OSA's Digital Library

Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 4, Iss. 11 — Nov. 1, 2013
  • pp: 2411–2432

Extended hierarchical Bayesian diffuse optical tomography for removing scalp artifact

Takeaki Shimokawa, Takashi Kosaka, Okito Yamashita, Nobuo Hiroe, Takashi Amita, Yoshihiro Inoue, and Masa-aki Sato  »View Author Affiliations

Biomedical Optics Express, Vol. 4, Issue 11, pp. 2411-2432 (2013)

View Full Text Article

Enhanced HTML    Acrobat PDF (2509 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Functional near-infrared spectroscopy (fNIRS) can non-invasively measure hemodynamic responses in the cerebral cortex with a portable apparatus. However, the observation signal in fNIRS measurements is contaminated by the artifact signal from the hemodynamic response in the scalp. In this paper, we propose a method to separate the signals from the cortex and the scalp by estimating both hemodynamic changes by diffuse optical tomography (DOT). In the inverse problem of DOT, we introduce smooth regularization to the hemodynamic change in the scalp and sparse regularization to that in the cortex based on the nature of the hemodynamic responses. These appropriate regularization models, with the spatial information of optical paths of many measurement channels, allow three-dimensional reconstruction of both hemodynamic changes. We validate our proposed method through two-layer phantom experiments and MRI-based head-model simulations. In both experiments, the proposed method simultaneously estimates the superficial smooth activity in the scalp area and the deep localized activity in the cortical area.

© 2013 Optical Society of America

OCIS Codes
(100.3010) Image processing : Image reconstruction techniques
(100.3190) Image processing : Inverse problems
(170.3880) Medical optics and biotechnology : Medical and biological imaging

ToC Category:
Image Reconstruction and Inverse Problems

Original Manuscript: September 3, 2013
Manuscript Accepted: September 27, 2013
Published: October 10, 2013

