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5.1 Introduction Recent technological advancements in the photonics industry have spurred real progress toward the development of clinical functional imaging and surgical and therapeutic systems. The development of the optical methods in modern medicine in the areas of diagnostics, surgery, and therapy has stimulated the investigation of optical properties of human tissues, since the efficacy of optical probing of the tissues depends on the photon propagation and fluence rate distribution within irradiated tissues. Examples of diagnostic use are the following: the monitoring of blood oxygenation and tissue metabolism, detection of stomach malignancies, and recently suggested various techniques for optical imaging. Therapeutic usage mostly includes applications in laser surgery and photodynamic therapy. For these applications, the knowledge of tissue optical properties is of great importance for the interpretation and quantification of the diagnostic data, and for the prediction of light distribution and absorbed energy for therapeutic and surgical use. Numerous investigations related to the determination of tissue optical properties are available in literature; however, the optical properties of many tissues have not been studied in a wide wavelength range. In this chapter we present an overview of tissue optical properties measured in a wide range of wavelength using the integrating sphere spectroscopy technique.
5.2 Basic Principles of Measurements of Tissue Optical Properties Methods for determining the optical parameters of tissues can be divided into two large groups, direct and indirect methods (Mueller and Sliney 1989, Cheong et al. 1990, Duck 1990, Welch and van Gemert 1992, Niemz 1996, Tuchin 1997, 2002, 2007, Vo-Dinh 2003). Direct methods include those based on some fundamental concepts and rules such as the Bouguer– Beer–Lambert law, the single-scattering phase function for thin samples, or the effective light penetration depth for slabs. The parameters measured are the collimated light transmission Tc and the scattering indicatrix I(θ) (angular dependence of the scattered light intensity, W/cm2 sr) for thin samples or the fluence rate distribution inside a slab. The normalized scattering indicatrix is equal to the scattering phase function I(θ)/I(0) ≡ p(θ), 1/sr. These methods are advantageous in that they use very simple analytic expressions for data processing. Their disadvantages are related to the necessity to strictly fulfill experimental conditions dictated by the selected model (single scattering in thin samples, exclusion of the effects of light polarization, and refraction at cuvette edges, etc.); in the case of slabs with multiple scattering, the recording detector (usually a fiber light guide with an isotropically scattering ball at the tip end) must be placed far from both the light source and the medium boundaries).
FIGURE 5.2 The double-integrating sphere setup. 1, the incident beam; 2,7, the entrance port; 3, the exit port; 4, the diffuse reflected radiation; 5, the tissue sample; 6, the transmitted radiation; 8, the integrating sphere.
p (θ ) 2π sin θ dθ = 1.
The value of g varies in the range from −1 to 1; g = 0 corresponds to isotropic (Rayleigh) scattering, g = 1 to total forward scattering (Mie scattering at large particles), and g = −1 to total backward scattering (Mueller and Sliney 1989, Cheong et al. 1990, Duck 1990, Welch and van Gemert 1992, Niemz 1996, Tuchin 1997, 2002, 2007, Ishimaru 1997, Mishchenko et al. 2002, Vo-Dinh 2003).
factor can be obtained from these data using an inverse method based on the radiative transfer theory. When the scattering phase function p(θ) is available from goniophotometry, g can be readily calculated. In this case, for the determination of μa and μs it is sufficient to measure Rd and Tt only. Sometimes in experiments with tissue and blood samples, a double-integrating sphere configuration is preferable, since in this case both reflectance and transmittance can be measured simultaneously and less degradation of the sample is expected during measurements (see Figure 5.2). Nevertheless, in the case of a double-integrating sphere arrangement of the experiment in addition to the singleintegrating sphere corrections of measured signals, multiple exchange of light between the spheres should be accounted for (Pickering et al. 1993b, Yaroslavsky et al. 2002a). Some tissues (e.g., melanin containing) and blood have high total attenuation coefficients in the visible and NIR spectral range. Therefore, the collimated transmittance measurement for such samples (e.g., the undiluted blood layer with a moderate thickness ≈0.1 mm) (Roggan et al. 1999a) is a technically difficult task. To solve this problem, a powerful light source combined with a sensitive detector must be used (Nilsson et al. 1997). Alternatively, it is possible to collect the collimated light together with some forwardscattered light using the third integrating sphere (Yaroslavsky et al. 1999a). In this case, the collimated transmittance is separated from the scattered flux on the stage of the data processing using, for example, a Monte Carlo (MC) technique (Yaroslavsky et al. 1996a) or a small angle approximation (Yaroslavsky et al. 1998). Another approach was used in papers by Bashkatov et al. (2007), Friebel et al. (2006), Hayakawa et al. (2001), and Meinke et al. (2007a,b). In these studies the diffuse reflectance, total and diffuse transmittance have been measured (see Figure 5.1), and IMC algorithm, taking into account geometry of the measurement, has been used for treatment of the experimental data.
