Abstract

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique that elicits growing interest for research and clinical applications. In the last decade, efforts have been made to develop a mathematical framework in order to image the effective sources of hemoglobin variations in brain tissues. Different approaches can be used to impose additional information or constraints when reconstructing the cerebral images of an ill-posed problem. The goal of this study is to compare the performance and limitations of several source localization techniques in the context of fNIRS tomography using individual anatomical magnetic resonance imaging (MRI) to model light propagation. The forward problem is solved using a Monte Carlo simulation of light propagation in the tissues. The inverse problem has been linearized using the Rytov approximation. Then, Tikhonov regularization applied to least squares, truncated singular value decomposition, back-projection, L1-norm regularization, minimum norm estimates, low resolution electromagnetic tomography and Bayesian model averaging techniques are compared using a receiver operating characteristic analysis, blurring and localization error measures. Using realistic simulations (n = 450) and data acquired from a human participant, this study depicts how these source localization techniques behave in a human head fNIRS tomography. When compared to other methods, Bayesian model averaging is proposed as a promising method in DOT and shows great potential to improve specificity, accuracy, as well as to reduce blurring and localization error even in presence of noise and deep sources. Classical reconstruction methods, such as regularized least squares, offer better sensitivity but higher blurring; while more novel L1-based method provides sparse solutions with small blurring and high specificity but lower sensitivity. The application of these methods is also demonstrated experimentally using visual fNIRS experiment with adult participant.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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  1. 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]
  2. F. B. Haeussinger, S. Heinzel, T. Hahn, M. Schecklmann, A.-C. Ehlis, and A. J. Fallgatter, “Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging,” PLoS One 6(10), e26377 (2011).
    [Crossref] [PubMed]
  3. S. J. Madsen, Optical Methods and Instrumentation in Brain Imaging and Therapy (Springer Science & Business Media, 2012), Chap. 3.
  4. S. R. Arridge, “Optical tomography in medical imaging,” Inverse Probl. 15(2), R41–R93 (1999).
    [Crossref]
  5. D. Boas, J. Culver, J. Stott, and A. Dunn, “Three dimensional Monte Carlo code for photon migration through complex heterogeneous media including the adult human head,” Opt. Express 10(3), 159–170 (2002).
    [Crossref] [PubMed]
  6. 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).
    [Crossref] [PubMed]
  7. 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. Methods Eng. 25(6), 711–732 (2009).
    [Crossref] [PubMed]
  8. M. Schweiger and S. Arridge, “The Toast++ software suite for forward and inverse modeling in optical tomography,” J. Biomed. Opt. 19(4), 040801 (2014).
    [Crossref] [PubMed]
  9. A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
    [Crossref] [PubMed]
  10. 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), 096006 (2014).
    [Crossref] [PubMed]
  11. D. A. Boas and A. M. Dale, “Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function,” Appl. Opt. 44(10), 1957–1968 (2005).
    [Crossref] [PubMed]
  12. R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
    [Crossref] [PubMed]
  13. 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]
  14. 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]
  15. S. D. Konecky, G. Y. Panasyuk, K. Lee, V. Markel, A. G. Yodh, and J. C. Schotland, “Imaging complex structures with diffuse light,” Opt. Express 16(7), 5048–5060 (2008).
    [Crossref] [PubMed]
  16. S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25(12), 123010 (2009).
    [Crossref]
  17. M. Süzen, A. Giannoula, and T. Durduran, “Compressed sensing in diffuse optical tomography,” Opt. Express 18(23), 23676–23690 (2010).
    [Crossref] [PubMed]
  18. S. Okawa, Y. Hoshi, and Y. Yamada, “Improvement of image quality of time-domain diffuse optical tomography with l sparsity regularization,” Biomed. Opt. Express 2(12), 3334–3348 (2011).
    [Crossref] [PubMed]
  19. C. B. Shaw and P. K. Yalavarthy, “Effective contrast recovery in rapid dynamic near-infrared diffuse optical tomography using ℓ(1)-norm-based linear image reconstruction method,” J. Biomed. Opt. 17(8), 086009 (2012).
    [Crossref] [PubMed]
  20. V. C. Kavuri, Z.-J. Lin, F. Tian, and H. Liu, “Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography,” Biomed. Opt. Express 3(5), 943–957 (2012).
    [Crossref] [PubMed]
  21. 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. Quantum Electron. 20(2), 74–82 (2014).
    [Crossref]
  22. R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
    [Crossref] [PubMed]
  23. M. S. Hämäläinen and R. J. Ilmoniemi, “Interpreting magnetic fields of the brain: minimum norm estimates,” Med. Biol. Eng. Comput. 32(1), 35–42 (1994).
    [Crossref] [PubMed]
  24. R. D. Pascual-Marqui, C. M. Michel, and D. Lehmann, “Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain,” Int. J. Psychophysiol. 18(1), 49–65 (1994).
    [Crossref] [PubMed]
  25. N. J. Trujillo-Barreto, E. Aubert-Vázquez, and P. A. Valdés-Sosa, “Bayesian model averaging in EEG/MEG imaging,” Neuroimage 21(4), 1300–1319 (2004).
    [Crossref] [PubMed]
  26. O. Yamashita, T. Shimokawa, T. Kosaka, M. A. Sato, T. Amita, and Y. Inoue, “Hierarchical Bayesian model for diffuse optical tomography of human brains,” in The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems (2012), pp. 1451–1455.
  27. O. Yamashita, T. Shimokawa, R. Aisu, T. Amita, Y. Inoue, and M. A. Sato, “Multi-subject and multi-task experimental validation of the hierarchical Bayesian diffuse optical tomography algorithm,” Neuroimage 135, 287–299 (2016).
    [Crossref] [PubMed]
  28. A. Miyamoto, K. Watanabe, K. Ikeda, and M.-A. Sato, “Variational inference with ARD prior for NIRS diffuse optical tomography,” IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1109–1114 (2015).
    [Crossref] [PubMed]
  29. P. Marqui, R. D. “Source localization: continuing discussion of the inverse problem,” ISBET Newsletter 6, 9–30 (1995).
  30. C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
    [Crossref] [PubMed]
  31. S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Müller, and G. Nolte, “Combining sparsity and rotational invariance in EEG/MEG source reconstruction,” Neuroimage 42(2), 726–738 (2008).
    [Crossref] [PubMed]
  32. C. Grova, J. Daunizeau, J.-M. Lina, C. G. Bénar, H. Benali, and J. Gotman, “Evaluation of EEG localization methods using realistic simulations of interictal spikes,” Neuroimage 29(3), 734–753 (2006).
    [Crossref] [PubMed]
  33. D. Paz-Linares, M. Vega-Hernández, P. A. Rojas-López, P. A. Valdés-Hernández, E. Martínez-Montes, and P. A. Valdés-Sosa, “Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models,” Front. Neurosci. 11, 635 (2017).
    [Crossref] [PubMed]
  34. M. Fuchs, M. Wagner, T. Köhler, and H. A. Wischmann, “Linear and nonlinear current density reconstructions,” J. Clin. Neurophysiol. 16(3), 267–295 (1999).
    [Crossref] [PubMed]
  35. M. Vega-Hernández, E. Martínez-Montes, J. M. Sánchez-Bornot, A. Lage-Castellanos, and P. A. Valdés-Sosa, “Penalized Least squares methods for solving the eeg inverse problem,” Stat. Sin. 18, 1535–1551 (2008).
  36. A. K. Dunn, A. Devor, H. Bolay, M. L. Andermann, M. A. Moskowitz, A. M. Dale, and D. A. Boas, “Simultaneous imaging of total cerebral hemoglobin concentration, oxygenation, and blood flow during functional activation,” Opt. Lett. 28(1), 28–30 (2003).
    [Crossref] [PubMed]
  37. J. Ashburner and K. J. Friston, “Unified segmentation,” Neuroimage 26(3), 839–851 (2005).
    [Crossref] [PubMed]
  38. B. Dogdas, D. W. Shattuck, and R. M. Leahy, “Segmentation of skull and scalp in 3-D human MRI using mathematical morphology,” Hum. Brain Mapp. 26(4), 273–285 (2005).
    [Crossref] [PubMed]
  39. N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: a review,” Front. Hum. Neurosci. 9, 3(2015).
  40. G. Strangman, M. A. Franceschini, and D. A. Boas, “Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters,” Neuroimage 18(4), 865–879 (2003).
    [Crossref] [PubMed]
  41. H. Dehghani, S. Srinivasan, B. W. Pogue, and A. Gibson, “Numerical modelling and image reconstruction in diffuse optical tomography,” Philos Trans A Math Phys Eng Sci 367(1900), 3073–3093 (2009).
    [Crossref] [PubMed]
  42. M. Dehaes, L. Gagnon, F. Lesage, M. Pélégrini-Issac, A. Vignaud, R. Valabrègue, R. Grebe, F. Wallois, and H. Benali, “Quantitative investigation of the effect of the extra-cerebral vasculature in diffuse optical imaging: a simulation study,” Biomed. Opt. Express 2(3), 680–695 (2011).
    [Crossref] [PubMed]
  43. P. C. Hansen, “Regularization Tools version 4.0 for Matlab 7.3,” Numer. Algorithms 5224(2), 189–194 (2007).
    [Crossref]
  44. P. C. Hansen, Discrete Inverse Problems: Insight and Algorithms (Society for Industrial and Applied Mathematics, 2010).
  45. S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An Interior-Point Method for Large-Scale -Regularized Least squares,” IEEE J. Sel. Top. Signal Process. 1(4), 606–617 (2007).
    [Crossref]
  46. M. Borrego, N. Trujillo-Barreto, Y. Rodriguez-Puentes, J. Bosch-Bayard, E. Martínez-Montes, L. Melie-Garcia, E. Aubert, and P. Valdés-Sosa, Neuronic Source Localizer: software for calculating Brain electromagnetic Tomography. Presented at the 17th Annual Meeting of the Organization for Human Mapping, June 26–30, 2011, Québec City, Canada.
  47. A. N. Tikhonov and V. Y. Arsenin, On the Solution ofIll-Posed Problems (John Wiley and Sons, 1977).
  48. P. C. Hansen, “The L-Curve and its Use in the Numerical Treatment of Inverse Problems,” Comput. Inverse Probl. Electrocardiol. Ed P Johnston Adv. Comput. Bioeng. 4, 119–142 (2000).
  49. D. A. Boas, K. Chen, D. Grebert, and M. A. Franceschini, “Improving the diffuse optical imaging spatial resolution of the cerebral hemodynamic response to brain activation in humans,” Opt. Lett. 29(13), 1506–1508 (2004).
    [Crossref] [PubMed]
  50. S. A. Walker, S. Fantini, and E. Gratton, “Image reconstruction by backprojection from frequency-domain optical measurements in highly scattering media,” Appl. Opt. 36(1), 170–174 (1997).
    [Crossref] [PubMed]
  51. T. Das, B. P. V. Dileep, and P. K. Dutta, “Generalized curved beam back-projection method for near-infrared imaging using banana function,” Appl. Opt. 57(8), 1838–1848 (2018).
    [Crossref] [PubMed]
  52. Y. Zhai and S. A. Cummer, “Fast tomographic reconstruction strategy for diffuse optical tomography,” Opt. Express 17(7), 5285–5297 (2009).
    [Crossref] [PubMed]
  53. 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]
  54. C. Leng, D. Yu, S. Zhang, Y. An, and Y. Hu, “Reconstruction Method for Optical Tomography Based on the Linearized Bregman Iteration with Sparse Regularization,” Comput. Math. Methods Med. 2015, 304191 (2015).
    [Crossref] [PubMed]
  55. J. Tang, B. Han, W. Han, B. Bi, and L. Li, “Mixed Total Variation and L1 Regularization Method for Optical Tomography Based on Radiative Transfer Equation,” Comput. Math. Methods Med. 2017, 2953560 (2017).
    [Crossref] [PubMed]
  56. W. Lu, D. Lighter, and I. B. Styles, “L1-norm based nonlinear reconstruction improves quantitative accuracy of spectral diffuse optical tomography,” Biomed. Opt. Express 9(4), 1423–1444 (2018).
    [Crossref] [PubMed]
  57. C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
    [Crossref] [PubMed]
  58. G. H. Golub, M. Heath, and G. Wahba, “Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter,” Technometrics 21(2), 215–223 (1979).
    [Crossref]
  59. A. C. Evans, D. L. Collins, S. R. Mills, E. D. Brown, R. L. Kelly, and T. M. Peters, “3D statistical neuroanatomical models from 305 MRI volumes,” in 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference (1993), pp. 1813–1817.
    [Crossref]
  60. T. Fawcett, ROC Graphs: Notes and Practical Considerations for Researchers (2004).
  61. R. A. Chowdhury, Y. Zerouali, T. Hedrich, M. Heers, E. Kobayashi, J.-M. Lina, and C. Grova, “MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy,” Brain Topogr. 28(6), 785–812 (2015).
    [Crossref] [PubMed]
  62. A. Machado, O. Marcotte, J. M. Lina, E. Kobayashi, and C. Grova, “Optimal optode montage on electroencephalography/functional near-infrared spectroscopy caps dedicated to study epileptic discharges,” J. Biomed. Opt. 19(2), 026010 (2014).
    [Crossref] [PubMed]
  63. W. J. Youden, “Index for rating diagnostic tests,” Cancer 3(1), 32–35 (1950).
    [Crossref] [PubMed]
  64. E. F. Schisterman, N. J. Perkins, A. Liu, and H. Bondell, “Optimal Cut-point and Its Corresponding Youden Index to Discriminate Individuals Using Pooled Blood Samples,” Epidemiology 16(1), 73–81 (2005).
    [Crossref] [PubMed]
  65. M. Rudnaya and R. Ochshorn, “Sharpness functions for computational aesthetics and image sublimation,” IAENG Int. J. Comput. Sci. 38, 359–367 (2011).
  66. M. Friedman, “The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance,” J. Am. Stat. Assoc. 32(200), 675–701 (1937).
    [Crossref]
  67. D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
    [Crossref] [PubMed]
  68. V. Y. Toronov, X. Zhang, and A. G. Webb, “A spatial and temporal comparison of hemodynamic signals measured using optical and functional magnetic resonance imaging during activation in the human primary visual cortex,” Neuroimage 34(3), 1136–1148 (2007).
    [Crossref] [PubMed]
  69. F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
    [Crossref] [PubMed]
  70. Q. Zhang, J. P. Culver, and E. L. Miller, “Reconstructing chromosphere concentration images directly by continuous-wave diffuse optical tomography,”Opt. Lett.  29, 256–258 (2004).
  71. S. L. Jacques, “Optical properties of biological tissues : a review,” Phys. Med. Biol.  58, 5007 (2013).
    [Crossref]
  72. J. Prakash and P. K. Yalavarthy, “A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography,” Med. Phys. 40(3), 033101 (2013).
    [Crossref] [PubMed]
  73. L. A. Dempsey, M. Persad, S. Powell, D. Chitnis, and J. C. Hebden, “Geometrically complex 3D-printed phantoms for diffuse optical imaging,” Biomed. Opt. Express 8(3), 1754–1762 (2017).
    [Crossref] [PubMed]