Takeaki Shimokawa, Takashi Kosaka, Okito Yamashita, Nobuo Hiroe, Takashi Amita, Yoshihiro Inoue, and Masa-aki Sato, "Extended hierarchical Bayesian diffuse optical tomography for removing scalp artifact," Biomed. Opt. Express 4, 2411-2432 (2013)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. F. F. Jöbsis, “Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters,” Science 198, 1264–1267 (1977). [CrossRef] [PubMed]
  2. Y. Hoshi, M. Tamura, “Detection of dynamic changes in cerebral oxygenation coupled to neuronal function during mental work in man,” Neurosci. Lett. 150, 5–8 (1993). [CrossRef] [PubMed]
  3. T. Kato, A. Kamei, S. Takashima, T. Ozaki, “Human visual cortical function during photic stimulation monitoring by means of near-infrared spectroscopy,” J. Cereb. Blood Flow Metab. 13, 516–520 (1993). [CrossRef] [PubMed]
  4. A. Villringer, J. Planck, C. Hock, L. Schleinkofer, U. Dirnagl, “Near infrared spectroscopy (NIRS): a new tool to study hemodynamic changes during activation of brain function in human adults,” Neurosci. Lett. 154, 101–104 (1993). [CrossRef] [PubMed]
  5. B. Chance, Z. Zhuang, C. UnAh, C. Alter, L. Lipton, “Cognition-activated low-frequency modulation of light absorption in human brain,” Proc. Natl. Acad. Sci. U.S.A. 90, 3770–3774 (1993). [CrossRef] [PubMed]
  6. M. Ferrari, V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” NeuroImage 63, 921–935 (2012). [CrossRef] [PubMed]
  7. T. Takahashi, Y. Takikawa, R. Kawagoe, S. Shibuya, T. Iwano, S. Kitazawa, “Influence of skin blood flow on near-infrared spectroscopy signals measured on the forehead during a verbal fluency task,” NeuroImage 57, 991–1002 (2011). [CrossRef] [PubMed]
  8. E. Kirilina, A. Jelzow, A Heine, M. Niessing, H. Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” NeuroImage 61, 70–81 (2012). [CrossRef] [PubMed]
  9. Y. Zhang, D. H. Brooks, M. A. Franceschini, D. A. Boas, “Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging,” J. Biomed. Opt. 10, 011014 (2005). [CrossRef]
  10. S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, 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). [CrossRef]
  11. Q. Zhang, E. N. Brown, 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). [CrossRef] [PubMed]
  12. Q. Zhang, G. E. Strangman, G. Ganis, “Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: how well and when does it work?” NeuroImage 45, 788–794 (2009). [CrossRef] [PubMed]
  13. T. Yamada, S. Umeyama, K. Matsuda, “Multidistance probe arrangement to eliminate artifacts in functional near-infrared spectroscopy,” J. Biomed. Opt. 14, 064034 (2009). [CrossRef]
  14. N. M. Gregg, B. R. White, B. W. Zeff, A. J. Berger, J. P. Culver, “Brain specificity of diffuse optical imaging: improvements from superficial signal regression and tomography,” Front. Neuroenergetics 2, 1–8 (2010).
  15. R. B. Saager, N. L. Telleri, A. J. Berger, “Two-detector Corrected Near Infrared Spectroscopy (C-NIRS) detects hemodynamic activation responses more robustly than single-detector NIRS,” NeuroImage 55, 1679–1685 (2011). [CrossRef] [PubMed]
  16. L. Gagnon, R. J. Cooper, M. A. Yücel, K. L. Perdue, D. N. Greve, D. A. Boas, “Short separation channel location impacts the performance of short channel regression in NIRS,” NeuroImage 59, 2518–2528 (2012). [CrossRef]
  17. L. Gagnon, M. A. Yücel, D. A. Boas, R. J. Cooper, “Further improvement in reducing superficial contamination in NIRS using double short separation measurements,” NeuroImage (in press).
  18. T. Funane, H. Atsumori, T. Katura, A. N. Obata, H. Sato, Y. Tanikawa, E. Okada, M. Kiguchi, “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” NeuroImage (in press).
  19. A. P. Gibson, J. C. Hebden, S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50, R1–R43 (2005). [CrossRef] [PubMed]
  20. D. A. Boas, A. M. Dale, “Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function,” Appl. Opt. 44, 1957–1968 (2005). [CrossRef] [PubMed]
  21. A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” NeuroImage 61, 1120–1128 (2012). [CrossRef] [PubMed]
  22. C. Habermehl, S. Holtze, J. Steinbrink, S. P. Koch, H. Obrig, J. Mehnert, C. H. Schmitz, “Somatosensory activation of two fingers can be discriminated with ultrahigh-density diffuse optical tomography,” NeuroImage 59, 3201–3211 (2011). [CrossRef] [PubMed]
  23. T. Shimokawa, T. Kosaka, O. Yamashita, N. Hiroe, T. Amita, Y. Inoue, M. Sato, “Hierarchical Bayesian estimation improves depth accuracy and spatial resolution of diffuse optical tomography,” Opt. Express 20, 20427–20446 (2012). [CrossRef] [PubMed]
  24. A. C. Kak, M. Slaney, Principles of Computerized Tomographic Imaging (IEEE Press, New York, 1988).
  25. M. A. O’Leary, “Imaging with diffuse photon density waves,” Ph.D. Thesis, University of Pennsylvania (1996).
  26. S. R. Arridge, “Optical tomography in medical imaging,” Inverse Probl. 15, R41–R93 (1999). [CrossRef]
  27. A. Kienle, M. S. Patterson, N. Dögnitz, R. Bays, G. Wagnières, H. van den Bergh, “Noninvasive determination of the optical properties of two-layered turbid media,” Appl. Opt. 37, 779–791 (1998). [CrossRef]
  28. C. M. Bishop, “Variational principal components,” In Proc. of ICANN 1, 509–514 (1999).
  29. M. Sato, T. Yoshioka, S. Kajihara, K. Toyama, N. Goda, K. Doya, M. Kawato, “Hierarchical Bayesian estimation for MEG inverse problem,” NeuroImage 23, 806–826 (2004). [CrossRef] [PubMed]
  30. S. J. Matcher, C. E. Elwell, C. E. Cooper, M. Cope, D. T. Delpy, “Performance comparison of several published tissue near-infrared spectroscopy algorithms,” Anal. Biochem. 227, 54–68 (1995). [CrossRef] [PubMed]
  31. “FreeSurfer,” http://surfer.nmr.mgh.harvard.edu/ .
  32. “VBMEG, Variational Bayesian Multimodal EncephaloGraphy,” http://vbmeg.atr.jp/ .
  33. Q. Fang, D. A. Boas, “Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units,” Opt. Express 17, 20178–20190 (2009). [CrossRef] [PubMed]
  34. Q. Fang, “Mesh-based Monte Carlo method using fast ray-tracing in Plücker coordinates,” Biomed. Opt. Express 1, 165–175 (2010). [CrossRef] [PubMed]
  35. N. Lange, S. L. Zeger, “Non-linear Fourier time series analysis for human brain mapping by functional magnetic resonance imaging,” Appl. Statist. 46, 1–29 (1997). [CrossRef]
  36. K. J. Worsley, C. H. Liao, J. Aston, V. Petre, G. H. Duncan, F. Morales, A. C. Evans, “A general statistical analysis for fMRI data,” NeuroImage 15, 1–15 (2002). [CrossRef] [PubMed]
  37. E. Okada, D. T. Delpy, “Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer,” Appl. Opt. 42, 2906–2914 (2003). [CrossRef] [PubMed]
  38. V. C. Kavuri, Z. J. Lin, F. Tian, H. Liu, “Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography,” Biomed. Opt. Express 3, 943–957 (2012). [CrossRef] [PubMed]
  39. D. Tsuzuki, V. Jurcak, A. K. Singh, M. Okamoto, E. Watanabe, I. Dan, “Virtual spatial registration of stand-alone fNIRS data to MNI space,” NeuroImage 34, 1506–1518 (2007). [CrossRef] [PubMed]
  40. H. Attias, “Inferring parameters and structure of latent variable models by variational Bayes,” Proc. 15th Conf. on Uncertainty in Artificial Intelligence, Morgan Kaufmann, 21–30 (1999).
  41. M. Sato, “Online model selection based on the variational Bayes,” Neural Comput. 13, 1649–1681 (2001). [CrossRef]
  42. H. Akaike, “Likelihood and the Bayes procedure,” in Bayesian Statistics,J. M. Bernardo, M. H. De Groot, D. V. Lindley, A. F. M. Smith, eds. (Univ. Press, Valencia, 1980), 143–166.
  43. O. Yamashita, “Dynamical EEG inverse problem and causality analysis of fMRI data,” Ph.D. Thesis, Graduate University for Advanced Studies (SOKENDAI) (2004).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited