Open Access
Issue
J. Eur. Opt. Soc.-Rapid Publ.
Volume 6, 2011
Article Number 11033
Number of page(s) 10
DOI https://doi.org/10.2971/jeos.2011.11033
Published online 10 June 2011
  1. F. F. Jöbsis, “Noninvasive infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters” Science 198, 1264–1267 (1977). [CrossRef] [PubMed] [Google Scholar]
  2. B. Montcel, R. Chabrier, and P Poulet, “Detection of cortical activation with time-resolved diffuse optical methods” Appl. Optics 44, 1942–1947 (2005). [NASA ADS] [CrossRef] [Google Scholar]
  3. S. Schmidt, C. M. Schwärzler, F. Sierra, M. Meyer-Wittkopf, and P. Rolfe, “Blood volume changes and oxygenation during labor–a laser spectroscopic analysis” Z. Geburtsh. Neonatol. 205, 33–37 (2001). [CrossRef] [Google Scholar]
  4. P. A. Rea, J. Crowe, Y. Wickramasinghe, and P. Rolfe, “Non-invasive optical methods for the study of cerebral metabolism in the human newborn: a technique for the future?” J. Med. Eng. Technol. 9, 160–166 (1985). [Google Scholar]
  5. H. Obrig, M. Neufang, R. Wenzel, M. Kohl, J. Steinbrink, K. Einhäupl, and A. Villringer, “Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults” NeuroImage 12, 623–639 (2000). [CrossRef] [Google Scholar]
  6. L. F. Leonardo, D. M. Hueber, and T. J. Barstow, “Effects of assuming constant optical scattering on measurements of muscle oxygenation by near-infrared spectroscopy during exercise” J. Appl. Physiol. 102, 358–367 (2007). [CrossRef] [Google Scholar]
  7. G. Strangman, D. A. Bois, and J. P. Sutton, “Non-invasive neuroimaging using near-infrared light” Biol. Psychiat. 52, 679–693 (2002). [CrossRef] [Google Scholar]
  8. A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging” Phys. Med. Biol. 50, R1–R43 (2005). [CrossRef] [Google Scholar]
  9. T. S. Leung, C. E. Elwell, and D. T. Delpy, “Estimation of cerebral oxy- and deoxy-haemoglobin concentration changes in a layered adult head model using near-infrared spectroscopy and multivariate statistical analysis” Phys. Med. Biol. 50, 5783–5798 (2005). [NASA ADS] [CrossRef] [Google Scholar]
  10. S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis” J. Biomed. Opt. 12, 062111 (2007). [NASA ADS] [CrossRef] [Google Scholar]
  11. Y. H. Zhang, D. H. Brooks, M. A. Franceschini, and D. A. Boas, “Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging” J. Biomed. Opt. 10, 011014 (2005). [NASA ADS] [CrossRef] [Google Scholar]
  12. G. Jasdzewski, G. Strangman, J. Wagner, K. K. Kwong, R. A. Poldrack, and D. A. Boas, “Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near infrared spectroscopy” NeuroImage 20, 479–488 (2003). [CrossRef] [Google Scholar]
  13. R. B. Saager and A. J. Berger, “Direct characterization and removal of interfering absorption trends in two-layer turbid media” J. Opt. Soc. Am. A 22, 1874–1882 (2005). [CrossRef] [Google Scholar]
  14. G. Morren, M. Wolf, P. Lemmerling, U. Wolf, J. H. Choi, E. Gratton, L. De Lathauwer, and S. Van Huffel, “Detection of fast neuronal signals in the motor cortex from functional near infrared spectroscopy measurements using independent component analysis” Med. Biol. Eng. Comput. 42, 92–99 (2004). [CrossRef] [Google Scholar]
  15. Q. Zhang, E. N. Brown, and G. E. Strangman, “Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study” J. Biomed. Opt. 12, 044014 (2007). [NASA ADS] [CrossRef] [Google Scholar]
  16. S. Prince, V. Kolehmainen, J. P. Kaipio, M. A. Franceschini, D. Boas, and S. R. Arridge, “Time-series estimation of biological factors in optical diffusion tomography” Phys. Med. Biol. 48, 1491–1504 (2003). [NASA ADS] [CrossRef] [Google Scholar]
  17. S. G. Diamond, T. J. Huppert, V. Kolehmainen, M. A. Franceschini, J. P. Kaipio, S. R. Arridge, and D. A. Boas, “Physiological system identification with the Kalman filter in diffuse optical tomography” Med. Image Comput. Comput. Assist Interv. 8, 649–656 (2005). [Google Scholar]
  18. A. F. Abdelnour and T. Huppert, “Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model” NeuroImage 46, 133–143 (2009). [CrossRef] [Google Scholar]
  19. Y. Zhang, J. W. Sun, and P. Rolfe, “Monte Carlo study for physiological interference reduction in near-infrared spectroscopy based on Empirical Mode Decomposition” J. Mod. Optic. 57, 2159–2169 (2010). [NASA ADS] [CrossRef] [Google Scholar]
