OSA's Digital Library

Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 7, Iss. 10 — Oct. 5, 2012
« Show journal navigation

Elastography of soft materials and tissues by holographic imaging of surface acoustic waves

Karan D. Mohan and Amy L. Oldenburg  »View Author Affiliations


Optics Express, Vol. 20, Issue 17, pp. 18887-18897 (2012)
http://dx.doi.org/10.1364/OE.20.018887


View Full Text Article

Acrobat PDF (1806 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We use optical interferometry to capture coherent surface acoustic waves for elastographic imaging. An inverse method is employed to convert multi-frequency data into an elastic depth profile. Using this method, we image elastic properties over a 55 mm range with <5 mm resolution. For relevance to breast cancer detection, we employ a tissue phantom with a tumor-like inclusion. Holographic elastography is also shown to be well-behaved in ex vivo tissue, revealing the subsurface position of a bone. Because digital holography can assess waves over a wide surface area, this constitutes a flexible new platform for large volume and non-invasive elastography.

© 2012 OSA

1. Introduction

In linear elastic theory, it has been posed that knowledge of an object's surface deformation in response to mechanical stress is sufficient to uniquely solve for the internal elastic properties [1

1. G. Eskin and J. Ralston, “On the inverse boundary value problem for linear isotropic elasticity,” Inverse Probl. 18(3), 907–921 (2002). [CrossRef]

]. Solutions to this type of boundary value problem have been employed in seismology [2

2. E. R. Engdahl, R. van der Hilst, and R. Buland, “Global teleseismic earthquake relocation with improved travel times and procedures for depth determination,” Bull. Seismol. Soc. Am. 88, 722–743 (1998).

] and non-destructive testing [3

3. B. A. Auld, “General electromechanical reciprocity relations applied to the calculation of elastic wave scattering coefficients,” Wave Motion 1(1), 3–10 (1979). [CrossRef]

] to map interior elastic properties of the earth and of structural materials, respectively. In many cases, elastography is limited by the inability to collect enough surface data to compute an accurate inversion. High frame rate holography is uniquely suited for capturing nanometer amplitude vibrations over a wide area, particularly in soft materials where surface acoustic waves have millimeter wavelengths at kilohertz frequencies. Here we demonstrate high resolution elastography by holographic imaging of surface acoustic (Rayleigh) waves at multiple interrogation frequencies.

The elastic properties of soft materials, such as polymers and human tissues, are of interest for their effects on electronic device performance [4

4. C. Kim, A. Facchetti, and T. J. Marks, “Polymer gate dielectric surface viscoelasticity modulates pentacene transistor performance,” Science 318(5847), 76–80 (2007). [CrossRef] [PubMed]

], thermoplastics used in manufacturing [5

5. C. P. Buckley, C. Prisacariu, and C. Martin, “Elasticity and inelasticity of thermoplastic polyurethane elastomers: Sensitivity to chemical and physical structure,” Polymer (Guildf.) 51(14), 3213–3224 (2010). [CrossRef]

], and medical diagnostics for breast cancer [6

6. A. Samani, J. Zubovits, and D. Plewes, “Elastic moduli of normal and pathological human breast tissues: an inversion-technique-based investigation of 169 samples,” Phys. Med. Biol. 52(6), 1565–1576 (2007). [CrossRef] [PubMed]

] and hepatic fibrosis [7

7. L. Castéra, J. Vergniol, J. Foucher, B. Le Bail, E. Chanteloup, M. Haaser, M. Darriet, P. Couzigou, and V. De Lédinghen, “Prospective comparison of transient elastography, fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C,” Gastroenterology 128(2), 343–350 (2005). [CrossRef] [PubMed]

]. In breast cancer, malignant breast tissue exhibits an elastic modulus 3-10 × greater than its healthy counterpart [6

6. A. Samani, J. Zubovits, and D. Plewes, “Elastic moduli of normal and pathological human breast tissues: an inversion-technique-based investigation of 169 samples,” Phys. Med. Biol. 52(6), 1565–1576 (2007). [CrossRef] [PubMed]

], motivating the development of biomedical imaging modalities that contrast elasticity. Current methods of biomedical elastography [8

8. L. Gao, K. J. Parker, R. M. Lerner, and S. F. Levinson, “Imaging of the elastic properties of tissue--a review,” Ultrasound Med. Biol. 22(8), 959–977 (1996). [CrossRef] [PubMed]

13

13. 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]

] are based on tracking interior tissue motion during mechanical excitation, which requires (3 + 1)-D data acquisition (3 spatial + 1 temporal dimension). To reduce the dimensionality of the data acquisition, we propose an elastography method based on high frame rate holographic imaging of Rayleigh waves, a (2 + 1)-D imaging system. By computing the solution to the boundary value problem, one may in theory extract all 3 spatial dimensions of elasticity.

