theoretical basis for the determination of optical pathlengths in tissue: temporal and frequency analysis,” Phys. Med. Biol. 37(7), 1531–1560 (1992).

[Crossref]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. X. Chen, Y. X. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fmri cortical mapping,” NeuroImage 61(4), 1120–1128 (2012).

[Crossref]

H. Dehghani, B. R. White, B. W. Zeff, A. Tizzard, and J. P. Culver, “Depth sensitivity and image reconstruction analysis of dense imaging arrays for mapping brain function with diffuse optical tomography,” Appl. Opt. 48(10), D137–D143 (2009).

[Crossref]

D. A. Boas, A. M. Dale, and M. A. Franceschini, “Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy,” NeuroImage 23, S275–S288 (2004).

[Crossref]

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Meth. Engng. 25(6), 711–732 (2009).

[Crossref]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. X. Chen, Y. X. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fmri cortical mapping,” NeuroImage 61(4), 1120–1128 (2012).

[Crossref]

H. Dehghani, B. R. White, B. W. Zeff, A. Tizzard, and J. P. Culver, “Depth sensitivity and image reconstruction analysis of dense imaging arrays for mapping brain function with diffuse optical tomography,” Appl. Opt. 48(10), D137–D143 (2009).

[Crossref]

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Meth. Engng. 25(6), 711–732 (2009).

[Crossref]

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The finite element method for the propagation of light in scattering media: boundary and source conditions,” Med. Phys. 22(11), 1779–1792 (1995).

[Crossref]

S. R. Arridge, M. Cope, and D. T. Delpy, “The theoretical basis for the determination of optical pathlengths in tissue: temporal and frequency analysis,” Phys. Med. Biol. 37(7), 1531–1560 (1992).

[Crossref]

W. Q. Lu, J. M. Duan, D. Orive-Miguel, L. Herve, and I. B. Styles, “Graph-and finite element-based total variation models for the inverse problem in diffuse optical tomography,” Biomed. Opt. Express 10(6), 2684–2707 (2019).

[Crossref]

W. Q. Lu, J. M. Duan, Z. W. Qiu, Z. K. Pan, Q. W. Liu, and L. Bai, “Implementation of high-order variational models made easy for image processing,” Math. Meth. Appl. Sci. 39(14), 4208–4233 (2016).

[Crossref]

J. M. Duan, Z. K. Pan, B. C. Zhang, W. Q. Liu, and X. C. Tai, “Fast algorithm for color texture image inpainting using the non-local CTV model,” J Glob Optim 62(4), 853–876 (2015).

[Crossref]

J. M. Duan, Z. K. Pan, W. Q. Liu, and X. C. Tai, “Color texture image inpainting using the non local CTV model,” JSIP 04(03), 43–51 (2013).

[Crossref]

J. J. Duderstadt and W. R. Martin, Transport Theory (John Wiley & Sons Chichester, 1979).

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Meth. Engng. 25(6), 711–732 (2009).

[Crossref]

G. Eason, A. R. Veitch, R. M. Nisbet, and F. W. Turnbull, “The theory of the back-scattering of light by blood,” J. Phys. D: Appl. Phys. 11(10), 1463–1479 (1978).

[Crossref]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. X. Chen, Y. X. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fmri cortical mapping,” NeuroImage 61(4), 1120–1128 (2012).

[Crossref]

L. Kocsis, P. Herman, and A. Eke, “The modified Beer–Lambert law revisited,” Phys. Med. Biol. 51(5), N91–N98 (2006).

[Crossref]

T. J. Farrell, M. S. Patterson, and B. Wilson, “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo,” Med. Phys. 19(4), 879–888 (1992).

[Crossref]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. X. Chen, Y. X. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fmri cortical mapping,” NeuroImage 61(4), 1120–1128 (2012).

[Crossref]

A. L. Bertozzi and A. Flenner, “Diffuse interface models on graphs for classification of high dimensional data,” Multiscale Model. Simul. 10(3), 1090–1118 (2012).

[Crossref]

D. A. Boas, A. M. Dale, and M. A. Franceschini, “Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy,” NeuroImage 23, S275–S288 (2004).

[Crossref]

D. A. Boas, T. Gaudette, G. Strangman, X. F. Cheng, J. J. A.. Marota, and J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” NeuroImage 13(1), 76–90 (2001).

[Crossref]

G. Gilboa and S. Osher, “Nonlocal operators with applications to image processing,” Multiscale Model. Simul. 7(3), 1005–1028 (2009).