  20. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis” Proc. R. Soc. Lond. Ser-A 454, 903–995 (1998). [NASA ADS] [CrossRef] [Google Scholar]
  21. P. Rolfe, “In vivo near-infrared spectroscopy” Annu. Rev. Biomed. Eng. 2, 715–754 (2000). [CrossRef] [Google Scholar]
  22. R. Saager and A. Berger, “Measurement of layer-like hemodynamic trends in scalp and cortex: implementations for physiological baseline suppression in functional near-infrared spectroscopy” J. Biomed. Opt. 13, 034017 (2008). [NASA ADS] [CrossRef] [Google Scholar]
  23. P. S. R. Diniz, Adaptive Filtering: Algorithms and Practical Implementation, Springer Science + Business Media, LLC, New York (2008). [CrossRef] [Google Scholar]
  24. L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML-Monte Carlo modeling of light transport in multi-layered tissues” Comput. Meth. Prog. Bio. 47, 131–146 (1995). [CrossRef] [Google Scholar]
  25. E. Okada and D. T. Delpy, “Near-infrared light propagation in an adult head model. II. Effect of superficial tissue thickness on the sensitivity of the near-infrared spectroscopy signal” Appl. Optics 42, 2915–2922 (2003). [NASA ADS] [CrossRef] [Google Scholar]
  26. S. Umeyama and T. Yamada, “Monte Carlo study of global interference cancellation by multidistance measurement of near-infrared spectroscopy” J. Biomed. Opt. 14, 064025 (2009). [NASA ADS] [CrossRef] [Google Scholar]
  27. N. Okui and E. Okada, “Wavelength dependence of crosstalk in dual wavelength measurement of oxy- and deoxy-hemoglobin” J. Biomed. Opt. 10, 011015 (2005). [NASA ADS] [CrossRef] [Google Scholar]
  28. T. Yamada, S. Umeyama, and K. Matsuda, “Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy” J. Biomed. Opt. 14, 064034 (2009). [NASA ADS] [CrossRef] [Google Scholar]
  29. S. J. Matcher, C. E. Elwell, C. E. Cooper, M. Cope, and D. T. Delpy, “Performance comparison of several published tissue near-infrared spectroscopy algorithms” Anal. Biochem. 227, 54–68 (1995). [CrossRef] [Google Scholar]
  30. M. S. Cohen, “Real-time functional magnetic resonance imaging” Methods 25, 201–220 (2001). [CrossRef] [Google Scholar]
  31. C. E. Elwell, R. Springett, E. Hillman, and D. T. Delpy, “Oscillations in cerebral haemodynamics. Implications for functional activation studies” Adv. Exp. Med. Biol. 471, 57–65 (1999). [CrossRef] [Google Scholar]
  32. T. Müller, J. Timmer, M. Reinhard, E. Oehm, and A. Hetzel, “Detection of very low-frequency oscillations of cerebral haemodynamics is influenced by data detrending” Med. Biol. Eng. Comput. 41, 69–74 (2003). [CrossRef] [Google Scholar]
  33. 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, 649–662 (2010). [NASA ADS] [CrossRef] [Google Scholar]
  34. Y. Yamashita, A. Maki, and H. Koizumi, “Wavelength dependence of the precision of noninvasive optical measurement of oxy-, deoxy-, and total-hemoglobin concentration” Med. Phys. 28, 1108–1114 (2001). [NASA ADS] [CrossRef] [Google Scholar]
  35. S.S. Long, T. B. Zhang, and F. Long, “Causes and solutions of overshoot and undershoot and end swing in Hilbert-Huang transform” Acta Seismologica Sinica 18, 602–610 (2005). [NASA ADS] [CrossRef] [Google Scholar]
  36. S. R. Qin and Y. M. Zhong, “A new envelope algorithm of Hilbert-Huang Transform” Mech. Syst. Signal Pr. 20, 1941–1952 (2006). [NASA ADS] [CrossRef] [Google Scholar]
  37. Z. P. Fan, T. S. Hong, Z. Z. Liu, and Z. Z. Jing, “Improve the Envelope of EMD with Piecewise Linear Fractal Interpolation” Key Eng. Mat. 439-440, 390–395 (2010). [CrossRef] [Google Scholar]
  38. H. Li, H. H. Hao, and Y.L. Sun, “Improved algorithm for empirical mode decomposition with independent elements” J. Harbin Inst. Tech. 41, 245–248 (2009). [Google Scholar]
  39. L. Zhao, X. F. Liu, S. R. Qin, P. H. Ju, and F. Zhao, “Use of masking signal to improve empirical mode decomposition” J. Vib. Shock 29, 13–17 (2010). [Google Scholar]
  40. M. A. Franceschini, S. Fantini, J. H. Thompson, J. P. Culver, and D. A. Boas, “Hemodynamic evoked response of the sensorimotor cortex measured noninvasively with near-infrared optical imaging” Psychophysiology 40, 548–560 (2003). [Google Scholar]
  41. M. A. Franceschini, D. K. Joseph, T. J. Huppert, S. G. Diamond, and D. A. Boas, “Diffuse optical imaging of the whole head” J. Biomed. Opt. 11, 054007 (2006). [NASA ADS] [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.