Near-infrared window in biological tissue


Near-infrared window in biological tissue


The near-infrared (NIR) window (also known as optical window or therapeutic window) defines the range of wavelengths where light has its maximum depth of penetration in tissue. Within the NIR window, scattering is the most dominant light-tissue interaction, and therefore the propagating light becomes diffused rapidly. Since scattering increases the distance travelled by photons within tissue, the probability of photon absorption also increases. Because scattering has weak dependence on wavelength, the NIR window is primarily limited by the light absorption of blood at short wavelengths and water at long wavelengths.

Contents

Absorption properties of tissue components

The absorption coefficient (μa) is defined as the probability of photon absorption in tissue per unit path length[1]. Different tissue components have different μa values. Moreover, μa is a function of wavelength. Below are discussed the absorption properties of the most important chromophores in tissue.

Figure 1: The molar extinction coefficients of HbO2 and Hb .
Figure 1: The molar extinction coefficients of HbO2 and Hb [2].

Blood: Blood consists of two different types of hemoglobin: oxyhemoglobin (HbO2) is bound to oxygen, while deoxyhemoglobin (Hb) is unbound to oxygen. These two different types of hemoglobin exhibit different absorption spectra that are normally represented in terms of molar extinction coefficients, as shown in Figure 1. The molar extinction coefficient of Hb has its highest absorption peak at 420 nm and a second peak at 580 nm. Its spectrum then gradually decreases as light wavelength increases. On the other hand, HbO2 shows its highest absorption peak at 410 nm, and two secondary peaks at 550 nm and 600 nm. As light wavelengths passes 600 nm, HbO2 absorption decays much faster than Hb absorption. The points where the molar extinction coefficient spectra of Hb and HbO2 intersect are called isosbestic points.

By using two different wavelengths, it is possible to calculate the concentrations of oxyhemoglobin (CHbO2) and deoxyhemoglobin (CHb) as shown in the following equations:

\mu_a(\lambda_1) = \ln(10)\varepsilon_{HbO2}(\lambda_1)C_{HbO2}+\ln(10)\varepsilon_{Hb}(\lambda_1)C_{Hb} \,
\mu_a(\lambda_2) = \ln(10)\varepsilon_{HbO2}(\lambda_2)C_{HbO2}+\ln(10)\varepsilon_{Hb}(\lambda_2)C_{Hb} \,
Figure 2: The absorption spectrum of water .
Figure 2: The absorption spectrum of water [3].

Here, λ1 and λ2 are the two wavelengths; εHbO2 and εHb are the molar extinction coefficients of HbO2 and Hb, respectively; CHbO2 and CHb are the molar concentrations of HbO2 and Hb in tissue, respectively. Oxygen saturation (SO2) can then be computed as

SO_2=\frac {C_{HbO2}} {C_{HbO2}+C_{Hb}}

Water: Although water is nearly transparent in the range of visible light, it becomes absorbing over the near-infrared region. Water is a critical component since its concentration is high in human tissue. The absorption spectrum of water in the range from 250 to 1000 nm is shown in Figure 2. Although absorption is rather low in this spectral range, it still contributes to the overall attenuation of tissue.

Figure 3: Figure 3: The molar extinction coefficients of eumelanin and pheomelanin.
Figure 3: The molar extinction coefficients of eumelanin and pheomelanin [4].

Other tissue components with less significant contributions to the total absorption spectrum of tissue are melanin and fat.

Figure 4: Figure 4: The absorption coefficient spectrum of fat .
Figure 4: The absorption coefficient spectrum of fat [5].

Melanin: Melanin is a chromophore that exists in the human epidermal layer of skin responsible for protection from harmful UV radiation. When melanocytes are stimulated by solar radiation, melanin is produced[6]. Melanin is one of the major absorbers of light in some biological tissue (although its contribution is smaller than other components). There are two types of melanin: eumelanin which is black-brown and pheomelanin which is red-yellow[7]. The molar extinction coefficient spectra corresponding to both types are shown in Figure 3.

Fat: Fat is one of the major components in tissue that can comprise 10-40% of tissue. Although not many mammalian fat spectra are available, Figure 4 shows an example extracted from [8].

Figure 5: Figure 5: The absorption coefficient spectrum of fat .
Figure 5: The scattering coefficient spectrum of biological tissue [9].

Scattering properties of tissue components

Optical scattering occurs due to mismatches in refractive index of the different tissue components, ranging from cell membranes to whole cells. Cell nuclei and mitochondria are the most important scatterers [10]. Their dimensions range from 100 nm to 6 μm, and thus fall within the NIR window. Most of these organelles fall in the Mie regime, and exhibit highly anisotropic forward-directed scattering [11].

Light scattering in biological tissue is denoted by the scattering coefficient (μs), which is defined as the probability of photon scattering in tissue per unit path length [12]. Figure 5 shows a plot of the scattering spectrum[13].