[Crossref]

M. Gunzburger and R. B. Lehoucq, “A nonlocal vector calculus with application to nonlocal boundary value problems,” Multiscale Model. Simul. 8(5), 1581–1598 (2010).

[Crossref]

L. Kocsis, P. Herman, and A. Eke, “The modified Beer–Lambert law revisited,” Phys. Med. Biol. 51(5), N91–N98 (2006).

[Crossref]

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The finite element method for the propagation of light in scattering media: boundary and source conditions,” Med. Phys. 22(11), 1779–1792 (1995).

[Crossref]

C. Ichai, H. Quintard, and J. C. Orban, Metabolic Disorders and Critically Ill Patients: From Pathophysiology to Treatment (Springer, 2017).

A. D. Klose, “The forward and inverse problem in tissue optics based on the radiative transfer equation: a brief review,” J. Quant. Spectrosc. Radiat. Transfer 111(11), 1852–1853 (2010).

[Crossref]

L. Kocsis, P. Herman, and A. Eke, “The modified Beer–Lambert law revisited,” Phys. Med. Biol. 51(5), N91–N98 (2006).

[Crossref]

E. Merkurjev, T. Kostic, and A. L. Bertozzi, “An MBO scheme on graphs for classification and image processing,” SIAM J. Imaging Sci. 6(4), 1903–1930 (2013).

[Crossref]

M. Gunzburger and R. B. Lehoucq, “A nonlocal vector calculus with application to nonlocal boundary value problems,” Multiscale Model. Simul. 8(5), 1581–1598 (2010).

[Crossref]

H. Zhang, D. Zeng, H. Zhang, J. Wang, Z. R. Liang, and J. H. Ma, “Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review,” Med. Phys. 44(3), 1168–1185 (2017).

[Crossref]

W. Q. Lu, J. M. Duan, Z. W. Qiu, Z. K. Pan, Q. W. Liu, and L. Bai, “Implementation of high-order variational models made easy for image processing,” Math. Meth. Appl. Sci. 39(14), 4208–4233 (2016).

[Crossref]

J. M. Duan, Z. K. Pan, B. C. Zhang, W. Q. Liu, and X. C. Tai, “Fast algorithm for color texture image inpainting using the non-local CTV model,” J Glob Optim 62(4), 853–876 (2015).

[Crossref]

J. M. Duan, Z. K. Pan, W. Q. Liu, and X. C. Tai, “Color texture image inpainting using the non local CTV model,” JSIP 04(03), 43–51 (2013).

[Crossref]

W. Q. Lu, J. M. Duan, D. Orive-Miguel, L. Herve, and I. B. Styles, “Graph-and finite element-based total variation models for the inverse problem in diffuse optical tomography,” Biomed. Opt. Express 10(6), 2684–2707 (2019).

[Crossref]

W. Q. Lu, D. Lighter, and I. B. Styles, “L 1-norm based nonlinear reconstruction improves quantitative accuracy of spectral diffuse optical tomography,” Biomed. Opt. Express 9(4), 1423–1444 (2018).

[Crossref]

W. Q. Lu, J. M. Duan, Z. W. Qiu, Z. K. Pan, Q. W. Liu, and L. Bai, “Implementation of high-order variational models made easy for image processing,” Math. Meth. Appl. Sci. 39(14), 4208–4233 (2016).

[Crossref]

D. R. Lynch, Numerical Partial Differential Equations for Environmental Scientists and Engineers: A First Practical Course (Springer Science & Business Media, 2004).

H. Zhang, D. Zeng, H. Zhang, J. Wang, Z. R. Liang, and J. H. Ma, “Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: A review,” Med. Phys. 44(3), 1168–1185 (2017).

[Crossref]

D. A. Boas, T. Gaudette, G. Strangman, X. F. Cheng, J. J. A.. Marota, and J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” NeuroImage 13(1), 76–90 (2001).

[Crossref]

D. A. Boas, T. Gaudette, G. Strangman, X. F. Cheng, J. J. A.. Marota, and J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” NeuroImage 13(1), 76–90 (2001).

[Crossref]

J. J. Duderstadt and W. R. Martin, Transport Theory (John Wiley & Sons Chichester, 1979).

V. Allen and A. L. McKenzie, “The modified diffusion dipole model,” Phys. Med. Biol. 36(12), 1621–1638 (1991).

[Crossref]

E. Merkurjev, T. Kostic, and A. L. Bertozzi, “An MBO scheme on graphs for classification and image processing,” SIAM J. Imaging Sci. 6(4), 1903–1930 (2013).