2018 (2)

2017 (3)

J. Tang, B. Han, W. Han, B. Bi, and L. Li, “Mixed Total Variation and L1 Regularization Method for Optical Tomography Based on Radiative Transfer Equation,” Comput. Math. Methods Med. 2017, 2953560 (2017).
[Crossref] [PubMed]

L. A. Dempsey, M. Persad, S. Powell, D. Chitnis, and J. C. Hebden, “Geometrically complex 3D-printed phantoms for diffuse optical imaging,” Biomed. Opt. Express 8(3), 1754–1762 (2017).
[Crossref] [PubMed]

D. Paz-Linares, M. Vega-Hernández, P. A. Rojas-López, P. A. Valdés-Hernández, E. Martínez-Montes, and P. A. Valdés-Sosa, “Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models,” Front. Neurosci. 11, 635 (2017).
[Crossref] [PubMed]

2016 (1)

O. Yamashita, T. Shimokawa, R. Aisu, T. Amita, Y. Inoue, and M. A. Sato, “Multi-subject and multi-task experimental validation of the hierarchical Bayesian diffuse optical tomography algorithm,” Neuroimage 135, 287–299 (2016).
[Crossref] [PubMed]

2015 (4)

A. Miyamoto, K. Watanabe, K. Ikeda, and M.-A. Sato, “Variational inference with ARD prior for NIRS diffuse optical tomography,” IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1109–1114 (2015).
[Crossref] [PubMed]

R. A. Chowdhury, Y. Zerouali, T. Hedrich, M. Heers, E. Kobayashi, J.-M. Lina, and C. Grova, “MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy,” Brain Topogr. 28(6), 785–812 (2015).
[Crossref] [PubMed]

C. Leng, D. Yu, S. Zhang, Y. An, and Y. Hu, “Reconstruction Method for Optical Tomography Based on the Linearized Bregman Iteration with Sparse Regularization,” Comput. Math. Methods Med. 2015, 304191 (2015).
[Crossref] [PubMed]

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: a review,” Front. Hum. Neurosci. 9, 3(2015).

2014 (7)

A. Machado, O. Marcotte, J. M. Lina, E. Kobayashi, and C. Grova, “Optimal optode montage on electroencephalography/functional near-infrared spectroscopy caps dedicated to study epileptic discharges,” J. Biomed. Opt. 19(2), 026010 (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. Quantum Electron. 20(2), 74–82 (2014).
[Crossref]

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]

M. Schweiger and S. Arridge, “The Toast++ software suite for forward and inverse modeling in optical tomography,” J. Biomed. Opt. 19(4), 040801 (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), 096006 (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]

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]

2013 (2)

S. L. Jacques, “Optical properties of biological tissues : a review,” Phys. Med. Biol.  58, 5007 (2013).
[Crossref]

J. Prakash and P. K. Yalavarthy, “A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography,” Med. Phys. 40(3), 033101 (2013).
[Crossref] [PubMed]

2012 (4)

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[Crossref] [PubMed]

C. B. Shaw and P. K. Yalavarthy, “Effective contrast recovery in rapid dynamic near-infrared diffuse optical tomography using ℓ(1)-norm-based linear image reconstruction method,” J. Biomed. Opt. 17(8), 086009 (2012).
[Crossref] [PubMed]

V. C. Kavuri, Z.-J. Lin, F. Tian, and H. Liu, “Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography,” Biomed. Opt. Express 3(5), 943–957 (2012).
[Crossref] [PubMed]

2011 (4)

F. B. Haeussinger, S. Heinzel, T. Hahn, M. Schecklmann, A.-C. Ehlis, and A. J. Fallgatter, “Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging,” PLoS One 6(10), e26377 (2011).
[Crossref] [PubMed]

S. Okawa, Y. Hoshi, and Y. Yamada, “Improvement of image quality of time-domain diffuse optical tomography with l sparsity regularization,” Biomed. Opt. Express 2(12), 3334–3348 (2011).
[Crossref] [PubMed]

M. Rudnaya and R. Ochshorn, “Sharpness functions for computational aesthetics and image sublimation,” IAENG Int. J. Comput. Sci. 38, 359–367 (2011).

