Ординатура / Офтальмология / Английские материалы / Handbook of Optical Coherence Tomography_Bouma, Tearney_2002

approximations such as Henyey–Greenstein or Mie theory are likely to produce acceptable results. However, if the same Monte Carlo model is used to simulate a typical endoscopic situation, in which the light delivery and collection fibers are very near to each other, the choice of phase function will significantly affect the number of photons collected. The influence of phase function on light transport for small source–detector separations was demonstrated previously by Mourant et al. [42]. The results of their simulations demonstrate the necessity of choosing a phase function that accurately reflects the probability of large-angle scatter when small source– detector separations are used. Accurate information about large-angle scattering is particularly critical in understanding the subcellular sources of OCT signals because most of the photons collected have been scattered through large angles.

We believe that a more flexible and more realistic approach such as that provided by the FDTD solution is the only way to estimate the accuracy of current Mie theory and Henyey–Greenstein approximations. In addition, we believe that there is valuable information about the interaction of light with a single cell that can be obtained from our more complicated approach. For instance, we find that the overall shape of the nucleus influences small-angle scattering whereas the effects of small intracellular organelles, or high frequency index of refraction fluctuations, are more evident at larger angles. We believe that understanding these types of trends not only develops fundamental knowledge of the interaction of light with a single cell but also serves a practical purpose in facilitating the design of more effective optical diagnostic systems, by allowing prediction of changes in scattering as a function of cell morphology.

Currently, limitations in the knowledge of cellular composition and dielectric structure make it difficult to construct a specific type of cell, simulate it, and conclude that the obtained scattering pattern is exactly what would be obtained through experimental measurements of the scattering from that kind of cell. However, the simulations presented in this chapter clearly establish that scattering patterns change significantly depending on the cell’s internal structure, the surrounding environment, and the wavelength of the illuminating light. Thus, the use of one phase function such as the Henyey–Greenstein phase function or a Mie theory approximation to represent a ‘‘typical’’ cell is probably unrealistic. Henyey–Greenstein phase functions have been shown to poorly describe large-angle scattering. This was documented by Mourant et al. [37] and is further supported by the experimental measurements presented in this work. Mie theory approximations are severely limited because they do not account for internal structures, which are expected to be the primary source of cellular scattering in in vivo measurements.

The FDTD simulations document the influence of organelle volume fraction and refractive index on cellular scattering. Both volume fraction and refractive index of cell components will be highly variable in biological cells and will depend upon cell type. Although it is possible to qualitatively predict trends in phase function or scattering cross section as refractive index and volume fraction are varied, the FDTD method provides more quantitative information. The simulations suggest that it is possible to consider that increasing the size and spatial variations of the nucleus is analogous to changing organelle volume fraction. The similarity of cells simulated with specific morphology to those with randomly generated dielectric structure suggest that it is the overall frequency and magnitude of index of refraction fluctuations, rather than a particular spatial arrangement of cell components, that has the more

crucial effect on a cell’s scattering pattern. The effect of high frequency fluctuations, which can be viewed as small scatterers, is particularly apparent at large angles. In a cell, high frequency fluctuations in refractive index might created by components such as microtubules, membranes, chromatin, or other organelle constituents.

In summary, although computationally intensive, the FDTD method places no limitations on the geometry or index structure of the objects under question. The model can be used to study the effect of changes of wavelength and of cellular biochemical and morphological structure. The data obtained from the model can be used to obtain phase functions, scattering cross section, and anisotropy. As the data demonstrate, relatively small changes in internal structure, external medium, and wavelength can impact both phase function and scattering cross section. These changes in phase function and scattering cross section significantly influence the appearance of OCT images.

16.4THE INFLUENCE OF TISSUE MICROSTRUCTURE ON OCT IMAGES

We now consider how changes in tissue microstructure affect OCT images. Most sources used in OCT have coherence lengths that limit the depth resolution to tens of micrometers. Even using high-powered, ultrafast laser sources of the shortest coherence length, the obtainable resolution is on the order of a few micrometers. With these specifications it is difficult to directly image even the largest organelle in a single (human) cell, the nucleus, which ranges between 5 and 15 m. However, the effects of subresolution particles have been experimentally observed in OCT images, in ways that are consistent with the predictions of the FDTD model presented in Section 16.3 [43–45].

Sergeev and colleagues [43] studied the effect of melanin on light scattering in tissue and the effect it had on OCT images. The FDTD model predicts increased scattering for a cell with melanin at all angles with respect to an amelanotic cell. This increase is most significant at large angles, where predicted scattering is increased by three to four orders of magnitude. The effect of this change in the scattering properties on OCT images was studied by imaging a pigmented nevus [43]. Although individual cells cannot be resolved in the OCT images obtained, they showed that by properly segmenting the image the regions of cells with melanin could be isolated. The isolated regions corresponded to areas of increased brightness in the OCT image.

The effects of increased nuclear size in epithelial cells are also evident in measured OCT images. The groups of Feldchtein [44] and Pitris [45] have shown images of cervical epithelium as it transitions from a normal region to a cancerous region. Figure 10 (see color plate) shows an image taken from Ref. 44 that illustrates this transition. At the left of the image, a layered structure is observed. The top layer, corresponding to the epithelium, shows characteristic relatively low backscatter with respect to the second connective tissue layer. To the right of the image, an invasive cancer of the uterine cervix is observed. Not only is the layered structure lost, but the backscatter of the epithelium is seen to increase with respect to the normal epithelium. This observation is consistent with the increased scattering cross section from cells with higher nuclear-to-cytoplasmic ratios.

Figure 10 Optical coherence tomographic image of the transition from normal epithelium (left) to pathological epithelium (right). (See color plate.) (Adapted from Ref. 44.)

16.5METHODS TO ENHANCE INTRINSIC CONTRAST

Scattering properties may be modulated in an attempt to enhance optical differentiation between normal and diseased tissue. In this section we consider one particularly promising approach to induce changes in scattering properties: the application of acetic acid as an optical contrast agent. Acetic acid is already in routine clinical use as a contrast agent during colposcopic examination to identify atypical areas of the cervix that require biopsy. Addition of 1–6% acetic acid causes acetowhitening of many cervical epithelial abnormalities including cervical intraepithelial neoplasia, adenocarcinoma, invasive squamous cell carcinoma, and inflammation [46]. Numerous studies have documented the use of acetic acid to improve the detection of dysplastic regions of the cervix [47,48].

Although acetic acid can augment detection of cervical dysplasia missed by Papanicolau screening [49], the mechanism through which acetic acid produces selective tissue whitening has never been fully elucidated. It has been suggested that acetic acid causes cross-linking of proteins, preventing light from passing through the epithelium. Because light does not penetrate to the underlying vessels, the epithelium appears white rather than pink. The effect is more pronounced in abnormal regions because these regions have a higher nuclear density and consequently a higher concentration of protein [46]. We hypothesize that some of the same mechanisms that cause the visual whitening effect of acetic acid may also help improve contrast in optical imaging techniques based on single backscattering such as OCT and reflected light confocal microscopy.

Smithpeter et al. [50] reported that acetic acid increases nuclear contrast in reflected light confocal images of human breast epithelial cells. Based on phase contrast microscopic experiments, we demonstrated that acetic acid induces spatial fluctuations in the nuclear refractive index. We hypothesized that these fluctuations increased backscattering from the nuclear region, resulting in clearer delineation of nuclear structure when cells were viewed using a reflected light confocal imaging system. Because both confocal microscopy and OCT rely on fluctuations of refractive index to generate image contrast, the confocal imaging results are relevant to

an occasional nucleus. The pre-acetic acid images of the abnormal biopsy show increased reflectivity of both the cell membranes and the nuclei. In addition, the cells are more crowded and irregularly spaced. Post-acetic acid images show increased signal from the nuclei in both the normal and abnormal biopsies. After confocal imaging, transverse frozen sections were cut and stained with hemotoxylin and eosin and were sent to an experienced pathologist for histological examination. Pathological diagnosis confirmed clinical expressions at the time of colposcopy.

The results show that after the addition of acetic acid, images of tissue can be obtained that illustrate characteristic differences between normal and neoplastic tissue. Without the application of acetic acid, it is difficult to distinguish the nucleus from the cytoplasm of a cell because of low contrast. This suggests that to develop diagnostic applications of confocal microscopy, enhanced visualization of the nucleus may be required to facilitate the identification of morphological changes indicative of dysplasia such as increased nuclear size and irregular shape. We have achieved similar results using acetic acid on multiple cell lines and tissue types in both in vitro and in vivo situations, so we suspect that acetic acid will be useful for improving nuclear contrast in a variety of organ sites.

Further work is needed to elucidate the underlying physical mechanisms responsible for the changes caused by acetic acid. Goniometric measurements have indicated increased scattering from cells after the addition of acetic acid [51]. Measurements of scattering cross section of cells before and after exposure to acetic acid also offer further evidence of enhanced scattering [36]. In addition, finite-dif- ference time-domain simulations of light scattering from inhomogeneous cells indicate increased scattering when cells are modeled with alterations in nuclear refractive index structure, based on phase contrast microscope images of cells before and after the addition of acetic acid [30]. Despite all of the evidence suggestive of heightened scattering due to acetic acid, the particular biochemical and morphological alterations caused by acetic acid that are responsible for the increase in scattering are not known.

Although further work is necessary to elucidate the precise mechanisms responsible for acetowhitening, it is evident that contrast agents such as acetic acid may offer a simple, inexpensive means to enhance the diagnostic utility of scattering-based imaging modalities. The encouraging preliminary results achieved using acetic acid to enhance contrast in confocal reflected light images suggest that acetic acid might be a valuable contrast agent for OCT as well.

Because the resolution of an OCT system is typically 10–20 m, whereas the resolution of a confocal microscope is closer to 1–2 m, the expected effect of acetic acid on an OCT image would be an apparent enhancement in brightness of those regions where scattering from nuclei had increased. It is important to recognize that changes in scattering, such as those induced by acetic acid, occur on a microscopic spatial scale well below the resolution of OCT imaging systems. However, these changes still impact OCT images.

It is possible to observe the enhanced backscattering induced by the addition of acetic acid in OCT images. Figures 12 and 13 (see color plates) show the effect of the exposure of normal buccal mucosa in vivo to 6% acetic acid for 3 min. Figure 12 shows the measurement site before the application of acetic acid, and Fig. 13 shows the same site after treatment with acetic acid. The figures were acquired with a fiberoptic probe–based system detailed in Ref. 52. For these measurements the system

Figure 12 In vivo OCT image of normal buccal mucosa. The intensity scale is in decibels of sample reflectivity. The figure shows a layered structure corresponding to the epithelium and connective tissue layers. (See color plate.)

had a shot noise limited sensitivity of 102 dB, with a measured axial resolution of 24 m. The dimensions of the images are 750 m (depth) by 840 m (transverse). The intensity scale is in decibels of sample reflectivity. Figure 12 illustrates the typical layered structure of the buccal mucosa. The top layer corresponds to the epithelium, which is weakly scattering. It has a uniform depth of about 150 m. Immediately below the epithelium there is a thin layer of dense connective tissue. This layer measures about 75 m and is observed as a bright band across the width of the image. Underneath the dense connective tissue lies a layer of softer connective tissue. This layer is less scattering in the image. Blood vessels, an example of which 500 m deep and 400–500 m across is shown in Fig. 12, are often observed in the soft connective tissue layer. The depth of this layer exceeds the total imaging depth of the system. Figure 13 shows the same site as Fig. 12 after the application of 6% acetic acid for a period of 3 min. Note that the intensity scales in both figures are the same. Increased backscatter is observed from the epithelial layer, making it difficult to discern from the underlying connective tissue. A faint demarcation is observed at a depth of 300 m. This increased scattering is consistent with the simulations and measurements mentioned above. Increased scattering from the epithelial layer significantly reduces the maximum imaging depth. Figure 13 shows that OCT imaging is sensitive to changes induced by exogenous agents, even when these changes are of a scale smaller than the resolution of the technique.

Figure 13 In vivo OCT image of normal buccal mucosa (same site as Fig. 12) after exposure to 6% acetic acid for 3 min. The epithelium shows enhanced scattering, consistent with FDTD modeling, goniometric measurements, and confocal imaging. (See color plate.)

16.6SUMMARY

Although it is clear that the large-scale, bulk optical parameters of a medium are important in the acquisition of OCT images, subresolution fluctuations of these parameters also affect the images significantly. Finite-difference time-domain modeling of the scattering characteristics of different tissues and tissue components provides a powerful tool with which to investigate their effect on OCT images. It also is an effective tool to investigate the changes in scattering induced by exogenous agents. The spatial resolution afforded by the technique has the added benefit of allowing the modeling of structures that closely resemble the morphological changes that occur throughout disease progression. FDTD modeling of these changes provides a bridge between subresolution morphological changes and the scattering properties of cells and tissue. The information gained from simulations of changes in tissue microstructure can then be applied to predict changes in the signal detected in OCT.

ACKNOWLEDGMENTS

The authors gratefully acknowledge financial support from NSF (BES-9872829). In addition, the contributions of Tom Collier, Benoit de Pradier, Peggy Shen, Anais

Malpica, and Michele Follen in collecting and analyzing the confocal microscopy images are gratefully acknowledged.

REFERENCES

1.Welch A, van Gemert M. Overview of optical and thermal laser-tissue interaction and nomenclature. In: A Welch, M van Gemert, eds. Optical-Thermal Response of LaserIrradiated Tissue. New York: Plenum Press, 1995.

2.Ishimaru A. Wave Propagation and Scattering in Random Media, Vol 1. San Diego, CA: Academic Press, 1978:572.

3.Prahl S, Jacques S, Welch A. A Monte Carlo model of light propagation in tissue. SPIE 5:102–111, 1989.

4.Star W. Diffusion theory of light transport. In: A. Welch, M van Gemert, eds. OpticalThermal Response of Laser-Irradiated Tissue. New York: Plenum Press, 1995.

5.Jacques S, Wang L. Monte Carlo modeling of light transport in tissue. In: A Welch, M Gemert, eds. Optical-Thermal Response of Laser-Irradiated Tissue. New York: Plenum Press, 1995:1–5.

6.Jacques S, Wang L, Hielscher A. Time resolved photon propagation in tissue. In: A Welch, M van Gemert, eds. Optical-Thermal Response of Laser-Irradiated Tissue. New York: Plenum Press, 1995.

7.Pfefer TJ, et al. A three dimensional modula adaptable grid numerical model for light propagation during laser irradiation of skin tissue. IEEE J Selected Topics Quant Electron 2:934–942, 1996.

8.Pan Y, et al. Low-coherence optical tomography in turbid issue: Theoretical analysis. Appl Opt 34:6564–6574, 1995.

9.Smithies DJ, et al. Signal attenuation and localization in optical coherence tomography studied by Monte Carlo simulation. Phys Med Biol 43:3025–3044, 1998.

10.Lindmo T, Chen Z, Nelson J, Milner T. Accuracy and noise in ODT studied by Monte Carlo simulation. Phys Med Biol 43:3045–3064, 1998.

11.Ducros MG. Design and construction of an optical coherence microscope prototype, PhD. Thesis, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 1996, p. 84.

12.Schmitt JM, et al. Optical characterization of dense tissues using low-coherence interferometry. Proc Soc Photo-Opt Instrum Eng 1889:197, 1993.

13.Schmitt JM, et al. Optical-coherence tomography of a dense tissue: Statistics of attenuation and backscattering. Phys Med Biol 39(10):1705–1720, 1994.

14.Schmitt JM, Knuttel A, Bonner RF. Measurement of optical properties of biological tissues by low-coherence reflectometry. Appl Opt 32:6032–6042, 1993.

15.Yadlowsky MJ, Schmitt JM, Bonner RF. Multiple scattering in optical coherence microscopy. Appl Opt 34(25):5699–5707, 1995.

16.Pan Y, Birngruber R, Engelhardt R. Optical coherence-gated imaging in biological tissues. Proc SPIE Int Soc Opt Eng 2678:165–171, 1996.

17.Schmitt JM, Knuttel A. Model of optical coherence tomography of heterogeneous tissue. J Opt Soc Am A. Opt Image Sci 14(6):1231–1242, 1997.

18.Knuttel A, Schork R, Bocker D. Analytical modeling of spatial resolution curves in turbid media acquired with optical coherence tomography (OCT). In: ThreeDimensional Microscopy: Image Acquisition and Processing III. Bellingham, WA: (C. Cogswell, G. Kino, and T. Wilson, eds.) Proc. SPIE 2655, p. 258–270 (1996).

19.Lutomirski RF. Atmospheric degradation of electrooptic system performance. Appl Opt 17:3915–3921, 1978.

20.Schmitt J, Kumar G. Turbulent nature of refractive index variations in biological tissue. Opt Lett 21:1310–1312, 1996.

21.Schmitt JM, Kumar G. Optical scattering properties of soft tissue: A discrete particle model. Appl Opt 37(13):2788–2797, 1998.

22.Streekstra GJ, Hoekstra AG, Nijhof EJ, Heethaar RM. Light scattering by red blood cells in ektacytometry: Fraunhofer versus anomalous diffraction. Appl Opt 32:2266– 2272, 1993.

23.Videen G, Ngo D. Light scattering multipole solution for a cell. J Biomed Opt 3:212–220, 1998.

24.Nillson A, Alsholm A, Karllson A, Andersson-Engels S. T-matrix computations of light scattering by red blood cells. Appl Opt 3:2735–2748, 1998.

25.Dunn A, Richards-Kortum R. Three-dimensional computational of light scattering from cells. IEEE J Spec Topics Quant Electron 1997:1997.

26.Beauvoit B, Kitai T, Chance B. Time-resolved spectroscopy of mitochondria, cells, and rat tissue under normal and pathological conditions. In Photon Transport in Highly Scattering Tissue (S. Aurillier, B. Chance, G. Mueller, A. Priezzhev, and V. Tuchin, eds.) Proc. SPIE 2326, 127–136. 1995.

27.Yee K. Numerical solutions of initial boundary value problems involving Maxwell’s equations in isotropic media. IEEE Trans Antennas Propagation AP-14:302–307, 1966.

28.Liao Z, Wong B, Yang B, Yuan Y. A transmitting boundary for transient wave analysis. Sci Sin Ser A 27:1063–1076, 1984.

29.Brunsting A. Differential light scattering from spherical mammalian cells. Biophysic J 14:439–453, 1974.

30.Drezek R. Finite difference time domain simulations and goniometric measurements of light scattering from cells. PhD Thesis, Electrical and Computer Engineering. Austin, TX: University of Texas at Austin, 1998.

31.Valkenburg J, Woldringh C. Phase separation between nucleoid and cytoplasm in E. coli as defined by immersive refractometry. J Bacteriol 160:1151–1157, 1984.

32.Maier J, Walker S, Fantini S, Franceschini, Gratton E. Possible correlation between blood glucose concentration and the reduced scattering coefficient of tissues in the near infrared. Opt Lett 19:2062–2064, 1994.

33.Liu H, Beauvoit B, Kimura M, Chance B. Dependence of tissue optical properties on solute-induced changes in refractive index and osmolarity. J Biomed Opt 1:200–211, 1996.

34.Vitkin I, Woolsey J, Wilson B, Anderson R. Optical and thermal characterization of natural (Sepia oficinalis) melanin. Photochem Photobiol 59:455–462, 1994.

35.Boone C, Kelloff G, Steele H. The natural history of intraepithelial neoplasia: Relevance to the search for intermediate endpoint biomarkers. Cell Biochem 16G:23–26, 1992.

36.Dunn A. Light scattering properties of cells. PhD Thesis, Electrical and Computer Engineering. Austin, TX: University of Texas at Austin, 1997.

37.Mourant J, Freyer J, Hielscher A, Eick A, Shen D, Johnson T. Mechanisms of light scattering from biological cells relevant to noninvasive optical tissue diagnostics. Appl Opt 37:3586–3593, 1998.

38.Barer R, Joseph S. Refractometry of living cells. Quart J Microsc Sci 95:399–423, 1954.

39.Yarker Y, Aspden R, Hukins D. Birefringence of articular cartilage and the distribution of collagen fibril orientations. Connect Tissue Res 11:207–213, 1983.

40.Perelman L, Backman V, Wallace M, Zonios G, Manoharan R, Nustrat A, Shields S, Seiler M, Lima C, Hamano T, Itzkan I, Van Dam J, Crawford J, Feld M. Observation of periodic fine structure in reflectance from biological tissue: A new technique for measuring nuclear size distribution. Phys Rev Lett 80:627–630, 1998.

41.van de Hulst HV. Light Scattering by Small Particles. New York: Dover, 1981.

42.Mourant J, Boyer J, Hielscher A, Bigio I. Influence of the scattering phase function on light transport measurements in turbid media performed with small source-detector separations. Opt Lett 21:546–548, 1996.

43.Sergeev A, et al. Melanin effect on light scattering in tissues: From electrodynamics of living cell to OCT imaging. In: Coherence Domain Optical Methods in Biomedical Science and Clinical Applications. V. Tuchin, H. Podbielska, and B. Ovyrn, eds. Proc. SPIE 2981, 58–63 (1997).

44.Feldchtein FI, et al. Endoscopic applications of optical coherence tomography. Opt Express 3(6):257–270, 1998.

45.Pitris C. et al. High resolution imaging of gynecologic neoplasms using optical coherence tomography. Obstet Gynecol 93(1):135–139, 1999.

46.Anderson M, Jordon J, Morse A, Sharp F. A Text and Atlas of Integrated Colposcopy. St. Louis: Mosby, 1993.

47.Ottaviano M, La Torre P. Examination of the cervix with the naked eye using the acetic acid test. Am J Obstet Gynecol 143:139–142, 1987.

48.Van Le L, Broekhuizen F, Janzer-Steele R, Behar M, Samter T. Acetic acid visualization of the cervix to detect cervical dysplasia. Obstet Gynecol 81:293–295, 1993.

49.Fiscor G, Fuller S, Jeromin J, Beyer D, Janca F. Enhancing cervical cancer detection using nucleic acid hybridization and acetic acid tests. Nurse Practitioner 15:26–30, 1990.

50.Smithpeter C, Dunn A, Drezek R, Collier T, Richards-Kortum R. Near real time confocal microscopy of in situ amelanotic cells. Sources of signal, contrast agents, and limits of contrast. J Biomed Opt 3:429–436, 1998.

51.Drezek R, Dunn A, Richards-Kortum R. Light scattering from cells: FDTD simulations and goniometric measurements. Appl Opt 38:3651–3661, 1999.

52.Zuluaga AF. Development of a cervical probe for optical coherence imaging in-vivo. PhD. Thesis, Dept Electrical and Computer Engineering. Austin, TX: Univ Texas at Austin, 1998:49.