where μt is determined basing on Bouguer–Beer–Lambert law (Tc = exp (−μtl)) from measured values of collimated transmittance Tc, where l is tissue sample thickness. Thus, all three parameters (μa, μs, g) can be found from the experimental data for total transmittance Tt, diffuse reflectance Rd, and collimated transmittance Tc of the sample. Often, such simple methods as the KM model (Prahl et al. 1993, Qu et al. 1994, Hammer et al. 1995, Chan et al. 1996a,b, Nemati et al. 1996, Roggan et al. 1999a,b, Sardar et al. 2001, 2004, 2005, 2007, Bashkatov et al. 2004, 2005a,b, 2006a,b, 2007, 2009, Wei et al. 2005, Friebel et al. 2006, 2009, Gebhart et al. 2006, Meinke et al. 2007a,b, Zhu et al. 2007) or diffusion approximation (Yaroslavsky et al. 1996a, 2002b, Hayakawa et al. 2004, Bargo et al. 2005, Salomatina et al. 2006) are used as the first step of the inverse algorithm for estimation of the optical properties of tissues and blood. The estimated values of the optical properties are then used to calculate the reflected and transmitted signals, employing one of the more sophisticated models of light propagation in tissue or blood. At the next step, the calculated values are compared with the measured ones. If the required accuracy is not achieved, the current optical properties are altered using one of the optimization algorithms. The procedures of altering the optical properties and calculating the reflected and transmitted signals are repeated until the calculated values match the measured values with the required accuracy.
R ηʹc i , ηcj → Rij ; T ηʹci , ηcj → Tij .
given by van de Hulst (1980) and Prahl (1988). The refractive index mismatch can be taken into account by adding effective boundary layers of zero thickness and having the reflection and transmission operators determined by Fresnel’s formulas. The total transmittance and reflectance of the slab are obtained by straightforward integration of Equation 5.4. Different methods of performing the integration and the IAD program provided by Prahl (1988, 2010) allows one to obtain the absorption and the scattering coefficients from the measured diffuse reflectance Rd and diffuse transmittance Td of the tissue slab. This program is the numerical solution to the steady-state RTE (see Equation 5.1) realizing an iterative process, which estimates the reflectance and transmittance from a set of optical parameters until the calculated reflectance and transmittance match the measured values. Values for the anisotropy factor g and the refractive index n must be provided to the program as input parameters. It was shown that using only four quadrature points, the IAD method provides optical parameters that are accurate to within 2%–3% (Prahl et al. 1993), as was mentioned earlier; higher accuracy, however, can be obtained by using more quadrature points, but it would require increased computation time. Another valuable feature of the IAD method is its validity for the study of samples with comparable absorption and scattering coefficients (Prahl et al. 1993), since other methods based on only diffusion approximation are inadequate. Furthermore, since both anisotropic phase function and Fresnel reflection at boundaries are accurately approximated, the IAD technique is well suited to optical measurements for biological tissues and blood held between two glass slides. The adding-doubling method provides accurate results in cases when the side losses are not significant, but it is less flexible than the MC technique. The IAD method has been successfully applied to determine optical parameters of blood (Sardar and Levy 1998); human and animal dermis (Chan et al. 1996b, Troy and Thennadil 2001, Bashkatov et al. 2005b); brain tissues (Gebhart et al. 2006); bronchial tissue (Qu et al. 1994); ocular tissues such as retina (Sardar et al. 2004, 2005), choroids, sclera, conjunctiva, and ciliary body (Chan et al. 1996b, Nemati et al. 1996, Bashkatov et al. 2009, 2010); mucous tissue (Bashkatov et al. 2004); subcutaneous tissue (Bashkatov et al. 2005a,b); cranial bone (Bashkatov et al. 2006b); aorta (Chan et al. 1996b); and other soft tissues in the wide range of the wavelengths (Cheong et al. 1990, Tuchin 2007).
⎧⎪π1 = 1, π2 = cos θ, ⎨ ⎪⎩τ1 = cos θ, τ2 = 3 cos 2θ.
per iteration. Two to five iterations were usually necessary to ­estimate the optical parameters with approximately 2% accuracy. The computer time required can be reduced not only by the condensed IMC method (Graaff et al. 1993b) but also by means of graphical solution of the inverse problem (Khalil et al. 2003, Pfefer et al. 2003, Thueler et al. 2003) or by means of producing a lookup table (van der Zee 1993a,b, Firbank et al. 1993, Hourdakis and Perris 1995, Kienle and Patterson 1996, Ripley et al. 1999, Ugryumova et al. 2004, Kotova et al. 2007) following a preliminary MC simulation. In the last case, a linear or spline interpolation (Press et al. 1992) between the data points can be used to improve the accuracy of the selection process. In general, in vivo μa and μʹs values for human skin proved to be significantly smaller than those obtained in vitro (about 10 and 2 times, respectively) (Graaff et al. 1993b, Laufer et al. 1998, Simpson et al. 1998, Doornbos et al. 1999, Ripley et al. 1999, Tuchin 2007, Salomatina and Yaroslavsky 2008). For μa, the discrepancy may be attributed to the low sensitivity of the double-integrating sphere, and goniometric techniques have been applied for in vitro measurements at weak absorption combined with strong scattering (μa ≪ μs) and the sample preparation methods. For μʹs , the discrepancy may be related to the strong dependence of the method on variations in the relative refractive index of scatterers and the ground medium of the tissue m, μʹs ∼ (m − 1)2 , which can be quite different for living and sampled tissue (Graaff et al. 1992, Khalil et al. 2003). The ex vivo measurements using the integrating sphere technique with corresponding IMC models, and very carefully prepared human tissue samples allow for accurate evaluation of μa and μʹs which are very close to in vivo measurements (Graaff et al. 1993a, Laufer et al. 1998, Simpson et al. 1998, Doornbos et al. 1999, Ripley et al. 1999, Roggan et al. 1999b, Salomatina and Yaroslavsky 2008).
g (λ) = A + B 1 − exp −(λ − C) D .
The experimental values of anisotropy factor g for many types of human and animal tissues are presented in Tables 5.1 through 5.11 and approximated using Equation 5.21. It should be noted that the correct prediction of light transport in tissues depends on the exact form of the phase function used for calculations (van der Zee 1993, Yaroslavsky et al. 1999b, 2002a, Thueler et al. 2003, Friebel et al. 2006, Tuchin 2007). Simulations performed with different forms of p(θ) (HGPF, Mie, and GKPF) with the same value of result in the collection of significantly different fractions of the incident photons, particularly when small numerical-aperture delivery and collection fibers (small source-detection separation) are employed (Yaroslavsky et al. 1996b, 1999b, 2002a, Bevilacqua et al. 1999). Moreover, for media with high anisotropy factors, precise measurements of the scattering phase function in the total angle range from 0° to 180° is a difficult technical task, demanding an extremely large dynamic range of measuring equipment. Most of the scattered radiation lies in the range from 0° to 30°, counting from the direction of the incident beam. In addition, measurements at angles close to 90° are strongly affected by scattering of higher orders, even for the samples of moderate optical thickness (Khlebtsov et al. 2002).
5.8 Optical Properties of Tissues Above-discussed methods and techniques were successfully applied for the estimation of optical properties of a wide number of tissues. Measurements done in vitro and ex vivo by different research groups are summarized in Tables 5.1 through 5.11. Evidently, many types of animal and human tissues may have very close optical properties, but some specificity is expected. Early published data on optical properties of both human and animal tissues are presented in the following papers: Ebert et al. (1998), Farrar et al. (1999), Wei et al. (2003, 2005), Bargo et al. (2005), Zhang et al. (2005), Gebhart et al. (2006), Qu et al. (1994), Sardar and Levy (1998), Sardar et al. (2001, 2004, 2005, 2007), Bashkatov (2002), Bashkatov et al. (2004, 2005a,b, 2006b, 2007, 2010), Friebel et al. (2006, 2009), Hammer et al. (1995), Marchesini et al. (1992), Meinke et al. (2007a,b), Ripley et al. (1999), Roggan et al. (1999a,b), Salomatina et al. (2006), Salomatina and Yaroslavsky (2008), Simpson et al. (1998), van der Zee et al. (1993b), Yaroslavsky et al. (1996b, 2002b), Chan et al. (1996a,b), Nemati et al. (1996), Spitzer and Ten Bosch (1975), Troy and Thennadil (2001), Prahl (1988), Laufer et al. (1998), Du et al. (2001), Firbank et al. (1993), Ugryumova et al. (2004), Graaff et al. (1993a), Peters et al. (1990), Ghosh et al. (2001), Schmitt and Kumar (1998), Wang (2000), Cilesiz and Welch (1993), Lin et al. (1996), Genina et al. (2005), Vargas et al. (1999), Jacques (1996), Beek et al. (1997), Germer et al. (1998), Ma et al. (2005), Maitland et al. (1993), Nilsson et al. (1995), Parsa et al. (1989), Patwardhan et al. (2005), Ritz et al. (2001), Schwarzmaier et al. (1998), Youn et al. (2000), and partially summarized in review papers, book chapters and books Cheong et al. (1990), Duck (1990), Tuchin (2002, 2007), Vo-Dinh (2003), Mobley and Vo-Dinh (2003), Roggan et al. (1995), Cheong (1995). Data presented in Tables 5.1 through 5.11 well reflect the situation in the field of tissue optical parameters measurements. It is clearly seen that the major attention was paid to skin and underlying tissues and head/brain optical properties investigations because of great importance and perspectives of optical tomography of subcutaneous tumors and optical monitoring and treatment of mental diseases. Optical properties of female breast are also well studied due perspectives to mammography. Nevertheless, in general, not many data for optical transport parameters are available in the literature. Moreover, these data are dependent on the tissue preparation technique, sample storage procedure, applied ­measuring method and inverse problem-solving algorithm, measuring instrumentation noise, and systematic errors.
Note: rms values are given in parentheses. IS, single integrating sphere; DIS, double integrating sphere.
Note: rms values are given in parentheses.
lipids of mostly presented by triglycerides. The diameters of the adipocytes are in the ranges from 15 to 250 μm and their mean diameter is varied from 50 to 120 μm. In the spaces between the cells there are blood capillaries (arterial and venous plexus), nerves, and reticular fibrils connecting each cell and providing metabolic activity of fat tissue. Absorption of the human adipose tissue is defined by the absorption of hemoglobin, lipids, and water. The main scatterers of adipose tissue are spherical droplets of lipids, which are uniformly distributed within adipocytes. The skin and subcutaneous tissue optical properties have been measured with integrating sphere technique in the visible and NIR spectral ranges (Prahl 1988, van Gemert et al. 1989, Marchesini et al. 1992, Chan et al. 1996b, Beek et al. 1997, Simpson et al. 1998, Du et al. 2001, Troy and Thennadil 2001, Bashkatov et al. 2005a,b, Ma et al. 2005, Patwardhan et al. 2005, Salomatina et al. 2006) and the in vitro and ex vivo results are presented and summarized in Table 5.1.
ophthalmologic inspection of the eye fundus is very important as a tool for diagnosis and therapy. Thus, a more fundamental knowledge of the optical properties of the ocular tissues is necessary to obtain correct applications and interpretations. The optical properties of ocular tissues have been measured with integrating sphere technique in the visible and NIR spectral ranges (Hammer et al. 1995, Chan et al. 1996b, Nemati et al. 1996, Farrar et al. 1999, Bashkatov et al. 2010) and are presented and summarized in Table 5.2.
WHO grade II and meningioma), using single-integratingsphere spectral measurements in the spectral range from 360 to 1100 nm and IMC algorithm for data processing are described by Yaroslavsky et al. (2002b) (see Table 5.3). As it follows from Table 5.3, all brain tissues under study shared qualitatively similar dependencies of the optical properties on the wavelength. The scattering coefficient decreased and the anisotropy factor increased with the wavelength, which can be explained by the lowering of the contribution of Rayleigh scattering and growing of the contribution of Mie scattering with the wavelength. The wavelength-dependent absorption coefficient behavior of all brain tissues resembled a mixture of oxy- and deoxy-hemoglobin absorption spectra. This means that in spite of careful preparation of the samples, it was not possible to remove all blood residuals from the tissue sections.
coagulation of soft brain tissues, their reduced scattering coefficient may considerably exceed that of skull bone for all tissues presented in Table 5.3. This would imply that in NIR spectroscopy on the adult head, the effect of light scattering by the skull is of the same order of magnitude as that of surrounding scalp tissue and brain. A possible reason for this is the high values of scattering anisotropy factor g due to the specific structure of bone. For example, the cortical bone consists of an underlying matrix of collagen fibers, around which calcium-bearing hydroxyapatite crystals are deposited. These crystals are the major scatterers of bone (Ugryumova et al. 2004, Bashkatov et al. 2006b); they are big sized and have a high refraction power, and therefore may be responsible for the high values of g. The head/brain tissues’ optical properties have been measured with integrating sphere technique in the visible and NIR spectral ranges (Firbank et al. 1993, Yaroslavsky et al. 2002b, Ugryumova et al. 2004, Genina et al. 2005, Bashkatov et al. 2006b, Gebhart et al. 2006) and summarized in Table 5.3.
and NIR spectral ranges (Maitland et al. 1993, Ripley et al. 1999, Bashkatov et al. 2004, 2007, Wei et al. 2005) and summarized in Table 5.4.
5.8.5 Breast Tissue Optical Properties Studies of optical breast imaging have usually focused on the clinical evaluation of specific source-detector systems. Clinically, optical imaging has been shown to be useful in distinguishing cystic from solid lesions and is particularly valuable in the diagnosis of hematoma. It has been able to detect some cancers which were not demonstrated by mammography and may, therefore, be a useful complementary procedure. However, optical method is still an experimental technique. Although it has the advantage of being a risk-free method of evaluating breast disease, current procedures are limited in their ability to detect small, deep lesions. The optimization of optical imaging requires a better understanding of the basic optical properties of breast tissues and the imaging process. The breast tissue optical properties have been measured with integrating sphere technique in the visible and NIR spectral ranges (Peters et al. 1990) and summarized in Table 5.5.
importance of the optical properties of cartilage, limited studies have been reported providing absorption and scattering coefficient data. Ebert et al. (1998) investigated optical properties of equine articular cartilage in the 300–850 nm spectral range. Although most cartilage in the body is composed of similar chemical structures such as water, collagen, and proteoglycans, these constituents are present in different proportions. Because of the varying composition, the optical properties of nasal septal cartilage are distinct from those of articular cartilage. Knowledge of optical properties of cartilage may be important for the development of noninvasive optical diagnostics to minimize nonspecific thermal damage in laser-assisted cartilage reshaping procedures. The tissue optical properties have been measured with integrating sphere technique in the visible and NIR spectral ranges (Beek et al. 1997, Ebert et al. 1998, Youn et al. 2000, Salomatina and Yaroslavsky 2008) and summarized in Table 5.6.
other treatment concepts such as laser-induced thermotherapy (LITT). Precise knowledge about the spatial distribution of induced thermal tissue damage and the temperature distribution in the specific target organ as well as its dependency on the selected application parameters is of decisive importance for the safe application of LITT. Ideally, it should already be possible to plan the required application parameters in advance so that treatment can be precisely adjusted to the individual findings. The calculation of laser-induced thermal tissue reactions is a complex task in which the computation of laser light distribution in scattering and absorbing media is of primary importance. This requires knowledge about the optical parameters (absorption, scattering, anisotropy) of the target tissue, which may considerably differ depending on the tissue structure. Especially in LITT of liver metastases, it is necessary to determine these parameters not only in the healthy liver but also in metastatic tissue. The tissue optical properties have been measured with integrating sphere technique in the visible and NIR spectral ranges (Parsa et al. 1989, Nilsson et al. 1995, Beek et al. 1997, Germer et al. 1998, Ritz et al. 2001) and summarized in Table 5.7.
5.8.8 Muscle The optical properties of muscle have been measured with integrating sphere technique in the visible and NIR spectral ranges (Nilsson et al. 1995, Beek et al. 1997, Simpson et al. 1998) and summarized in Table 5.8.
5.8.9 Aorta Aorta is a turbid tissue composed of interwoven elastin and collagen fibers, arranged in a trilayer structure of intima, media, and adventitia. Its appearance ranges from opaque white (porcine) to a pinkish-white in cadaveric samples. The tissue optical properties have been measured with integrating sphere technique in the visible and NIR spectral ranges (Cilesiz and Welch 1993, Chan et al. 1996b) and summarized in Table 5.9.
5.8.10 Lung Tissue The lung tissue optical properties have been measured with ­integrating sphere technique in the visible and NIR spectral ranges (Qu et al. 1994, Beek et al. 1997) and summarized in Table 5.10.
5.8.11 Myocardium The myocardium tissue optical properties have been measured with integrating sphere technique in the visible and NIR spectral ranges (Beek et al. 1997, Schwarzmaier et al. 1998) and ­summarized in Table 5.11.
5.9 Summary We believe that this overview of tissue optical properties will give to users a possibility to predict optical properties of tissues under their interest and evaluate light distribution in the organ under examination or treatment. Authors tried to collect as complete as possible data on tissue optical properties and presented some of these data in the form of approximation formulas as a function of the wavelength to be easy to use these data. In spite of this and availability of other reviews (Cheong et al. 1990, Duck 1990, Cheong 1995, Müller and Roggan 1995, Roggan et al. 1995, Mobley and Vo-Dinh 2003, Tuchin 2007, 2009, Altshuler and Tuchin 2009), evidently, the data collection and measurements should be continued in order to have more complete and precise information about different tissues in norm and pathology, to recognize age-related, disease-related, and treatmentrelated changes of optical properties. Laser photodynamic therapy and laser-induced interstitial thermal therapy (LITT) of deep tumors are the most promising techniques among the least invasive therapies of cancer. In this case, besides the knowledge of the optical properties of tumor tissue and the surrounding substances, the knowledge of the blood content and its optical properties is essential for therapy planning and for exact dosimetry. Data on blood optical properties can be found in the following papers, book chapters, and books: Tuchin (2007, 2009), Friebel et al. (2006, 2009), Meinke et al. (2007a,b), Roggan et al. (1994, 1995, 1999a), Yaroslavsky et al. (1996b, 1999b, 2002a), Hammer et al. (2001), Turcu et al. (2006), Mobley and Vo-Dinh (2003).
Acknowledgments The research described in this chapter has been made possible by grants: 224014 Photonics4life-FP7-ICT-2007-2; Grant RUB1-2932-SR-08 CRDF; RF Ministry of Science and Education 2.1.1/4989 and 2.2.1.1/2950, Project 1.4.09 of Federal Agency of Education of RF; RFBR-08-02-92224-NNSF_a (RF-P.R. China); RFBR-09-02-90487 Ukr_a; RFBR-10-02-90039-Bel_a; the Special Program of RF “Scientific and Pedagogical Personnel of Innovative Russia,” Governmental contracts 02.740.11.0484 and 02.740.11.0770.
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The breadth and depth of multidisciplinary knowledge in biomedical optics has been expanding continuously and exponentially, thus underscoring the lack of a single source to serve as a reference and teaching tool for scientists in related fields. Handbook of Biomedical Optics addresses this need, offering the most complete up-to-date overview of the field for researchers and students alike.
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