M. Dehaes, L. Gagnon, F. Lesage, M. Pélégrini-Issac, A. Vignaud, R. Valabrègue, R. Grebe, F. Wallois, and H. Benali, “Quantitative investigation of the effect of the extra-cerebral vasculature in diffuse optical imaging: a simulation study,” Biomed. Opt. Express 2(3), 680–695 (2011).
[Crossref] [PubMed]

2010 (3)

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[Crossref] [PubMed]

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[Crossref] [PubMed]

M. Süzen, A. Giannoula, and T. Durduran, “Compressed sensing in diffuse optical tomography,” Opt. Express 18(23), 23676–23690 (2010).
[Crossref] [PubMed]

2009 (5)

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

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).
[Crossref] [PubMed]

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. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

H. Dehghani, S. Srinivasan, B. W. Pogue, and A. Gibson, “Numerical modelling and image reconstruction in diffuse optical tomography,” Philos Trans A Math Phys Eng Sci 367(1900), 3073–3093 (2009).
[Crossref] [PubMed]

Y. Zhai and S. A. Cummer, “Fast tomographic reconstruction strategy for diffuse optical tomography,” Opt. Express 17(7), 5285–5297 (2009).
[Crossref] [PubMed]

2008 (4)

S. D. Konecky, G. Y. Panasyuk, K. Lee, V. Markel, A. G. Yodh, and J. C. Schotland, “Imaging complex structures with diffuse light,” Opt. Express 16(7), 5048–5060 (2008).
[Crossref] [PubMed]

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
[Crossref] [PubMed]

S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Müller, and G. Nolte, “Combining sparsity and rotational invariance in EEG/MEG source reconstruction,” Neuroimage 42(2), 726–738 (2008).
[Crossref] [PubMed]

M. Vega-Hernández, E. Martínez-Montes, J. M. Sánchez-Bornot, A. Lage-Castellanos, and P. A. Valdés-Sosa, “Penalized Least squares methods for solving the eeg inverse problem,” Stat. Sin. 18, 1535–1551 (2008).

2007 (4)

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]

P. C. Hansen, “Regularization Tools version 4.0 for Matlab 7.3,” Numer. Algorithms 5224(2), 189–194 (2007).
[Crossref]

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An Interior-Point Method for Large-Scale -Regularized Least squares,” IEEE J. Sel. Top. Signal Process. 1(4), 606–617 (2007).
[Crossref]

V. Y. Toronov, X. Zhang, and A. G. Webb, “A spatial and temporal comparison of hemodynamic signals measured using optical and functional magnetic resonance imaging during activation in the human primary visual cortex,” Neuroimage 34(3), 1136–1148 (2007).
[Crossref] [PubMed]

2006 (1)

C. Grova, J. Daunizeau, J.-M. Lina, C. G. Bénar, H. Benali, and J. Gotman, “Evaluation of EEG localization methods using realistic simulations of interictal spikes,” Neuroimage 29(3), 734–753 (2006).
[Crossref] [PubMed]

2005 (4)

J. Ashburner and K. J. Friston, “Unified segmentation,” Neuroimage 26(3), 839–851 (2005).
[Crossref] [PubMed]

B. Dogdas, D. W. Shattuck, and R. M. Leahy, “Segmentation of skull and scalp in 3-D human MRI using mathematical morphology,” Hum. Brain Mapp. 26(4), 273–285 (2005).
[Crossref] [PubMed]

D. A. Boas and A. M. Dale, “Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function,” Appl. Opt. 44(10), 1957–1968 (2005).
[Crossref] [PubMed]

E. F. Schisterman, N. J. Perkins, A. Liu, and H. Bondell, “Optimal Cut-point and Its Corresponding Youden Index to Discriminate Individuals Using Pooled Blood Samples,” Epidemiology 16(1), 73–81 (2005).
[Crossref] [PubMed]

2004 (5)

Q. Zhang, J. P. Culver, and E. L. Miller, “Reconstructing chromosphere concentration images directly by continuous-wave diffuse optical tomography,”Opt. Lett.  29, 256–258 (2004).

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

D. A. Boas, K. Chen, D. Grebert, and M. A. Franceschini, “Improving the diffuse optical imaging spatial resolution of the cerebral hemodynamic response to brain activation in humans,” Opt. Lett. 29(13), 1506–1508 (2004).
[Crossref] [PubMed]

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

N. J. Trujillo-Barreto, E. Aubert-Vázquez, and P. A. Valdés-Sosa, “Bayesian model averaging in EEG/MEG imaging,” Neuroimage 21(4), 1300–1319 (2004).
[Crossref] [PubMed]

2003 (2)

A. K. Dunn, A. Devor, H. Bolay, M. L. Andermann, M. A. Moskowitz, A. M. Dale, and D. A. Boas, “Simultaneous imaging of total cerebral hemoglobin concentration, oxygenation, and blood flow during functional activation,” Opt. Lett. 28(1), 28–30 (2003).
[Crossref] [PubMed]

G. Strangman, M. A. Franceschini, and D. A. Boas, “Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters,” Neuroimage 18(4), 865–879 (2003).
[Crossref] [PubMed]

2002 (1)

2000 (1)

P. C. Hansen, “The L-Curve and its Use in the Numerical Treatment of Inverse Problems,” Comput. Inverse Probl. Electrocardiol. Ed P Johnston Adv. Comput. Bioeng. 4, 119–142 (2000).

1999 (2)

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

M. Fuchs, M. Wagner, T. Köhler, and H. A. Wischmann, “Linear and nonlinear current density reconstructions,” J. Clin. Neurophysiol. 16(3), 267–295 (1999).
[Crossref] [PubMed]

1997 (1)

1995 (1)

P. Marqui, R. D. “Source localization: continuing discussion of the inverse problem,” ISBET Newsletter 6, 9–30 (1995).

1994 (2)

M. S. Hämäläinen and R. J. Ilmoniemi, “Interpreting magnetic fields of the brain: minimum norm estimates,” Med. Biol. Eng. Comput. 32(1), 35–42 (1994).
[Crossref] [PubMed]

R. D. Pascual-Marqui, C. M. Michel, and D. Lehmann, “Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain,” Int. J. Psychophysiol. 18(1), 49–65 (1994).
[Crossref] [PubMed]

1979 (1)

G. H. Golub, M. Heath, and G. Wahba, “Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter,” Technometrics 21(2), 215–223 (1979).
[Crossref]

1950 (1)

W. J. Youden, “Index for rating diagnostic tests,” Cancer 3(1), 32–35 (1950).
[Crossref] [PubMed]

1937 (1)

M. Friedman, “The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance,” J. Am. Stat. Assoc. 32(200), 675–701 (1937).
[Crossref]

Aisu, R.

O. Yamashita, T. Shimokawa, R. Aisu, T. Amita, Y. Inoue, and M. A. Sato, “Multi-subject and multi-task experimental validation of the hierarchical Bayesian diffuse optical tomography algorithm,” Neuroimage 135, 287–299 (2016).
[Crossref] [PubMed]

Amita, T.

O. Yamashita, T. Shimokawa, R. Aisu, T. Amita, Y. Inoue, and M. A. Sato, “Multi-subject and multi-task experimental validation of the hierarchical Bayesian diffuse optical tomography algorithm,” Neuroimage 135, 287–299 (2016).
[Crossref] [PubMed]

An, Y.

C. Leng, D. Yu, S. Zhang, Y. An, and Y. Hu, “Reconstruction Method for Optical Tomography Based on the Linearized Bregman Iteration with Sparse Regularization,” Comput. Math. Methods Med. 2015, 304191 (2015).
[Crossref] [PubMed]

Andermann, M. L.

Arridge, S.

M. Schweiger and S. Arridge, “The Toast++ software suite for forward and inverse modeling in optical tomography,” J. Biomed. Opt. 19(4), 040801 (2014).
[Crossref] [PubMed]

Arridge, S. R.

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

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

Ashburner, J.

J. Ashburner and K. J. Friston, “Unified segmentation,” Neuroimage 26(3), 839–851 (2005).
[Crossref] [PubMed]

Aubert-Vázquez, E.

N. J. Trujillo-Barreto, E. Aubert-Vázquez, and P. A. Valdés-Sosa, “Bayesian model averaging in EEG/MEG imaging,” Neuroimage 21(4), 1300–1319 (2004).
[Crossref] [PubMed]

Bastien, D.

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

Benali, H.

Bénar, C. G.

C. Grova, J. Daunizeau, J.-M. Lina, C. G. Bénar, H. Benali, and J. Gotman, “Evaluation of EEG localization methods using realistic simulations of interictal spikes,” Neuroimage 29(3), 734–753 (2006).
[Crossref] [PubMed]

Bi, B.

J. Tang, B. Han, W. Han, B. Bi, and L. Li, “Mixed Total Variation and L1 Regularization Method for Optical Tomography Based on Radiative Transfer Equation,” Comput. Math. Methods Med. 2017, 2953560 (2017).
[Crossref] [PubMed]

Boas, D.

Boas, D. A.

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[Crossref] [PubMed]

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[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).
[Crossref] [PubMed]

D. A. Boas and A. M. Dale, “Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function,” Appl. Opt. 44(10), 1957–1968 (2005).
[Crossref] [PubMed]

D. A. Boas, K. Chen, D. Grebert, and M. A. Franceschini, “Improving the diffuse optical imaging spatial resolution of the cerebral hemodynamic response to brain activation in humans,” Opt. Lett. 29(13), 1506–1508 (2004).
[Crossref] [PubMed]

A. K. Dunn, A. Devor, H. Bolay, M. L. Andermann, M. A. Moskowitz, A. M. Dale, and D. A. Boas, “Simultaneous imaging of total cerebral hemoglobin concentration, oxygenation, and blood flow during functional activation,” Opt. Lett. 28(1), 28–30 (2003).
[Crossref] [PubMed]

G. Strangman, M. A. Franceschini, and D. A. Boas, “Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters,” Neuroimage 18(4), 865–879 (2003).
[Crossref] [PubMed]

Bolay, H.

Bondell, H.

E. F. Schisterman, N. J. Perkins, A. Liu, and H. Bondell, “Optimal Cut-point and Its Corresponding Youden Index to Discriminate Individuals Using Pooled Blood Samples,” Epidemiology 16(1), 73–81 (2005).
[Crossref] [PubMed]

Boyd, S.

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An Interior-Point Method for Large-Scale -Regularized Least squares,” IEEE J. Sel. Top. Signal Process. 1(4), 606–617 (2007).
[Crossref]

Brown, E. D.

A. C. Evans, D. L. Collins, S. R. Mills, E. D. Brown, R. L. Kelly, and T. M. Peters, “3D statistical neuroanatomical models from 305 MRI volumes,” in 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference (1993), pp. 1813–1817.
[Crossref]

Caffini, M.

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[Crossref] [PubMed]

Camilleri, K. P.

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
[Crossref] [PubMed]

Cao, N.

Carpenter, C. M.

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. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

Cassar, T.

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
[Crossref] [PubMed]

Chen, K.

Chitnis, D.

Chowdhury, R. A.

R. A. Chowdhury, Y. Zerouali, T. Hedrich, M. Heers, E. Kobayashi, J.-M. Lina, and C. Grova, “MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy,” Brain Topogr. 28(6), 785–812 (2015).
[Crossref] [PubMed]

Collins, D. L.

A. C. Evans, D. L. Collins, S. R. Mills, E. D. Brown, R. L. Kelly, and T. M. Peters, “3D statistical neuroanatomical models from 305 MRI volumes,” in 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference (1993), pp. 1813–1817.
[Crossref]

Cooper, R. J.

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[Crossref] [PubMed]

Culver, J.

Culver, J. P.

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]

Q. Zhang, J. P. Culver, and E. L. Miller, “Reconstructing chromosphere concentration images directly by continuous-wave diffuse optical tomography,”Opt. Lett.  29, 256–258 (2004).

Cummer, S. A.

Custo, A.

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[Crossref] [PubMed]

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[Crossref] [PubMed]

Dale, A. M.

Dan, I.

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[Crossref] [PubMed]

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[Crossref] [PubMed]

Das, T.

Daunizeau, J.

C. Grova, J. Daunizeau, J.-M. Lina, C. G. Bénar, H. Benali, and J. Gotman, “Evaluation of EEG localization methods using realistic simulations of interictal spikes,” Neuroimage 29(3), 734–753 (2006).
[Crossref] [PubMed]

Davis, S. C.

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. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

Dehaes, M.

Dehghani, H.

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]

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. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

H. Dehghani, S. Srinivasan, B. W. Pogue, and A. Gibson, “Numerical modelling and image reconstruction in diffuse optical tomography,” Philos Trans A Math Phys Eng Sci 367(1900), 3073–3093 (2009).
[Crossref] [PubMed]

Dempsey, L. A.

Devor, A.

Dileep, B. P. V.

Dogdas, B.

B. Dogdas, D. W. Shattuck, and R. M. Leahy, “Segmentation of skull and scalp in 3-D human MRI using mathematical morphology,” Hum. Brain Mapp. 26(4), 273–285 (2005).
[Crossref] [PubMed]

Dubb, J.

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[Crossref] [PubMed]

Dunn, A.

Dunn, A. K.

Durduran, T.

Dutta, P. K.

Eames, M. E.

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. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

Eggebrecht, A. 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]

Ehlis, A.-C.

F. B. Haeussinger, S. Heinzel, T. Hahn, M. Schecklmann, A.-C. Ehlis, and A. J. Fallgatter, “Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging,” PLoS One 6(10), e26377 (2011).
[Crossref] [PubMed]

Evans, A. C.

A. C. Evans, D. L. Collins, S. R. Mills, E. D. Brown, R. L. Kelly, and T. M. Peters, “3D statistical neuroanatomical models from 305 MRI volumes,” in 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference (1993), pp. 1813–1817.
[Crossref]

Fabri, S. G.

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
[Crossref] [PubMed]

Fallgatter, A. J.

F. B. Haeussinger, S. Heinzel, T. Hahn, M. Schecklmann, A.-C. Ehlis, and A. J. Fallgatter, “Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging,” PLoS One 6(10), e26377 (2011).
[Crossref] [PubMed]

Fang, Q.

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[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).
[Crossref] [PubMed]

Fantini, S.

Fawcett, T.

T. Fawcett, ROC Graphs: Notes and Practical Considerations for Researchers (2004).

Ferradal, S. L.

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]

Fischl, B.

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[Crossref] [PubMed]

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[Crossref] [PubMed]

Franceschini, M. A.

D. A. Boas, K. Chen, D. Grebert, and M. A. Franceschini, “Improving the diffuse optical imaging spatial resolution of the cerebral hemodynamic response to brain activation in humans,” Opt. Lett. 29(13), 1506–1508 (2004).
[Crossref] [PubMed]

G. Strangman, M. A. Franceschini, and D. A. Boas, “Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters,” Neuroimage 18(4), 865–879 (2003).
[Crossref] [PubMed]

Friedman, M.

M. Friedman, “The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance,” J. Am. Stat. Assoc. 32(200), 675–701 (1937).
[Crossref]

Friston, K. J.

J. Ashburner and K. J. Friston, “Unified segmentation,” Neuroimage 26(3), 839–851 (2005).
[Crossref] [PubMed]

Fuchs, M.

M. Fuchs, M. Wagner, T. Köhler, and H. A. Wischmann, “Linear and nonlinear current density reconstructions,” J. Clin. Neurophysiol. 16(3), 267–295 (1999).
[Crossref] [PubMed]

Gagnon, L.

Gallagher, A.

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

Giannoula, A.

Gibson, A.

H. Dehghani, S. Srinivasan, B. W. Pogue, and A. Gibson, “Numerical modelling and image reconstruction in diffuse optical tomography,” Philos Trans A Math Phys Eng Sci 367(1900), 3073–3093 (2009).
[Crossref] [PubMed]

Golub, G. H.

G. H. Golub, M. Heath, and G. Wahba, “Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter,” Technometrics 21(2), 215–223 (1979).
[Crossref]

Gonzalez, S.

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

Gorinevsky, D.

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An Interior-Point Method for Large-Scale -Regularized Least squares,” IEEE J. Sel. Top. Signal Process. 1(4), 606–617 (2007).
[Crossref]

Gotman, J.

C. Grova, J. Daunizeau, J.-M. Lina, C. G. Bénar, H. Benali, and J. Gotman, “Evaluation of EEG localization methods using realistic simulations of interictal spikes,” Neuroimage 29(3), 734–753 (2006).
[Crossref] [PubMed]

Gratton, E.

Grave de Peralta, R.

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

Grebe, R.

Grebert, D.

Grech, R.

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
[Crossref] [PubMed]

Grimson, W. E. L.

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[Crossref] [PubMed]

Grova, C.

R. A. Chowdhury, Y. Zerouali, T. Hedrich, M. Heers, E. Kobayashi, J.-M. Lina, and C. Grova, “MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy,” Brain Topogr. 28(6), 785–812 (2015).
[Crossref] [PubMed]

A. Machado, O. Marcotte, J. M. Lina, E. Kobayashi, and C. Grova, “Optimal optode montage on electroencephalography/functional near-infrared spectroscopy caps dedicated to study epileptic discharges,” J. Biomed. Opt. 19(2), 026010 (2014).
[Crossref] [PubMed]

C. Grova, J. Daunizeau, J.-M. Lina, C. G. Bénar, H. Benali, and J. Gotman, “Evaluation of EEG localization methods using realistic simulations of interictal spikes,” Neuroimage 29(3), 734–753 (2006).
[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), 096006 (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]

Haeussinger, F. B.

F. B. Haeussinger, S. Heinzel, T. Hahn, M. Schecklmann, A.-C. Ehlis, and A. J. Fallgatter, “Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging,” PLoS One 6(10), e26377 (2011).
[Crossref] [PubMed]

Hahn, T.

F. B. Haeussinger, S. Heinzel, T. Hahn, M. Schecklmann, A.-C. Ehlis, and A. J. Fallgatter, “Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging,” PLoS One 6(10), e26377 (2011).
[Crossref] [PubMed]

Hämäläinen, M. S.

M. S. Hämäläinen and R. J. Ilmoniemi, “Interpreting magnetic fields of the brain: minimum norm estimates,” Med. Biol. Eng. Comput. 32(1), 35–42 (1994).
[Crossref] [PubMed]

Han, B.

J. Tang, B. Han, W. Han, B. Bi, and L. Li, “Mixed Total Variation and L1 Regularization Method for Optical Tomography Based on Radiative Transfer Equation,” Comput. Math. Methods Med. 2017, 2953560 (2017).
[Crossref] [PubMed]

Han, W.

J. Tang, B. Han, W. Han, B. Bi, and L. Li, “Mixed Total Variation and L1 Regularization Method for Optical Tomography Based on Radiative Transfer Equation,” Comput. Math. Methods Med. 2017, 2953560 (2017).
[Crossref] [PubMed]

Hansen, P. C.

P. C. Hansen, “Regularization Tools version 4.0 for Matlab 7.3,” Numer. Algorithms 5224(2), 189–194 (2007).
[Crossref]

P. C. Hansen, “The L-Curve and its Use in the Numerical Treatment of Inverse Problems,” Comput. Inverse Probl. Electrocardiol. Ed P Johnston Adv. Comput. Bioeng. 4, 119–142 (2000).

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), 096006 (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]

S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Müller, and G. Nolte, “Combining sparsity and rotational invariance in EEG/MEG source reconstruction,” Neuroimage 42(2), 726–738 (2008).
[Crossref] [PubMed]

Heath, M.

G. H. Golub, M. Heath, and G. Wahba, “Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter,” Technometrics 21(2), 215–223 (1979).
[Crossref]

Hebden, J. C.

Hedrich, T.

R. A. Chowdhury, Y. Zerouali, T. Hedrich, M. Heers, E. Kobayashi, J.-M. Lina, and C. Grova, “MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy,” Brain Topogr. 28(6), 785–812 (2015).
[Crossref] [PubMed]

Heers, M.

R. A. Chowdhury, Y. Zerouali, T. Hedrich, M. Heers, E. Kobayashi, J.-M. Lina, and C. Grova, “MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy,” Brain Topogr. 28(6), 785–812 (2015).
[Crossref] [PubMed]

Heinzel, S.

F. B. Haeussinger, S. Heinzel, T. Hahn, M. Schecklmann, A.-C. Ehlis, and A. J. Fallgatter, “Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging,” PLoS One 6(10), e26377 (2011).
[Crossref] [PubMed]

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]

Hong, K.-S.

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: a review,” Front. Hum. Neurosci. 9, 3(2015).

Hoshi, Y.

Hu, Y.

C. Leng, D. Yu, S. Zhang, Y. An, and Y. Hu, “Reconstruction Method for Optical Tomography Based on the Linearized Bregman Iteration with Sparse Regularization,” Comput. Math. Methods Med. 2015, 304191 (2015).
[Crossref] [PubMed]

Ikeda, K.

A. Miyamoto, K. Watanabe, K. Ikeda, and M.-A. Sato, “Variational inference with ARD prior for NIRS diffuse optical tomography,” IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1109–1114 (2015).
[Crossref] [PubMed]

Ilmoniemi, R. J.

M. S. Hämäläinen and R. J. Ilmoniemi, “Interpreting magnetic fields of the brain: minimum norm estimates,” Med. Biol. Eng. Comput. 32(1), 35–42 (1994).
[Crossref] [PubMed]

Inoue, Y.

O. Yamashita, T. Shimokawa, R. Aisu, T. Amita, Y. Inoue, and M. A. Sato, “Multi-subject and multi-task experimental validation of the hierarchical Bayesian diffuse optical tomography algorithm,” Neuroimage 135, 287–299 (2016).
[Crossref] [PubMed]

Jacobs, M.

Jacques, S. L.

S. L. Jacques, “Optical properties of biological tissues : a review,” Phys. Med. Biol.  58, 5007 (2013).
[Crossref]

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. Quantum Electron. 20(2), 74–82 (2014).
[Crossref]

Kavuri, V. C.

Kelly, R. L.

A. C. Evans, D. L. Collins, S. R. Mills, E. D. Brown, R. L. Kelly, and T. M. Peters, “3D statistical neuroanatomical models from 305 MRI volumes,” in 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference (1993), pp. 1813–1817.
[Crossref]

Kim, S.-J.

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An Interior-Point Method for Large-Scale -Regularized Least squares,” IEEE J. Sel. Top. Signal Process. 1(4), 606–617 (2007).
[Crossref]

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]

Kobayashi, E.

R. A. Chowdhury, Y. Zerouali, T. Hedrich, M. Heers, E. Kobayashi, J.-M. Lina, and C. Grova, “MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy,” Brain Topogr. 28(6), 785–812 (2015).
[Crossref] [PubMed]

A. Machado, O. Marcotte, J. M. Lina, E. Kobayashi, and C. Grova, “Optimal optode montage on electroencephalography/functional near-infrared spectroscopy caps dedicated to study epileptic discharges,” J. Biomed. Opt. 19(2), 026010 (2014).
[Crossref] [PubMed]

Koh, K.

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An Interior-Point Method for Large-Scale -Regularized Least squares,” IEEE J. Sel. Top. Signal Process. 1(4), 606–617 (2007).
[Crossref]

Köhler, T.

M. Fuchs, M. Wagner, T. Köhler, and H. A. Wischmann, “Linear and nonlinear current density reconstructions,” J. Clin. Neurophysiol. 16(3), 267–295 (1999).
[Crossref] [PubMed]

Konecky, S. D.

Lage-Castellanos, A.

M. Vega-Hernández, E. Martínez-Montes, J. M. Sánchez-Bornot, A. Lage-Castellanos, and P. A. Valdés-Sosa, “Penalized Least squares methods for solving the eeg inverse problem,” Stat. Sin. 18, 1535–1551 (2008).

Lantz, G.

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

Lassonde, M.

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

Leahy, R. M.

B. Dogdas, D. W. Shattuck, and R. M. Leahy, “Segmentation of skull and scalp in 3-D human MRI using mathematical morphology,” Hum. Brain Mapp. 26(4), 273–285 (2005).
[Crossref] [PubMed]

Lee, K.

Lehmann, D.

R. D. Pascual-Marqui, C. M. Michel, and D. Lehmann, “Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain,” Int. J. Psychophysiol. 18(1), 49–65 (1994).
[Crossref] [PubMed]

Leng, C.

C. Leng, D. Yu, S. Zhang, Y. An, and Y. Hu, “Reconstruction Method for Optical Tomography Based on the Linearized Bregman Iteration with Sparse Regularization,” Comput. Math. Methods Med. 2015, 304191 (2015).
[Crossref] [PubMed]

Lepore, F.

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

Lesage, F.

Li, L.

J. Tang, B. Han, W. Han, B. Bi, and L. Li, “Mixed Total Variation and L1 Regularization Method for Optical Tomography Based on Radiative Transfer Equation,” Comput. Math. Methods Med. 2017, 2953560 (2017).
[Crossref] [PubMed]

Lighter, D.

Lin, Z.-J.

Lina, J. M.

A. Machado, O. Marcotte, J. M. Lina, E. Kobayashi, and C. Grova, “Optimal optode montage on electroencephalography/functional near-infrared spectroscopy caps dedicated to study epileptic discharges,” J. Biomed. Opt. 19(2), 026010 (2014).
[Crossref] [PubMed]

Lina, J.-M.

R. A. Chowdhury, Y. Zerouali, T. Hedrich, M. Heers, E. Kobayashi, J.-M. Lina, and C. Grova, “MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy,” Brain Topogr. 28(6), 785–812 (2015).
[Crossref] [PubMed]

C. Grova, J. Daunizeau, J.-M. Lina, C. G. Bénar, H. Benali, and J. Gotman, “Evaluation of EEG localization methods using realistic simulations of interictal spikes,” Neuroimage 29(3), 734–753 (2006).
[Crossref] [PubMed]

Liu, A.

E. F. Schisterman, N. J. Perkins, A. Liu, and H. Bondell, “Optimal Cut-point and Its Corresponding Youden Index to Discriminate Individuals Using Pooled Blood Samples,” Epidemiology 16(1), 73–81 (2005).
[Crossref] [PubMed]

Liu, H.

Lu, W.

Lustig, M.

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An Interior-Point Method for Large-Scale -Regularized Least squares,” IEEE J. Sel. Top. Signal Process. 1(4), 606–617 (2007).
[Crossref]

Machado, A.

A. Machado, O. Marcotte, J. M. Lina, E. Kobayashi, and C. Grova, “Optimal optode montage on electroencephalography/functional near-infrared spectroscopy caps dedicated to study epileptic discharges,” J. Biomed. Opt. 19(2), 026010 (2014).
[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. Quantum Electron. 20(2), 74–82 (2014).
[Crossref]

Marcotte, O.

A. Machado, O. Marcotte, J. M. Lina, E. Kobayashi, and C. Grova, “Optimal optode montage on electroencephalography/functional near-infrared spectroscopy caps dedicated to study epileptic discharges,” J. Biomed. Opt. 19(2), 026010 (2014).
[Crossref] [PubMed]

Markel, V.

Marqui, P.

P. Marqui, R. D. “Source localization: continuing discussion of the inverse problem,” ISBET Newsletter 6, 9–30 (1995).

Martínez-Montes, E.

D. Paz-Linares, M. Vega-Hernández, P. A. Rojas-López, P. A. Valdés-Hernández, E. Martínez-Montes, and P. A. Valdés-Sosa, “Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models,” Front. Neurosci. 11, 635 (2017).
[Crossref] [PubMed]

M. Vega-Hernández, E. Martínez-Montes, J. M. Sánchez-Bornot, A. Lage-Castellanos, and P. A. Valdés-Sosa, “Penalized Least squares methods for solving the eeg inverse problem,” Stat. Sin. 18, 1535–1551 (2008).

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]

Mesquita, R.

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[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]

Michel, C. M.

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

R. D. Pascual-Marqui, C. M. Michel, and D. Lehmann, “Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain,” Int. J. Psychophysiol. 18(1), 49–65 (1994).
[Crossref] [PubMed]

Miller, E. L.

Q. Zhang, J. P. Culver, and E. L. Miller, “Reconstructing chromosphere concentration images directly by continuous-wave diffuse optical tomography,”Opt. Lett.  29, 256–258 (2004).

Mills, S. R.

A. C. Evans, D. L. Collins, S. R. Mills, E. D. Brown, R. L. Kelly, and T. M. Peters, “3D statistical neuroanatomical models from 305 MRI volumes,” in 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference (1993), pp. 1813–1817.
[Crossref]

Miyamoto, A.

A. Miyamoto, K. Watanabe, K. Ikeda, and M.-A. Sato, “Variational inference with ARD prior for NIRS diffuse optical tomography,” IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1109–1114 (2015).
[Crossref] [PubMed]

Moskowitz, M. A.

Muehlemann, T.

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[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]

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), 096006 (2014).
[Crossref] [PubMed]

S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Müller, and G. Nolte, “Combining sparsity and rotational invariance in EEG/MEG source reconstruction,” Neuroimage 42(2), 726–738 (2008).
[Crossref] [PubMed]

Murray, M. M.

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

Muscat, J.

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
[Crossref] [PubMed]

Naseer, N.

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: a review,” Front. Hum. Neurosci. 9, 3(2015).

Nehorai, A.

Nikulin, V. V.

S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Müller, and G. Nolte, “Combining sparsity and rotational invariance in EEG/MEG source reconstruction,” Neuroimage 42(2), 726–738 (2008).
[Crossref] [PubMed]

Nolte, G.

S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Müller, and G. Nolte, “Combining sparsity and rotational invariance in EEG/MEG source reconstruction,” Neuroimage 42(2), 726–738 (2008).
[Crossref] [PubMed]

Ochshorn, R.

M. Rudnaya and R. Ochshorn, “Sharpness functions for computational aesthetics and image sublimation,” IAENG Int. J. Comput. Sci. 38, 359–367 (2011).

Okawa, S.

Panasyuk, G. Y.

Pascual-Marqui, R. D.

R. D. Pascual-Marqui, C. M. Michel, and D. Lehmann, “Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain,” Int. J. Psychophysiol. 18(1), 49–65 (1994).
[Crossref] [PubMed]

Paulsen, K. D.

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. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

Paz-Linares, D.

D. Paz-Linares, M. Vega-Hernández, P. A. Rojas-López, P. A. Valdés-Hernández, E. Martínez-Montes, and P. A. Valdés-Sosa, “Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models,” Front. Neurosci. 11, 635 (2017).
[Crossref] [PubMed]

Pélégrini-Issac, M.

Perkins, N. J.

E. F. Schisterman, N. J. Perkins, A. Liu, and H. Bondell, “Optimal Cut-point and Its Corresponding Youden Index to Discriminate Individuals Using Pooled Blood Samples,” Epidemiology 16(1), 73–81 (2005).
[Crossref] [PubMed]

Persad, M.

Peters, T. M.

A. C. Evans, D. L. Collins, S. R. Mills, E. D. Brown, R. L. Kelly, and T. M. Peters, “3D statistical neuroanatomical models from 305 MRI volumes,” in 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference (1993), pp. 1813–1817.
[Crossref]

Pogue, B. W.

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. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

H. Dehghani, S. Srinivasan, B. W. Pogue, and A. Gibson, “Numerical modelling and image reconstruction in diffuse optical tomography,” Philos Trans A Math Phys Eng Sci 367(1900), 3073–3093 (2009).
[Crossref] [PubMed]

Powell, S.

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. Quantum Electron. 20(2), 74–82 (2014).
[Crossref]

J. Prakash and P. K. Yalavarthy, “A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography,” Med. Phys. 40(3), 033101 (2013).
[Crossref] [PubMed]

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]

Rojas-López, P. A.

D. Paz-Linares, M. Vega-Hernández, P. A. Rojas-López, P. A. Valdés-Hernández, E. Martínez-Montes, and P. A. Valdés-Sosa, “Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models,” Front. Neurosci. 11, 635 (2017).
[Crossref] [PubMed]

Rudnaya, M.

M. Rudnaya and R. Ochshorn, “Sharpness functions for computational aesthetics and image sublimation,” IAENG Int. J. Comput. Sci. 38, 359–367 (2011).

Sakkalis, V.

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
[Crossref] [PubMed]

Sánchez-Bornot, J. M.

M. Vega-Hernández, E. Martínez-Montes, J. M. Sánchez-Bornot, A. Lage-Castellanos, and P. A. Valdés-Sosa, “Penalized Least squares methods for solving the eeg inverse problem,” Stat. Sin. 18, 1535–1551 (2008).

Sato, M. A.

O. Yamashita, T. Shimokawa, R. Aisu, T. Amita, Y. Inoue, and M. A. Sato, “Multi-subject and multi-task experimental validation of the hierarchical Bayesian diffuse optical tomography algorithm,” Neuroimage 135, 287–299 (2016).
[Crossref] [PubMed]

Sato, M.-A.

A. Miyamoto, K. Watanabe, K. Ikeda, and M.-A. Sato, “Variational inference with ARD prior for NIRS diffuse optical tomography,” IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1109–1114 (2015).
[Crossref] [PubMed]

Schecklmann, M.

F. B. Haeussinger, S. Heinzel, T. Hahn, M. Schecklmann, A.-C. Ehlis, and A. J. Fallgatter, “Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging,” PLoS One 6(10), e26377 (2011).
[Crossref] [PubMed]

Schisterman, E. F.

E. F. Schisterman, N. J. Perkins, A. Liu, and H. Bondell, “Optimal Cut-point and Its Corresponding Youden Index to Discriminate Individuals Using Pooled Blood Samples,” Epidemiology 16(1), 73–81 (2005).
[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]

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[Crossref] [PubMed]

Schotland, J. C.

Schweiger, M.

M. Schweiger and S. Arridge, “The Toast++ software suite for forward and inverse modeling in optical tomography,” J. Biomed. Opt. 19(4), 040801 (2014).
[Crossref] [PubMed]

Shattuck, D. W.

B. Dogdas, D. W. Shattuck, and R. M. Leahy, “Segmentation of skull and scalp in 3-D human MRI using mathematical morphology,” Hum. Brain Mapp. 26(4), 273–285 (2005).
[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. Quantum Electron. 20(2), 74–82 (2014).
[Crossref]

C. B. Shaw and P. K. Yalavarthy, “Effective contrast recovery in rapid dynamic near-infrared diffuse optical tomography using ℓ(1)-norm-based linear image reconstruction method,” J. Biomed. Opt. 17(8), 086009 (2012).
[Crossref] [PubMed]

Shimokawa, T.

O. Yamashita, T. Shimokawa, R. Aisu, T. Amita, Y. Inoue, and M. A. Sato, “Multi-subject and multi-task experimental validation of the hierarchical Bayesian diffuse optical tomography algorithm,” Neuroimage 135, 287–299 (2016).
[Crossref] [PubMed]

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]

Spichtig, S.

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[Crossref] [PubMed]

Spinelli, L.

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

Srinivasan, S.

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. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

H. Dehghani, S. Srinivasan, B. W. Pogue, and A. Gibson, “Numerical modelling and image reconstruction in diffuse optical tomography,” Philos Trans A Math Phys Eng Sci 367(1900), 3073–3093 (2009).
[Crossref] [PubMed]

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]

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), 096006 (2014).
[Crossref] [PubMed]

Stott, J.

Strangman, G.

G. Strangman, M. A. Franceschini, and D. A. Boas, “Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters,” Neuroimage 18(4), 865–879 (2003).
[Crossref] [PubMed]

Styles, I. B.

Süzen, M.

Tang, J.

J. Tang, B. Han, W. Han, B. Bi, and L. Li, “Mixed Total Variation and L1 Regularization Method for Optical Tomography Based on Radiative Transfer Equation,” Comput. Math. Methods Med. 2017, 2953560 (2017).
[Crossref] [PubMed]

Thériault, M.

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

Tian, F.

Toronov, V. Y.

V. Y. Toronov, X. Zhang, and A. G. Webb, “A spatial and temporal comparison of hemodynamic signals measured using optical and functional magnetic resonance imaging during activation in the human primary visual cortex,” Neuroimage 34(3), 1136–1148 (2007).
[Crossref] [PubMed]

Tremblay, J.

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

Trujillo-Barreto, N. J.

N. J. Trujillo-Barreto, E. Aubert-Vázquez, and P. A. Valdés-Sosa, “Bayesian model averaging in EEG/MEG imaging,” Neuroimage 21(4), 1300–1319 (2004).
[Crossref] [PubMed]

Tsuzuki, D.

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[Crossref] [PubMed]

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[Crossref] [PubMed]

Valabrègue, R.

Valdés-Hernández, P. A.

D. Paz-Linares, M. Vega-Hernández, P. A. Rojas-López, P. A. Valdés-Hernández, E. Martínez-Montes, and P. A. Valdés-Sosa, “Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models,” Front. Neurosci. 11, 635 (2017).
[Crossref] [PubMed]

Valdés-Sosa, P. A.

D. Paz-Linares, M. Vega-Hernández, P. A. Rojas-López, P. A. Valdés-Hernández, E. Martínez-Montes, and P. A. Valdés-Sosa, “Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models,” Front. Neurosci. 11, 635 (2017).
[Crossref] [PubMed]

M. Vega-Hernández, E. Martínez-Montes, J. M. Sánchez-Bornot, A. Lage-Castellanos, and P. A. Valdés-Sosa, “Penalized Least squares methods for solving the eeg inverse problem,” Stat. Sin. 18, 1535–1551 (2008).

N. J. Trujillo-Barreto, E. Aubert-Vázquez, and P. A. Valdés-Sosa, “Bayesian model averaging in EEG/MEG imaging,” Neuroimage 21(4), 1300–1319 (2004).
[Crossref] [PubMed]

Vannasing, P.

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

Vanrumste, B.

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
[Crossref] [PubMed]

Vega-Hernández, M.

D. Paz-Linares, M. Vega-Hernández, P. A. Rojas-López, P. A. Valdés-Hernández, E. Martínez-Montes, and P. A. Valdés-Sosa, “Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models,” Front. Neurosci. 11, 635 (2017).
[Crossref] [PubMed]

M. Vega-Hernández, E. Martínez-Montes, J. M. Sánchez-Bornot, A. Lage-Castellanos, and P. A. Valdés-Sosa, “Penalized Least squares methods for solving the eeg inverse problem,” Stat. Sin. 18, 1535–1551 (2008).

Vignaud, A.

Wagner, M.

M. Fuchs, M. Wagner, T. Köhler, and H. A. Wischmann, “Linear and nonlinear current density reconstructions,” J. Clin. Neurophysiol. 16(3), 267–295 (1999).
[Crossref] [PubMed]

Wahba, G.

G. H. Golub, M. Heath, and G. Wahba, “Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter,” Technometrics 21(2), 215–223 (1979).
[Crossref]

Walker, S. A.

Wallois, F.

Watanabe, K.

A. Miyamoto, K. Watanabe, K. Ikeda, and M.-A. Sato, “Variational inference with ARD prior for NIRS diffuse optical tomography,” IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1109–1114 (2015).
[Crossref] [PubMed]

Webb, A. G.

V. Y. Toronov, X. Zhang, and A. G. Webb, “A spatial and temporal comparison of hemodynamic signals measured using optical and functional magnetic resonance imaging during activation in the human primary visual cortex,” Neuroimage 34(3), 1136–1148 (2007).
[Crossref] [PubMed]

Wells, W.

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[Crossref] [PubMed]

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[Crossref] [PubMed]

Wischmann, H. A.

M. Fuchs, M. Wagner, T. Köhler, and H. A. Wischmann, “Linear and nonlinear current density reconstructions,” J. Clin. Neurophysiol. 16(3), 267–295 (1999).
[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]

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[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]

Xanthopoulos, P.