Effective attenuation coefficient

Attenuation of light in deep biological tissue depends on the effective attenuation coefficient (μeff), which is defined as

\mu_{eff}=\sqrt{3\mu_a(\mu_a+\mu'_s)}

where \mu^'_s is the transport scattering coefficient defined as

\mu'_s=\mu_s (1-g) \,

where g is the anisotropy of biological tissue, which has a representative value of 0.9. The effective attenuation coefficient is the dominant factor for determining light attenuation at depth d >> 1/ μeff.

Estimation of the NIR window in tissue

NIR window can be computed based on the absorption coefficient spectrum or the effective attenuation coefficient spectrum. A possible criterion for selecting the NIR window is given by the FWHM of the inverse of these spectra.

Oxygen saturation will define the concentration of oxy and deoxyhemoglobin in tissue and so the total absorption spectrum. Depending on the type of tissue, we can consider different situations.

Figure_3_The_absorption_spectrum_for_arteries
Figure 6 (a): The absorption spectrum for arteries (SaO2 ≈ 98%). λmin = 686 nm; NIR window = (634 - 756) nm.
Figure_4_The_absorption_spectrum_for_veins
Figure 6 (b): The absorption spectrum for veins (SvO2 ≈ 60%).

λmin = 730 nm; NIR window = (664 - 934) nm.

Figure_5_The_absorption_spectrum_for_brain_tissue
Figure 6 (c): The absorption spectrum for brain tissue (StO2 ≈ 70%). λmin = 730 nm; NIR window = (656 - 916) nm.

Absorption spectrum for arteries: In this case SaO2 ≈ 98% (arterial oxygen saturation). Then oxyhemoglobin will be dominant in the total absorption (black) and the effective attenuation (magenta) coefficient spectra, as shown in Figure 6 (a).

Absorption spectrum for veins: In this case SvO2 ≈ 60% (venous oxygen saturation). Then oxyhemoglobin and deoxyhemoglobin will have similar contributions to the total absorption (black) and the effective attenuation (magenta) coefficient spectra, as shown in Figure 6 (b).

Absorption spectrum for brain tissue: To define StO2 (tissue oxygen saturation) it is necessary to define a distribution of arteries and veins in tissue. For brain tissue, the ratio is given by 1:3 for arteries and veins, respectively [14]. Thus tissue oxygen saturation can be defined as StO2 = 0.3 x SaO2 + 0.7 x SvO2 ≈ 70%.

The total absorption (black) and the effective attenuation (magenta) coefficient spectra for brain tissue is shown in Figure 6 (c).

References

  1. ^ LV. Wang and HI. Wu, Biomedical Optics. Wiley. ISBN 9780471743040, 2007.
  2. ^ Molar extinction coefficients of oxy and deoxyhemoglobin compiled by Scott Prahl. URL: http://omlc.ogi.edu/spectra/hemoglobin.
  3. ^ G. M. Hale, and M. R. Querry, Optical constants of water in the 200 nm to 200 µm wavelength region, Appl. Opt., 12, 555-563, 1973.
  4. ^ Extinction coefficient of melanin by Steven Jacques. URL: http://omlc.ogi.edu/spectra/melanin/extcoeff.html.
  5. ^ R.L.P. van Veen, H.J.C.M. Sterenborg, A. Pifferi, A. Torricelli, and R. Cubeddu, OSA Annual BIOMED Topical Meeting, 2004.
  6. ^ T. Vo-Dinh, Biomedical Photonics Handbook. Taylor & Francis, Inc. ISBN 0849311160, 2002.
  7. ^ George Zonios and Aikaterini Dimou, Ioannis Bassukas, Dimitrios Galaris, and Argyrios Ysolakidis and Efthimios Kaxiras, J. Biomed. Opt., Vol.13, 014017, 2008.
  8. ^ R.L.P. van Veen, H.J.C.M. Sterenborg, A. Pifferi, A. Torricelli, and R. Cubeddu, OSA Annual BIOMED Topical Meeting, 2004.
  9. ^ S. Jacques, C. Newman, D. Levy, and A. von Eschenbach. Univ. of Texas M. D. Anderson Cancer Center, 1987.
  10. ^ LV. Wang and HI. Wu, Biomedical Optics. Wiley. ISBN 9780471743040, 2007.
  11. ^ T. Vo-Dinh, Biomedical Photonics Handbook. Taylor & Francis, Inc. ISBN 0849311160, 2002.
  12. ^ LV. Wang and HI. Wu, Biomedical Optics. Wiley. ISBN 9780471743040, 2007.
  13. ^ S. Jacques, C. Newman, D. Levy, and A. von Eschenbach. Univ. of Texas M. D. Anderson Cancer Center, 1987.
  14. ^ M. A. Mintun, M. E. Raichle, W. R. W. Martin, and P. Herscovitch, Brain oxygen utilization measured with O-15 radiotracers and positron emission tomography. J. Nucl. Med. 25, 177-187, 1984.

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