Open Access
Add to BibSonomyAbstract
We present a novel sample arm arrangement for dynamic optical coherence elastography based on excitation by a ring actuator. The actuator enables coincident excitation and imaging to be performed on a sample, facilitating in vivo operation. Sub-micrometer vibrations in the audio frequency range were coupled to samples that were imaged using optical coherence tomography. The resulting vibration amplitude and microstrain maps are presented for bilayer silicone phantoms and multiple skin sites on a human subject. Contrast based on the differing elastic properties is shown, notably between the epidermis and dermis. The results constitute the first demonstration of a practical means of performing in vivo dynamic optical coherence elastography on a human subject.
©2009 Optical Society of America
1. Introduction
Diseased and abnormal tissue can often be distinguished from healthy tissue based on its elastic properties [1,2]. For example, cancer is commonly detected by the increased stiffness of the tumor in comparison with the surrounding tissue [3]. It has been reported that the Young’s modulus (the ratio of stress to strain) of breast tumors can vary by up to a factor of 90 in comparison with healthy tissue [1]. Manual palpation is routinely performed by physicians to ‘feel’ this variation in tissue elastic properties. Although palpation is a vital tool, it is limited by its subjectivity and low resolution. Over the last 20 years, there has been interest in techniques to image the elastic properties of tissue, so-called elastography. Contrast in elastography images is related to differences in the elastic properties of tissue: large variation in elastic properties results in high contrast, whereas low contrast is observed between tissues with similar elastic properties. Early research focused on employing ultrasound [4,5] and magnetic resonance imaging (MRI) [6,7] as the underlying imaging modalities. Interest in these techniques remains high, with a number of emerging clinical applications [8,9]. For all elastography techniques, the resolution is determined by the underlying imaging modality. This limits ultrasound and MRI elastography to typical resolutions of 100 μm and 1 mm, respectively. Optical coherence tomography (OCT) has an imaging resolution in the range 1-15 μm [10,11], at least an order of magnitude better than ultrasound or MRI. Its application to elastography, denoted optical coherence elastography (OCE), is endowed with the same advantage.
Several research groups have explored OCE techniques based on speckle tracking [12–15]. A significant problem with these techniques is that the speckle pattern, which can be described as a realization of an ergodic random process, rapidly becomes decorrelated with itself upon compression of the sample [12,13]. More recently, a technique has been proposed based on the Doppler shift between consecutive A-scans [16–18]. This promising dynamic technique has been demonstrated at tissue excitation frequencies up to 20 Hz but has not, to our knowledge, been demonstrated in vivo. Tissue excitation at a much higher frequency of 20 kHz has been demonstrated in an attempt to enhance OCT image contrast [19]. Recently, our group has presented a novel OCE technique based on analysis of the frequency harmonics caused by sample excitation in the audio frequency range [20].
Generally, the aim of OCE techniques is to extract strain from measurements of tissue displacement. If the applied force is known, an estimate of Young’s modulus can then be made. Strain is defined as the ratio of the change in length to the original length of the sample section under investigation. If samples are subjected to sub-micrometer displacements, as they were in this study, the resulting measurements fall in the linear-elastic, low-strain regime. The response of tissue to such external excitation is viscoelastic, i.e., it has a viscous as well as an elastic component [21]. Viscosity is characterized by the time lag between applied force and the resultant strain. It has been reported that the viscosity of human skin decays exponentially as a function of frequency and is considerably damped for frequencies above 600 Hz [22]. In order to probe the purely elastic response of skin, in the present study samples were probed at a frequency of 820 Hz.
In the majority of OCE studies presented to date [13–20], imaging has been performed from one side of a sample, and excitation from the opposite side. Whilst generally suitable for ex vivo imaging, this experimental setup presents insurmountable problems for in vivo imaging, due to the thickness of human organs. One notable exception, based on tissue preloading and speckle tracking, was proposed by Schmitt [12]. In addition to the limitations imposed by speckle decorrelation, the large preload required a delay of at least one minute to allow internal strain dissipation prior to imaging, limiting the potential for in vivo imaging. We have recently presented in vivo experimental results based on exciting and imaging the sample from opposite sides [20]. However, an opposed geometry limits measurements to a few suitable locations, such as demonstrated for the webbing between thumb and index finger.
In this paper, we propose a novel sample arm design for OCE suitable for in vivo imaging. The design is based on a ring actuator that enables excitation and imaging to be performed from the same side of the sample. Experimental results from bilayer, room-temperature vulcanizing (RTV) silicone phantoms demonstrate an alternative source of contrast in OCE images compared with standard OCT images. Measurements of the audio-frequency elastic properties of skin in the low strain regime are presented from several locations on the hand of a human volunteer. The results illustrate the potential of this technique to distinguish between skin layers in vivo based on elastic contrast.
The paper is structured as follows: Section 2 provides details of the experimental setup and method, and discusses the design and characterization of the tissue-mimicking phantoms; Section 3 presents and discusses results obtained from both phantoms and human tissue; Section 4 reports the main conclusions of the work.
2. Materials and methods
2.1. Experimental setup
A schematic of the experimental fiber-based, time-domain OCT system utilizing balanced optical quadrature detection is presented in Fig. 1(a) . A broadband superluminescent source with a near-Gaussian spectrum centered at 1310 nm and a 3 dB bandwidth of 154 nm was employed. The output optical power from the source was 21 mW. After passing through a circulator, the light was split by a 50/50 coupler into the sample and reference arms, respectively. The reference arm consisted of a frequency-domain optical delay line (FD-ODL) that utilized a blazed grating with pitch of 400 lines/mm and a blaze angle of 13.9°. Lateral scanning across the sample was achieved using an x-scanning galvanometer mirror, as indicated in Fig. 1. The sample arm contained a triplet lens, with a focal length of 30 mm, to focus the beam through a 1.5 mm thick glass window fixed to a piezoelectric transducer (PZT) ring actuator (Piezomechanik, Germany). This lens provided a theoretical lateral resolution of 15.3 μm. At the output of the coupler, the light was split into orthogonal polarization channels using fiber-based polarization beam splitters with greater than 27 dB extinction ratio. These channels were detected using balanced InGaAs photodetectors, with a common-mode rejection ratio of 25 dB. The sensitivity of the system, measured using a calibrated glass-air interface at normal incidence, was 113 dB.
Fig. 1 (a) Schematic of the fiber-based OCT system employing balanced optical quadrature detection. The sample arm setup is shown for (b) Phantom and (c) In vivo skin measurements.
Dynamic uni-axial compressive loading was applied to the sample, as indicated in Figs. 1(b) and 1(c), by bringing the sample into contact with the ring actuator. The actuator applied dynamic compression to the sample from the same side as the illumination beam. The ring actuator had an aperture of 15 mm, a maximum stroke of 7 μm, a stiffness of 1000 N/μm and a resonance frequency of 45 kHz. An unloaded vibration amplitude of 0.4 μm and frequency of 820 Hz were chosen for these measurements. For tissue phantoms, the distal side of the phantom was in contact with a rigid plate to ensure compression was introduced. For in vivo skin tissue, the underlying bone acted as a rigid body against which the skin was compressed.
The FD-ODL was operated off-pivot to acquire conventional OCT images using a carrier frequency of 1150 Hz [23] and on-pivot when the vibration of sample scatterers generated the carrier frequency for OCE images. On-pivot operation is preferable as it leads to reduced phase noise and maximizes the vibration sensitivity, which was measured to be 50 nm.
For phantom measurements, the off-pivot configuration was used to generate co-registered OCT images. For in vivo skin experiments, a swept-source OCT (SS-OCT) system (Thorlabs, USA) was used to acquire manually co-registered OCT images.
2.2. Experimental method
The method used to extract the vibration amplitude is based on analysis of the frequency spectrum and has been presented in detail previously [20]. Briefly, the dynamic interferometric signal amplitude depends not only on the scatterer’s vibration amplitude but also on the quasi-static phase of the interferometer. This unwanted dependence is generally known as interferometric signal fading [24]. In our method, balanced quadrature detection is used to overcome its effects by incoherently mixing the quadrature channels [25]. The detected channels may be expanded into a sum of harmonics with Bessel function coefficients. The ratio of the Bessel functions can be related to the local vibration amplitude in the sample [26], allowing it to be determined at each position in an A-scan.
The data acquisition rate was limited by the requirement that the vibration signal harmonic spectra do not overlap, constraining the OCT electrical signal bandwidth to a fraction of the vibration frequency [27]. Higher vibration frequencies permit higher acquisition rates. In this study, a conservative A-scan frequency of 0.26 Hz was used, leading to an electrical bandwidth of 130 Hz.
For the measurements reported here, all samples were fixed in position. The actuator was brought into contact with the sample surface whilst observing the interference signal on an oscilloscope. Once interference fringes due to sample excitation were detected, the actuator was fixed in position and no further preload was applied. Previous results have demonstrated that this technique results in a linear-elastic response of skin layers [20].
2.3 Phantom fabrication
In order to assess the performance of our OCE system, phantoms were fabricated using RTV silicone, a viscous polyorganosiloxane resin that crosslinks to form a solid upon addition of a catalyst. The elastic properties of RTV silicone can be controlled by varying the mixing ratio of the resin and catalyst [28–30] and its elasticity range is reported to be similar to that of human breast tissue [28]. Reference measurements of the elasticity of the phantoms presented here were performed using a standard compression test (Instron, USA). Consistent with previous elastography studies, we present values for Young’s modulus in the sample regions for which strain is less than 0.1 [21,31,32]. Figure 2(a) shows standard compression test measurements of the Young’s modulus of single-layer silicone phantoms for different resin-catalyst ratios. The Young’s modulus decreased for increasing mixing ratio. Figure 2(b) plots the stress versus strain during loading of a phantom with a mixing ratio of 15:1 by volume. It can be seen that the response of the phantom is non-linear for strains above 0.1. In order to investigate the potential of our OCE system to distinguish between sample layers with different elastic properties, bilayer phantoms were fabricated. Mixing ratios of 10:1 and 30:1 by volume were chosen, which resulted in a measured ratio between the Young’s modulus in the two layers of 4.3.
Fig. 2 (a) Young’s modulus for different ratios of resin and catalyst measured using a standard compression test; (b) Stress-strain curve measured for a silicone phantom with a mixing ratio of 15:1. The inset shows the region of the curve used to measure the Young’s modulus.
To introduce optical scattering, titanium dioxide (TiO2) particles with a mean diameter of 1 μm and a refractive index of approximately 2.45 were added to the phantoms [29]. Two bilayer phantoms were fabricated with the same mixing ratios. For Phantom 1, the TiO2 particle concentrations were 3.3 mg/ml and 6.6 mg/ml, respectively. For Phantom 2, the concentration was 6.6 mg/ml in both layers. After addition of the TiO2 particles, electronic stirring was performed for 25 minutes in order to minimize particle clusters within the silicone. For both phantoms, the first layer was prepared in a mold and allowed to cure for 24 hours. The second layer was then poured onto the first layer whilst it was still in the mold and the phantom was left to cure for a further 24 hours. The surface area of both phantoms was 0.78 × 10−6 m2 and the thickness was 3 mm. The thickness of the first layer in both phantoms was approximately 250 μm. Standard compression tests confirmed that the elasticity of the phantoms was not altered by the addition of the TiO2 particles.
2.4 In vivo subject
In vivo strain measurements were taken on the hand of a 29-year-old Caucasian male. Several sites were chosen for these measurements. The hand was placed on the ring actuator sample arm and fixed in position. The arm was also supported in this position to minimize motion artifacts. The positioning, for the case of the index finger, is illustrated in Fig. 1(c).
3. Results and discussion
Figure 3 shows measured OCT and OCE images of Phantom 1. The OCT image recorded with no vibration in Fig. 3(a) clearly shows the boundary between the layers caused by the large variation in scatterer concentration. The areas of high intensity located at the boundary between layers are caused by residual clumps of scatterers remaining after electronic stirring. The corresponding OCE image (measured vibration amplitude) is presented in Fig. 3(b). The vibration amplitude and optical beam are both introduced from the top in this image. The vibration amplitude is largest close to the surface of the phantom, which is closest to the actuator, and decays as a function of depth in the material. The two layers can readily be distinguished by inspection of the vibration amplitude image, which confirms the variation in elasticity of the silicone [33]. This contrast is more clearly represented by the uni-axial dynamic longitudinal strain along the axis of the OCT beam, defined as [1,14]:
where d1 is the measured vibration amplitude at depth position z, and d2 is the amplitude at position z + Δz. Figure 3(c) shows the strain image calculated from the vibration amplitude shown in Fig. 3(b). The strain shown in grayscale is based on Δz = 60 μm in Eq. (1). The layer interface is represented by a red line in this figure. This interface was determined using a curve-fitting algorithm we now describe. If we consider an ideal layer with uniform elastic properties, the decay in vibration amplitude as a function of depth would be linear [33]. Because the strain in each layer of a sample is uniform, then within each layer, the z-component of the gradient of the vibration amplitude map should also be uniform. Beginning at the primary air-sample interface (which is easily identified), each ideal vibration-amplitude curve (versus depth), deduced from the OCT A-scans, should be continuous and the union of two line segments with different slopes. It can be parameterized by four values, the depth and vibration-amplitude magnitude of the intersection point, and the two gradients on either side. Using a least-mean-squared error approach, we fit such a four-parameter plot to each experimentally acquired depth curve. Because the fitting procedure is corrupted by noise, we then utilize the ensemble of A-scans to extract global slope parameters (strains) for each layer. They are chosen to be the median values of the appropriate fitted parameters. We then repeat the curve-fitting procedure for each A-scan, incorporating the global parameters, so that only the coordinates of the intersection point need to be determined in each case. The depth parameter gives the position of the boundary between the two regions of the phantom. A low-pass filter, applied to the boundary as a function of lateral scan position, is used to remove high-frequency noise. The latter’s position in Fig. 3(c) corresponds well with that observed in the optical contrast map of Fig. 3(a). The strain in each layer, averaged over each A-scan segment, versus lateral scanning position, is presented in Fig. 3(d). The average strain in the first layer was 1.3 με (standard deviation 1.1 με), where με signifies microstrain, and in the second layer 9.0 με (standard deviation 0.2 με). The small negative excursions in the average strain are within the experimental error and not physically meaningful.Fig. 3 . (a) TD-OCT; (b) Vibration amplitude; and (c) Strain B-scans of a bilayer phantom with different scatterer concentrations in each layer. In (c), layers are demarcated by the red curve. The scale bar represents 250 μm × 250 μm. In (d), the average strain in the first layer (red) and second layer (blue) is plotted for each A-scan.
Significant contrast is visible from Figs. 3(c) and 3(d). From the low strain in the first layer (Fig. 3(d), red line), it is apparent that this layer is displaced predominantly as a bulk medium and that the stress is dissipated mainly in the more elastic second layer (Fig. 3(d), blue line). For this reason, the contrast between the two layers is larger than would be expected from the preliminary characterization results presented in Fig. 2. This is discussed in more detail later in this section.
Figure 4 shows measured OCT and OCE images of Phantom 2. The OCT image in Fig. 4(a) confirms the expected low optical scattering contrast, given the equal concentration of scatterers in each layer. Figures 4(b) and 4(c) show the strong variation in vibration amplitude and strain, clearly depicting the difference in elastic properties of the two layers. These results demonstrate the potential of our OCE technique to provide additional contrast not visible in standard OCT imaging. The average strain in the first layer was 0.5 με (standard deviation 0.6 με) and in the second layer 8.9 με (standard deviation 0.2 με). As expected, given their identical fabrication procedures, the boundaries between layers in Phantoms 1 and 2 were located at approximately the same depth. The measured strain averaged over the A-scan segment in each layer as a function of lateral scanning position is shown in Fig. 4(d).
Fig. 4 . (a) TD-OCT; (b) Vibration amplitude; and (c) Strain B-scans of a bilayer phantom with the same scatterer concentration in each layer. In (c), layers are demarcated by the red curve. The scale bar represents 250 μm × 250 μm. In (d) the average strain introduced in the first layer (red) and second layer (blue) is plotted for each A-scan.
For the first layer (red line), there is an increase in strain of approximately 3 με across the lateral scanning range, indicating non-uniform elasticity of the first layer as a function of lateral position. It can be seen that this increase corresponds to a reduction in strain in the second layer (blue line), as less applied stress is dissipated in it. However, for each A-scan there is still clear contrast in the strain introduced in each layer. This lateral variation in elasticity is thought to be due to imperfect mixing of the two components of RTV silicone. For this reason, the strain values averaged over the whole sample are of limited utility and cannot directly be compared with the Instron test values on single layers.
Figure 5 shows in vivo OCT and OCE images of the side of the index finger of a human volunteer. In the OCT image in Fig. 5(a) , the first (most superficial) layer is the stratum corneum, consisting mainly of cornified basal keratinocytes. Below the stratum corneum are the remaining layers of the epidermis. The main constituent of these superficial cellular layers is keratinocytes, which account for 95% of all cells. The epidermis-dermis interface is located at an optical depth (product of physical depth and average refractive index) of approximately 250 μm. The dermis is composed primarily of the fibrous proteins collagen and elastin, embedded in the extrafibrillar matrix or ground substance. OCE measurements were taken from approximately the same region of the finger. The vibration amplitude and strain are presented in Figs. 5(b) and 5(c) , respectively. A clear variation in strain can be seen at an optical depth of approximately 250 μm. This corresponds well with the OCT image presented in Fig. 5(a) .
Fig. 5 . In vivo B-scans of the side of the index finger: (a) SS-OCT; (b) Vibration amplitude; and (c) Strain, with layers demarcated by the red curve. Scale bar represents 250 μm × 250 μm. In (d) the average strain in the epidermis (red) and dermis (blue) is plotted for each A-scan.
The variation in strain is believed to correspond to the epidermal-dermal junction. Epidermal thickness is defined as the stratum corneum plus underlying epidermal layers. The average strain in the epidermis was 0.26 με (standard deviation 0.38 με). The average strain in the dermis was 9 με (standard deviation 0.29 με). The average strain in the epidermis and dermis versus lateral position is presented in Fig. 5(d) . Very low strain is introduced to the stiffer, less compliant epidermis (red line), most of the stress being dissipated in the more elastic dermis (blue line). This result corresponds well with previous in vivo OCE results [12].Fig. 6 shows OCT and OCE in vivo images of the side of the index finger of a human volunteer, taken in close proximity to the knuckle. A photograph of the finger, showing a superficial laceration, is presented in Fig. 6(a) . An SS-OCT B-scan is presented in Fig. 6(b) ; the laceration appears on the right-hand side of this image. The vibration amplitude is significantly reduced in the region of the laceration, as can be seen in Fig. 6(c) and, as expected, so is the strain, presented in Fig. 6(d) . The strain images in Fig. 6(d) and Fig. 5(c) show that the detected epidermal thickness varies considerably between these two skin locations. The measured epidermal thickness in Fig. 5 is approximately 250 μm, whilst the measured epidermal thickness in Fig. 6 is approximately 100 μm. This is consistent with the SS-OCT images presented in Figs. 5(a) and 6(a) .
Fig. 6 . In vivo measurements of a laceration on the index finger; (a) Photograph; (b) SS-OCT; (c) Vibration amplitude; and (d) Strain B-scan images, with layers demarcated by the red curve. Scale bar for (b)-(d) represents 250 μm × 250 μm.
The in vivo skin measurements are similar to results obtained using an independent non-optical technique [22]. In [22], a vibration of 0.06 mm was introduced to tissue using a vibration stylus and the shear waves generated were measured a distance of 1-7 mm from the point of excitation. The results of that work suggested that surface shear waves were responsible for the measured vibration at frequencies up to 600 Hz, and at higher frequencies the energy is propagated through the underlying tissue, with minimal shearing in the epidermis. The bulk motion of the epidermis presented in Figs. 5 and 6 is consistent with this behavior, as bulk motion results in minimal shearing. This trend is confirmed by the results presented in Figs. 3 and 4. For both Phantom 1 and Phantom 2, very low strain is introduced into the less elastic first layer, which is displaced as a bulk medium. The result is that the strain contrast measured between the two layers of each phantom is larger than would be expected from standard compression testing performed on each layer separately.
Strain measurements were performed on several positions on the hand. The results are shown in Table 1 . The epidermal thicknesses presented in Table 1 (Boundary) are consistent with previous measurements from the human finger [34–36]. The measured strain in the epidermis is equal to or less than 0.73 με for all skin locations. As discussed previously, this low strain indicates that the epidermis is displaced as a bulk and that the majority of the stress is dissipated in the more elastic dermis. Dermal strain measured for similar tissues shows an inverse dependence on the distance to the underlying bone, which determines the rate of dissipation of the mechanical wave. For example, the measured strain in the skin around the knuckle was 15.8 με, whilst that around the index finger was 3.1 με, consistent with the distances to the underlying bone of approximately 1 mm for the knuckle and several millimetres for the index finger. This demonstrated dependence on underlying tissue architecture must be taken into account in order to avoid erroneous interpretation of strain measurements.
Table 1. Measured strain in the epidermis and dermis and the thickness (optical pathlength) of the combined stratum corneum and epidermis.
Conclusions
We have presented a novel sample arm design and realization based on a ring actuator for use in dynamic in vivo optical coherence elastography. The ring design enables coincident microstrain excitation and imaging, which is crucial for practical in vivo measurements. The sample arm was tested on bilayer RTV silicone tissue phantoms. The OCE images clearly distinguished layers in the phantom based on their elastic properties, including in the absence of OCT scattering contrast. The results on skin constitute the first demonstration of a practical means of performing in vivo dynamic optical coherence elastography on a human subject. These results show clear contrast between different layers of skin and that the strain introduced to skin layers varies as a function of distance to the underlying bone. Optical coherence elastography shows promise for the measurement of tissue elastic properties. The results presented here set the scene for future in vivo investigations.
Acknowledgements
We thank Dr. Steven Adie for useful discussions regarding the sample arm design. We also thank Julia Moll and Michael Lam for their assistance in fabricating the silicone phantoms.
References and links
1. J. F. Greenleaf, M. Fatemi, and M. Insana, “Selected methods for imaging elastic properties of biological tissues,” Annu. Rev. Biomed. Eng. 5(1), 57–78 ( 2003). [CrossRef] [PubMed]
2. M. Fatemi, A. Manduca, and J. F. Greenleaf, “Imaging elastic properties of biological tissues by low-frequency harmonic vibration,” Proc. IEEE 91(10), 1503–1519 ( 2003). [CrossRef]
3. L. Gao, K. J. Parker, R. M. Lerner, and S. F. Levinson, “Imaging of elastic properties of tissue--a review,” Ultrasound Med. Biol. 22(8), 959–977 ( 1996). [CrossRef] [PubMed]
4. J. Ophir, I. Cespedes, H. Ponnekanti, Y. Yazdi, and X. Li, “Elastography: a quantitative method for imaging the elasticity of biological tissues,” Ultrason. Imaging 13(2), 186–210 ( 1991). [CrossRef] [PubMed]
5. C. L. De Korte, A. F. W. Van Der Steen, E. I. Céspedes, and G. Pasterkamp, “Intravascular ultrasound elastography in human arteries: initial experience in vitro,” Ultrasound Med. Biol. 24(3), 401–408 ( 1998). [CrossRef] [PubMed]
6. R. Muthupillai, D. J. Lomas, P. J. Rossman, J. F. Greenleaf, A. Manduca, and R. L. Ehman, “Magnetic resonance elastography by direct visualization of propagating acoustic strain waves,” Science 269(5232), 1854–1857 ( 1995). [CrossRef] [PubMed]
7. A. Manduca, T. E. Oliphant, M. A. Dresner, J. L. Mahowald, S. A. Kruse, E. Amromin, J. P. Felmlee, J. F. Greenleaf, and R. L. Ehman, “Magnetic resonance elastography: non-invasive mapping of tissue elasticity,” Med. Image Anal. 5(4), 237–254 ( 2001). [CrossRef] [PubMed]
8. A. L. McKnight, J. L. Kugel, P. J. Rossman, A. Manduca, L. C. Hartmann, and R. L. Ehman, “MR elastography of breast cancer: preliminary results,” AJR Am. J. Roentgenol. 178(6), 1411–1417 ( 2002). [PubMed]
9. A. Itoh, E. Ueno, E. Tohno, H. Kamma, H. Takahashi, T. Shiina, M. Yamakawa, and T. Matsumura, “Breast disease: clinical application of US elastography for diagnosis,” Radiology 239(2), 341–350 ( 2006). [CrossRef] [PubMed]
10. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 ( 1991). [CrossRef] [PubMed]
11. W. Drexler, U. Morgner, F. X. Kärtner, C. Pitris, S. A. Boppart, X. D. Li, E. P. Ippen, and J. G. Fujimoto, “In vivo ultrahigh-resolution optical coherence tomography,” Opt. Lett. 24(17), 1221–1223 ( 1999). [CrossRef] [PubMed]
12. J. M. Schmitt, “OCT elastography: imaging microscopic deformation and strain of tissue,” Opt. Express 3(6), 199–211 ( 1998). [CrossRef] [PubMed]
13. R. C. Chan, A. H. Chau, W. C. Karl, S. Nadkarni, A. S. Khalil, N. Iftimia, M. Shishkov, G. J. Tearney, M. R. Kaazempur-Mofrad, and B. E. Bouma, “OCT-based arterial elastography: robust estimation exploiting tissue biomechanics,” Opt. Express 12(19), 4558–4572 ( 2004). [CrossRef] [PubMed]
14. J. Rogowska, N. A. Patel, J. G. Fujimoto, and M. E. Brezinski, “Optical coherence tomographic elastography technique for measuring deformation and strain of atherosclerotic tissues,” Heart 90(5), 556–562 ( 2004). [CrossRef] [PubMed]
15. H. J. Ko, W. Tan, R. Stack, and S. A. Boppart, “Optical coherence elastography of engineered and developing tissue,” Tissue Eng. 12(1), 63–73 ( 2006). [CrossRef] [PubMed]
16. R. K. Wang, Z. H. Ma, and S. J. Kirkpatrick, “Tissue Doppler optical coherence elastography for real time strain rate and strain mapping of soft tissue,” Appl. Phys. Lett. 89(14), 144103 ( 2006). [CrossRef]
17. S. J. Kirkpatrick, R. K. Wang, and D. D. Duncan, “OCT-based elastography for large and small deformations,” Opt. Express 14(24), 11585–11597 ( 2006). [CrossRef] [PubMed]
18. X. Liang, A. L. Oldenburg, V. Crecea, E. J. Chaney, and S. A. Boppart, “Optical micro-scale mapping of dynamic biomechanical tissue properties,” Opt. Express 16(15), 11052–11065 ( 2008). [CrossRef] [PubMed]
19. P. A. Edney and J. T. Walsh, “Acoustic modulation and photon-phonon scattering in optical coherence tomography,” Appl. Opt. 40(34), 6381–6388 ( 2001). [CrossRef] [PubMed]
20. S. G. Adie, B. F. Kennedy, J. J. Armstrong, S. A. Alexandrov, and D. D. Sampson, “Audio frequency in vivo optical coherence elastography,” Phys. Med. Biol. 54(10), 3129–3139 ( 2009). [CrossRef] [PubMed]
21. T. A. Krouskop, T. M. Wheeler, F. Kallel, B. S. Garra, and T. Hall, “Elastic moduli of breast and prostate tissues under compression,” Ultrason. Imaging 20(4), 260–274 ( 1998). [PubMed]
22. R. O. Potts, D. A. Chrisman Jr, and E. M. Buras Jr., “The dynamic mechanical properties of human skin in vivo,” J. Biomech. 16(6), 365–372 ( 1983). [CrossRef] [PubMed]
23. A. V. Zvyagin, E. D. J. Smith, and D. D. Sampson, “Delay and dispersion characteristics of a frequency-domain optical delay line for scanning interferometry,” J. Opt. Soc. Am. A 20(2), 333–341 ( 2003). [CrossRef]
24. E. Udd, Fibre Optic Sensors: An Introduction for Engineers and Scientists (Wiley New York, 1991).
25. Y. H. Zhao, Z. P. Chen, Z. H. Ding, H. W. Ren, and J. S. Nelson, “Real-time phase-resolved functional optical coherence tomography by use of optical Hilbert transformation,” Opt. Lett. 27(2), 98–100 ( 2002). [CrossRef] [PubMed]
26. O. Sasaki and H. Okazaki, “Sinusoidal phase modulating interferometry for surface profile measurement,” Appl. Opt. 25(18), 3137–3140 ( 1986). [CrossRef] [PubMed]
27. D. D. Sampson, and T. R. Hillman, “Optical coherence tomography,” in Lasers And Current Optical Techniques In Biology, G. Palumbo and R. Pratesi, eds. (ESP Comprehensive Series in Photosciences, Cambridge, UK, 2004) pp. 481–571.
28. S. Jiang, B. W. Pogue, T. O. McBride, M. M. Doyley, S. P. Poplack, and K. D. Paulsen, “Near-infrared breast tomography calibration with optoelastic tissue simulating phantoms,” J. Electron. Imaging 12(4), 613–620 ( 2003). [CrossRef]
29. B. W. Pogue and M. S. Patterson, “Review of tissue simulating phantoms for optical spectroscopy, imaging and dosimetry,” J. Biomed. Opt. 11(4), 041102 ( 2006). [CrossRef] [PubMed]
30. C.-E. Bisaillon, G. Lamouche, R. Macielko, M. Dufour, and J.-P. Monchalin, “Deformable and durable phantoms with controlled density of scatterers,” Phys. Med. Biol. 53(13), 237–247 ( 2008). [CrossRef]
31. E. J. Chen, J. Novakofski, W. Kenneth Jenkins, and W. D. O’Brien Jr, “Young’s modulus measurements of soft tissues with application to elastic imaging,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 43(1), 191–194 ( 1996). [CrossRef]
32. F. H. Silver, J. W. Freeman, and D. DeVore, “Viscoelastic properties of human skin and processed dermis,” Skin Res. Technol. 7(1), 18–23 ( 2001). [CrossRef] [PubMed]
33. K. J. Parker, L. S. Taylor, S. Gracewski, and D. J. Rubens, “A unified view of imaging the elastic properties of tissue,” J. Acoust. Soc. Am. 117(5), 2705–2712 ( 2005). [CrossRef] [PubMed]
34. J. T. Whitton and J. D. Everall, “The thickness of the epidermis,” Br. J. Dermatol. 89(5), 467–476 ( 1973). [CrossRef] [PubMed]
35. T. Gambichler, R. Matip, G. Moussa, P. Altmeyer, and K. Hoffmann, “In vivo data of epidermal thickness evaluated by optical coherence tomography: effects of age, gender, skin type, and anatomic site,” J. Dermatol. Sci. 44(3), 145–152 ( 2006). [CrossRef] [PubMed]
36. M. Mogensen, H. A. Morsy, L. Thrane, and G. B. E. Jemec, “Morphology and epidermal thickness of normal skin imaged by optical coherence tomography,” Dermatology 217(1), 14–20 ( 2008). [CrossRef] [PubMed]