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
[Crossref] [PubMed]

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. Quantum Electron. 20(2), 74–82 (2014).
[Crossref]

J. Prakash and P. K. Yalavarthy, “A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography,” Med. Phys. 40(3), 033101 (2013).
[Crossref] [PubMed]

C. B. Shaw and P. K. Yalavarthy, “Effective contrast recovery in rapid dynamic near-infrared diffuse optical tomography using ℓ(1)-norm-based linear image reconstruction method,” J. Biomed. Opt. 17(8), 086009 (2012).
[Crossref] [PubMed]

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. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

Yamada, Y.

Yamashita, O.

O. Yamashita, T. Shimokawa, R. Aisu, T. Amita, Y. Inoue, and M. A. Sato, “Multi-subject and multi-task experimental validation of the hierarchical Bayesian diffuse optical tomography algorithm,” Neuroimage 135, 287–299 (2016).
[Crossref] [PubMed]

Yodh, A. G.

Youden, W. J.

W. J. Youden, “Index for rating diagnostic tests,” Cancer 3(1), 32–35 (1950).
[Crossref] [PubMed]

Yu, D.

C. Leng, D. Yu, S. Zhang, Y. An, and Y. Hu, “Reconstruction Method for Optical Tomography Based on the Linearized Bregman Iteration with Sparse Regularization,” Comput. Math. Methods Med. 2015, 304191 (2015).
[Crossref] [PubMed]

Zerouali, Y.

R. A. Chowdhury, Y. Zerouali, T. Hedrich, M. Heers, E. Kobayashi, J.-M. Lina, and C. Grova, “MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy,” Brain Topogr. 28(6), 785–812 (2015).
[Crossref] [PubMed]

Zervakis, M.

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
[Crossref] [PubMed]

Zhai, Y.

Zhang, Q.

Q. Zhang, J. P. Culver, and E. L. Miller, “Reconstructing chromosphere concentration images directly by continuous-wave diffuse optical tomography,”Opt. Lett.  29, 256–258 (2004).

Zhang, S.

C. Leng, D. Yu, S. Zhang, Y. An, and Y. Hu, “Reconstruction Method for Optical Tomography Based on the Linearized Bregman Iteration with Sparse Regularization,” Comput. Math. Methods Med. 2015, 304191 (2015).
[Crossref] [PubMed]

Zhang, X.

V. Y. Toronov, X. Zhang, and A. G. Webb, “A spatial and temporal comparison of hemodynamic signals measured using optical and functional magnetic resonance imaging during activation in the human primary visual cortex,” Neuroimage 34(3), 1136–1148 (2007).
[Crossref] [PubMed]

Ziehe, A.

S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Müller, and G. Nolte, “Combining sparsity and rotational invariance in EEG/MEG source reconstruction,” Neuroimage 42(2), 726–738 (2008).
[Crossref] [PubMed]

Zimmermann, R.

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]

Appl. Opt. (3)

Biomed. Opt. Express (5)

Brain Res. (1)

D. Bastien, A. Gallagher, J. Tremblay, P. Vannasing, M. Thériault, M. Lassonde, and F. Lepore, “Specific functional asymmetries of the human visual cortex revealed by functional near-infrared spectroscopy,” Brain Res. 1431, 62–68 (2012).
[Crossref] [PubMed]

Brain Topogr. (1)

R. A. Chowdhury, Y. Zerouali, T. Hedrich, M. Heers, E. Kobayashi, J.-M. Lina, and C. Grova, “MEG-EEG Information Fusion and Electromagnetic Source Imaging: From Theory to Clinical Application in Epilepsy,” Brain Topogr. 28(6), 785–812 (2015).
[Crossref] [PubMed]

Cancer (1)

W. J. Youden, “Index for rating diagnostic tests,” Cancer 3(1), 32–35 (1950).
[Crossref] [PubMed]

Clin. Neurophysiol. (2)

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. Grave de Peralta, “EEG source imaging,” Clin. Neurophysiol. 115(10), 2195–2222 (2004).
[Crossref] [PubMed]

Commun. Numer. Methods Eng. (1)

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. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

Comput. Inverse Probl. Electrocardiol. Ed P Johnston Adv. Comput. Bioeng. (1)

P. C. Hansen, “The L-Curve and its Use in the Numerical Treatment of Inverse Problems,” Comput. Inverse Probl. Electrocardiol. Ed P Johnston Adv. Comput. Bioeng. 4, 119–142 (2000).

Comput. Math. Methods Med. (2)

C. Leng, D. Yu, S. Zhang, Y. An, and Y. Hu, “Reconstruction Method for Optical Tomography Based on the Linearized Bregman Iteration with Sparse Regularization,” Comput. Math. Methods Med. 2015, 304191 (2015).
[Crossref] [PubMed]

J. Tang, B. Han, W. Han, B. Bi, and L. Li, “Mixed Total Variation and L1 Regularization Method for Optical Tomography Based on Radiative Transfer Equation,” Comput. Math. Methods Med. 2017, 2953560 (2017).
[Crossref] [PubMed]

Epidemiology (1)

E. F. Schisterman, N. J. Perkins, A. Liu, and H. Bondell, “Optimal Cut-point and Its Corresponding Youden Index to Discriminate Individuals Using Pooled Blood Samples,” Epidemiology 16(1), 73–81 (2005).
[Crossref] [PubMed]

Front. Hum. Neurosci. (1)

N. Naseer and K.-S. Hong, “fNIRS-based brain-computer interfaces: a review,” Front. Hum. Neurosci. 9, 3(2015).

Front. Neurosci. (1)

D. Paz-Linares, M. Vega-Hernández, P. A. Rojas-López, P. A. Valdés-Hernández, E. Martínez-Montes, and P. A. Valdés-Sosa, “Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models,” Front. Neurosci. 11, 635 (2017).
[Crossref] [PubMed]

Hum. Brain Mapp. (1)

B. Dogdas, D. W. Shattuck, and R. M. Leahy, “Segmentation of skull and scalp in 3-D human MRI using mathematical morphology,” Hum. Brain Mapp. 26(4), 273–285 (2005).
[Crossref] [PubMed]

IAENG Int. J. Comput. Sci. (1)

M. Rudnaya and R. Ochshorn, “Sharpness functions for computational aesthetics and image sublimation,” IAENG Int. J. Comput. Sci. 38, 359–367 (2011).

IEEE J. Sel. Top. Quantum Electron. (1)

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. Quantum Electron. 20(2), 74–82 (2014).
[Crossref]

IEEE J. Sel. Top. Signal Process. (1)

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An Interior-Point Method for Large-Scale -Regularized Least squares,” IEEE J. Sel. Top. Signal Process. 1(4), 606–617 (2007).
[Crossref]

IEEE Trans. Neural Netw. Learn. Syst. (1)

A. Miyamoto, K. Watanabe, K. Ikeda, and M.-A. Sato, “Variational inference with ARD prior for NIRS diffuse optical tomography,” IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1109–1114 (2015).
[Crossref] [PubMed]

Int. J. Psychophysiol. (1)

R. D. Pascual-Marqui, C. M. Michel, and D. Lehmann, “Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain,” Int. J. Psychophysiol. 18(1), 49–65 (1994).
[Crossref] [PubMed]

Inverse Probl. (2)

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

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

ISBET Newsletter (1)

P. Marqui, R. D. “Source localization: continuing discussion of the inverse problem,” ISBET Newsletter 6, 9–30 (1995).

J. Am. Stat. Assoc. (1)

M. Friedman, “The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance,” J. Am. Stat. Assoc. 32(200), 675–701 (1937).
[Crossref]

J. Biomed. Opt. (5)

A. Machado, O. Marcotte, J. M. Lina, E. Kobayashi, and C. Grova, “Optimal optode montage on electroencephalography/functional near-infrared spectroscopy caps dedicated to study epileptic discharges,” J. Biomed. Opt. 19(2), 026010 (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), 096006 (2014).
[Crossref] [PubMed]

C. B. Shaw and P. K. Yalavarthy, “Effective contrast recovery in rapid dynamic near-infrared diffuse optical tomography using ℓ(1)-norm-based linear image reconstruction method,” J. Biomed. Opt. 17(8), 086009 (2012).
[Crossref] [PubMed]

M. Schweiger and S. Arridge, “The Toast++ software suite for forward and inverse modeling in optical tomography,” J. Biomed. Opt. 19(4), 040801 (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]

J. Clin. Neurophysiol. (1)

M. Fuchs, M. Wagner, T. Köhler, and H. A. Wischmann, “Linear and nonlinear current density reconstructions,” J. Clin. Neurophysiol. 16(3), 267–295 (1999).
[Crossref] [PubMed]

J. Neuroeng. Rehabil. (1)

R. Grech, T. Cassar, J. Muscat, K. P. Camilleri, S. G. Fabri, M. Zervakis, P. Xanthopoulos, V. Sakkalis, and B. Vanrumste, “Review on solving the inverse problem in EEG source analysis,” J. Neuroeng. Rehabil. 5(1), 25 (2008).
[Crossref] [PubMed]

Med. Biol. Eng. Comput. (1)

M. S. Hämäläinen and R. J. Ilmoniemi, “Interpreting magnetic fields of the brain: minimum norm estimates,” Med. Biol. Eng. Comput. 32(1), 35–42 (1994).
[Crossref] [PubMed]

Med. Phys. (1)

J. Prakash and P. K. Yalavarthy, “A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography,” Med. Phys. 40(3), 033101 (2013).
[Crossref] [PubMed]

Nat. Photonics (1)

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]

Neuroimage (10)

A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage 49(1), 561–567 (2010).
[Crossref] [PubMed]

R. J. Cooper, M. Caffini, J. Dubb, Q. Fang, A. Custo, D. Tsuzuki, B. Fischl, W. Wells, I. Dan, and D. A. Boas, “Validating atlas-guided DOT: a comparison of diffuse optical tomography informed by atlas and subject-specific anatomies,” Neuroimage 62(3), 1999–2006 (2012).
[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]

S. Haufe, V. V. Nikulin, A. Ziehe, K.-R. Müller, and G. Nolte, “Combining sparsity and rotational invariance in EEG/MEG source reconstruction,” Neuroimage 42(2), 726–738 (2008).
[Crossref] [PubMed]

C. Grova, J. Daunizeau, J.-M. Lina, C. G. Bénar, H. Benali, and J. Gotman, “Evaluation of EEG localization methods using realistic simulations of interictal spikes,” Neuroimage 29(3), 734–753 (2006).
[Crossref] [PubMed]

G. Strangman, M. A. Franceschini, and D. A. Boas, “Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters,” Neuroimage 18(4), 865–879 (2003).
[Crossref] [PubMed]

J. Ashburner and K. J. Friston, “Unified segmentation,” Neuroimage 26(3), 839–851 (2005).
[Crossref] [PubMed]

N. J. Trujillo-Barreto, E. Aubert-Vázquez, and P. A. Valdés-Sosa, “Bayesian model averaging in EEG/MEG imaging,” Neuroimage 21(4), 1300–1319 (2004).
[Crossref] [PubMed]

O. Yamashita, T. Shimokawa, R. Aisu, T. Amita, Y. Inoue, and M. A. Sato, “Multi-subject and multi-task experimental validation of the hierarchical Bayesian diffuse optical tomography algorithm,” Neuroimage 135, 287–299 (2016).
[Crossref] [PubMed]

V. Y. Toronov, X. Zhang, and A. G. Webb, “A spatial and temporal comparison of hemodynamic signals measured using optical and functional magnetic resonance imaging during activation in the human primary visual cortex,” Neuroimage 34(3), 1136–1148 (2007).
[Crossref] [PubMed]

Numer. Algorithms (1)

P. C. Hansen, “Regularization Tools version 4.0 for Matlab 7.3,” Numer. Algorithms 5224(2), 189–194 (2007).
[Crossref]

Opt. Express (6)

Opt. Lett (1)

Q. Zhang, J. P. Culver, and E. L. Miller, “Reconstructing chromosphere concentration images directly by continuous-wave diffuse optical tomography,”Opt. Lett.  29, 256–258 (2004).

Opt. Lett. (2)

Philos Trans A Math Phys Eng Sci (1)

H. Dehghani, S. Srinivasan, B. W. Pogue, and A. Gibson, “Numerical modelling and image reconstruction in diffuse optical tomography,” Philos Trans A Math Phys Eng Sci 367(1900), 3073–3093 (2009).
[Crossref] [PubMed]

Phys. Med. Biol (1)

S. L. Jacques, “Optical properties of biological tissues : a review,” Phys. Med. Biol.  58, 5007 (2013).
[Crossref]

Physiol. Meas. (1)

F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
[Crossref] [PubMed]

PLoS One (1)

F. B. Haeussinger, S. Heinzel, T. Hahn, M. Schecklmann, A.-C. Ehlis, and A. J. Fallgatter, “Simulation of Near-Infrared Light Absorption Considering Individual Head and Prefrontal Cortex Anatomy: Implications for Optical Neuroimaging,” PLoS One 6(10), e26377 (2011).
[Crossref] [PubMed]

Stat. Sin. (1)

M. Vega-Hernández, E. Martínez-Montes, J. M. Sánchez-Bornot, A. Lage-Castellanos, and P. A. Valdés-Sosa, “Penalized Least squares methods for solving the eeg inverse problem,” Stat. Sin. 18, 1535–1551 (2008).

Technometrics (1)

G. H. Golub, M. Heath, and G. Wahba, “Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter,” Technometrics 21(2), 215–223 (1979).
[Crossref]

Other (7)

A. C. Evans, D. L. Collins, S. R. Mills, E. D. Brown, R. L. Kelly, and T. M. Peters, “3D statistical neuroanatomical models from 305 MRI volumes,” in 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference (1993), pp. 1813–1817.
[Crossref]

T. Fawcett, ROC Graphs: Notes and Practical Considerations for Researchers (2004).

P. C. Hansen, Discrete Inverse Problems: Insight and Algorithms (Society for Industrial and Applied Mathematics, 2010).

M. Borrego, N. Trujillo-Barreto, Y. Rodriguez-Puentes, J. Bosch-Bayard, E. Martínez-Montes, L. Melie-Garcia, E. Aubert, and P. Valdés-Sosa, Neuronic Source Localizer: software for calculating Brain electromagnetic Tomography. Presented at the 17th Annual Meeting of the Organization for Human Mapping, June 26–30, 2011, Québec City, Canada.

A. N. Tikhonov and V. Y. Arsenin, On the Solution ofIll-Posed Problems (John Wiley and Sons, 1977).

O. Yamashita, T. Shimokawa, T. Kosaka, M. A. Sato, T. Amita, and Y. Inoue, “Hierarchical Bayesian model for diffuse optical tomography of human brains,” in The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems (2012), pp. 1451–1455.

S. J. Madsen, Optical Methods and Instrumentation in Brain Imaging and Therapy (Springer Science & Business Media, 2012), Chap. 3.

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Figures (6)

Fig. 1
Fig. 1 fNIRS Set-up. (A) Co-registration of the optode covering the visual cortex with the 3D-MRI reconstruction. The montage included 8 laser light (830 nm and 690 nm) sources (green) and 8 detectors (shown in red). (B) Sensitivity maps of one boundary measurement corresponding to the represented detector (X) and source (O) overlaid on T1 axial view (logarithmic color scale).
Fig. 2
Fig. 2 Representative normalized DOT images (divided by the maximum value across all voxels) estimated with each reconstruction method. Among the 300 simulations, four simulated activations were selected (top row, two with a single cluster and two with two clusters) based on the AUC percentile ranks in order to show various level of performances: A) 90%, B) 70%, C) 30%, D) 10%, the highest percentiles being the more accurate results, regardless the method. Note that in the case of L1 method, we used diamonds to illustrate the position of the estimated activations as they were always very small in size and hardly visible.
Fig. 3
Fig. 3 The histogram shows the median localization error for the simulated data without added noise (in blue), the simulated data with added Gaussian noise (20dB pSNR; in green) and the simulations with a depth between 25 and 50 mm (in red) for each method.
Fig. 4
Fig. 4 Retinotopic effect is shown with HbO and HbR concentration variations measured with fNIRS. (A,B,C,D) lower right visual field stimulation, (A) Average HbO and HbR hemodynamic curves and standard deviation for each hemisphere (left in blue/turquoise and right in red/pink). The orange horizontal line shows the visual stimulation duration (0-30 seconds) and the visual field stimulated is presented below. (B) HbO (top) and HbR (bottom) topographic projections of the measurements, averaged over the 10 to 35 s time window (marked by the horizontal black bar on the graphs A and E). (C) HbO (top) and HbR (bottom) BMA reconstruction. (D) HbO (top) and HbR (bottom) rLSQR reconstruction. (E,F,G,H) same as (A,B,C,D) but using a lower left visual field stimulation.
Fig. 5
Fig. 5 Distributions of the AUC values obtained for all simulations using BMA. The upper histogram shows the distribution for simulated activations located in a single region of the BMA atlas whereas lower histogram shows the distribution for simulated activations located in at least two regions of the BMA atlas.
Fig. 6
Fig. 6 Distributions of the localization error plotted for each method as a function of the depth of the simulated active source for data without noise (in blue), with additive Gaussian noise (pSNR = 20dB; in green) and with deep sources (a depth between 25 and 50 mm; in red).

Tables (3)

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Table 1 Optical coefficients of different tissues used for the head modela

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Table 2 Quality measures of reconstruction (830 nm) for the different methodsa

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Table 3 Friedman ranks for localization errors in noise-free, noisy and deep source simulationsa

Equations (21)

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i ω N Φ ( r , ω ) / c 0 · κ ( r ) Φ ( r , ω ) + μ a 0 ( r ) Φ ( r , ω ) = q 0 ( r , ω )
d O D ( r s , r d , λ , t ) log ( Φ ( r s , r d , λ , t ) Φ 0 ( r s , r d , λ , t 0 ) )
d O D ( r s , r d , λ , t ) = 1 G ( r s , r d , λ ) i = 1 n G ( r s , r i , λ ) G ( r d , r i , λ ) Δ μ a ( r i , λ , t   )
y = Ax + ξ
x ^ r L S Q R = argmin x ( y Ax 2 + α ( x x 0 ) 2 )
x ^ t S V D = i = 1 M t u i T y σ i ν i
x ^ B P = A T y
x ^ L 1 = argmin x ( y Ax 2 + α | x | 1 )
x ^ M N =  argmin x ( | | Ax | | 2   + α | | x | | 2 )
x ^ M N = ( A T A + α Ι n ) 1 A T y
x ^ L O R E T A = argmin x ( | | y Ax| | 2 + α | | Lx | | 2 )
x ^ L O R E T A = ( A T A + α L T L ) 1 A T y
p ( y | x , θ ) = N ( Ax ,   θ )
p ( x | y,θ, H k ) = p ( y | x , θ , H k ) p ( x | θ , H k ) p ( y | θ , H k )
p ( θ | y, H k ) = p ( y | θ , H k ) p ( θ | H k ) p ( y | H k )
p ( H k | y ) = p ( y | H k ) p ( H k ) p ( y )
p ( x | y ) = a l l H k p ( x | y , H k ) p ( H k | y ) = k p ( x | y , H k ) p ( H k | y )
p S N R = 20 log 10 ( max ( d O D ) σ n o i s e )
B l u r r i n g = 1 n i = 1 n x i 2
L o c a l i z a t i o n _ e r r o r = x ^ p e a k x t r u e _ p e a k
[ d O D ( λ 1 ) d O D ( λ 2 ) ] = [ ε HbO 1 )A(λ 1 ) ε HbR 1 )A(λ 1 ) ε HbO 2 )A(λ 2 ) ε HbR 2 )A(λ 2 ) ] [ Δ [ HbO ] Δ [ HbR ] ]

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