[Crossref]

A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)2, 60–65 (2005).

G. Eason, A. R. Veitch, R. M. Nisbet, and F. W. Turnbull, “The theory of the back-scattering of light by blood,” J. Phys. D: Appl. Phys. 11(10), 1463–1479 (1978).

[Crossref]

C. Ichai, H. Quintard, and J. C. Orban, Metabolic Disorders and Critically Ill Patients: From Pathophysiology to Treatment (Springer, 2017).

G. Gilboa and S. Osher, “Nonlocal operators with applications to image processing,” Multiscale Model. Simul. 7(3), 1005–1028 (2009).

[Crossref]

W. Q. Lu, J. M. Duan, Z. W. Qiu, Z. K. Pan, Q. W. Liu, and L. Bai, “Implementation of high-order variational models made easy for image processing,” Math. Meth. Appl. Sci. 39(14), 4208–4233 (2016).

[Crossref]

J. M. Duan, Z. K. Pan, B. C. Zhang, W. Q. Liu, and X. C. Tai, “Fast algorithm for color texture image inpainting using the non-local CTV model,” J Glob Optim 62(4), 853–876 (2015).

[Crossref]

J. M. Duan, Z. K. Pan, W. Q. Liu, and X. C. Tai, “Color texture image inpainting using the non local CTV model,” JSIP 04(03), 43–51 (2013).

[Crossref]

A. Kienle, M. S. Patterson, N. Dögnitz, R. Bays, G. Wagnières, and H. van Den Bergh, “Noninvasive determination of the optical properties of two-layered turbid media,” Appl. Opt. 37(4), 779–791 (1998).

[Crossref]

T. J. Farrell, M. S. Patterson, and B. Wilson, “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo,” Med. Phys. 19(4), 879–888 (1992).

[Crossref]

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Meth. Engng. 25(6), 711–732 (2009).

[Crossref]

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Meth. Engng. 25(6), 711–732 (2009).

[Crossref]

W. Q. Lu, J. M. Duan, Z. W. Qiu, Z. K. Pan, Q. W. Liu, and L. Bai, “Implementation of high-order variational models made easy for image processing,” Math. Meth. Appl. Sci. 39(14), 4208–4233 (2016).

[Crossref]

C. Ichai, H. Quintard, and J. C. Orban, Metabolic Disorders and Critically Ill Patients: From Pathophysiology to Treatment (Springer, 2017).

W. H. Reed, “New difference schemes for the neutron transport equation,” Nucl. Sci. Eng. 46(2), 309–314 (1971).

[Crossref]

M. Schweiger, S. R. Arridge, M. Hiraoka, and D. T. Delpy, “The finite element method for the propagation of light in scattering media: boundary and source conditions,” Med. Phys. 22(11), 1779–1792 (1995).

[Crossref]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. X. Chen, Y. X. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fmri cortical mapping,” NeuroImage 61(4), 1120–1128 (2012).

[Crossref]

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Meth. Engng. 25(6), 711–732 (2009).

[Crossref]

D. A. Boas, T. Gaudette, G. Strangman, X. F. Cheng, J. J. A.. Marota, and J. B. Mandeville, “The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics,” NeuroImage 13(1), 76–90 (2001).

[Crossref]

W. Q. Lu, J. M. Duan, D. Orive-Miguel, L. Herve, and I. B. Styles, “Graph-and finite element-based total variation models for the inverse problem in diffuse optical tomography,” Biomed. Opt. Express 10(6), 2684–2707 (2019).

[Crossref]

W. Q. Lu, D. Lighter, and I. B. Styles, “L 1-norm based nonlinear reconstruction improves quantitative accuracy of spectral diffuse optical tomography,” Biomed. Opt. Express 9(4), 1423–1444 (2018).

[Crossref]

X. Bresson, X. C. Tai, T. F. Chan, and A. Szlam, “Multi-class transductive learning based on l1 relaxations of Cheeger cut and Mumford-Shah-Potts model,” J Math Imaging Vis 49(1), 191–201 (2014).

[Crossref]

J. M. Duan, Z. K. Pan, B. C. Zhang, W. Q. Liu, and X. C. Tai, “Fast algorithm for color texture image inpainting using the non-local CTV model,” J Glob Optim 62(4), 853–876 (2015).

[Crossref]

X. Bresson, X. C. Tai, T. F. Chan, and A. Szlam, “Multi-class transductive learning based on l1 relaxations of Cheeger cut and Mumford-Shah-Potts model,” J Math Imaging Vis 49(1), 191–201 (2014).

[Crossref]

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