As known in seismology [14

14. F.-C. Lin, M. P. Moschetti, and M. H. Ritzwoller, “Surface wave tomography of the western United States from ambient seismic noise: Rayleigh and Love wave phase velocity maps,” Geophys. J. Int. 173(1), 281–298 (2008). [CrossRef]

] and non-destructive testing [15

15. C. Glorieux, W. Gao, S. E. Kruger, K. Van de Rostyne, W. Lauriks, and J. Thoen, “Surface acoustic wave depth profiling of elastically inhomogeneous materials,” J. Appl. Phys. 88(7), 4394–4400 (2000). [CrossRef]

], Rayleigh waves provide depth-resolved elastography because their sensitivity kernel (i.e., the material space over which propagation is affected by elasticity) extends in depth on a scale proportional to the wavelength, as illustrated in Fig. 1
Fig. 1 Cartoon diagram of depth-dependent out-of-plane (z) motion of Rayleigh waves as a function of wavelength (not to scale). Scanning the wavelength effectively scans the probing depth of the SAW. The amplitude of this motion is used as the sensitivity kernel in the proposed inverse method.
. In effect, lower frequency waves probe more deeply than higher frequency waves, so that the apparent wave dispersion provides depth-resolved elasticity information. The use of Rayleigh waves to quantify the elasticity of layers in soft materials has previously been performed by scanning a single detector away from a point source [16

16. T. J. Royston, H. A. Mansy, and R. H. Sandler, “Excitation and propagation of surface waves on a viscoelastic half-space with application to medical diagnosis,” J. Acoust. Soc. Am. 106(6), 3678–3686 (1999). [CrossRef] [PubMed]

, 17

17. X. Zhang and J. F. Greenleaf, “Estimation of tissue’s elasticity with surface wave speed,” J. Acoust. Soc. Am. 122(5), 2522–2525 (2007). [CrossRef] [PubMed]

]. Imaging the spatial pattern of surface waves on tissues has been accomplished by pulsed digital holography [18

18. S. Schedin, G. Pedrini, and H. J. Tiziani, “Pulsed digital holography for deformation measurements on biological tissues,” Appl. Opt. 39(16), 2853–2857 (2000). [CrossRef] [PubMed]

, 19

19. M. S. Hernández-Montes, C. Pérez-López, and F. M. Santoyo, “Finding the position of tumor inhomogeneities in a gel-like model of a human breast using 3-D pulsed digital holography,” J. Biomed. Opt. 12(2), 024027 (2007). [CrossRef] [PubMed]

] or dynamic speckle imaging [20

20. S. J. Kirkpatrick, R. K. Wang, D. D. Duncan, M. Kulesz-Martin, and K. Lee, “Imaging the mechanical stiffness of skin lesions by in vivo acousto-optical elastography,” Opt. Express 14(21), 9770–9779 (2006). [CrossRef] [PubMed]

], the latter of which was used to generate 2-D surface strain maps. We recently demonstrated imaging of the real-time propagation of Rayleigh waves with high frame rate holography [21

21. S. Li, K. D. Mohan, W. W. Sanders, and A. L. Oldenburg, “Toward soft-tissue elastography using digital holography to monitor surface acoustic waves,” J. Biomed. Opt. 16(11), 116005 (2011). [CrossRef] [PubMed]

], and showed that they qualitatively exhibit the correct dispersive behavior in layered media. It was also recently shown that high frame rate holography can visualize vibrations induced in skin by a bone conduction device [22

22. M. Leclercq, M. Karray, V. Isnard, F. Gautier, and P. Picart, “Quantitative evaluation of skin vibration induced by a bone-conduction device using holographic recording in the quasi-time-averaging regime,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2012), paper DW1C.2.

].

2. Methods

2.1 Theoretical model and inverse method

In a homogeneous, elastic half-space, Rayleigh waves exhibit a phase velocity of [23

23. I. A. Viktorov, Rayleigh and Lamb Waves: Physical Theory and Applications (Plenum Press, 1967) p. 3.

]:
cR0.87+1.12ν1+νE2ρ(1+ν),
(1)
where E is Young's modulus, ρ is mass density and ν is Poisson's ratio. For heterogeneous materials, where Young's modulus varies spatially, cR becomes frequency dependent. In the case of a material with a 1-D elasticity variation, E(z), as a function of depth, z, a common approach for determining cR is to divide the heterogeneous half-space into N homogeneous layers [15

15. C. Glorieux, W. Gao, S. E. Kruger, K. Van de Rostyne, W. Lauriks, and J. Thoen, “Surface acoustic wave depth profiling of elastically inhomogeneous materials,” J. Appl. Phys. 88(7), 4394–4400 (2000). [CrossRef]

, 24

24. N. A. Haskell, “The dispersion of surface waves on multilayered media,” Bull. Seismol. Soc. Am. 43, 17–34 (1953).

]. Wave equations are then written for each layer, along with the conditions of continuity across boundaries between the layers, a free top surface, and finiteness at infinity. This leads to a system of 4N equations, and the determinant of this system contains an implicit relationship between cR and frequency. While this approach is exact within the constraints of the model, it requires tedious numerical calculation and is not easily amenable to an inverse method for reconstructing E(z).

We propose an alternate approach that uses a Rayleigh wave sensitivity kernel equal to the depth-dependent Rayleigh wave amplitude in a homogeneous medium, specifically, the z component of particle displacement, which takes the form of a double inverse exponential [23

23. I. A. Viktorov, Rayleigh and Lamb Waves: Physical Theory and Applications (Plenum Press, 1967) p. 3.

]. We hypothesize that Rayleigh waves at a given frequency f in a medium with elastic depth profile, E(z), behave as they would in a homogeneous medium with an effective Young’s modulus, Eeff(f), given by the average of E(z) weighted by the sensitivity kernel for the effective homogeneous medium. Thus, our forward model has the form:
Eeff(f)=0k(f,z)E(z)dz0k(f,z)dz,
(2)
where the sensitivity kernel k(f,z) is given by:
k(f,z)=αexp(αzfcR(f))β2+4π22βexp(βzfcR(f)),
(3)
α and β depend only on Poisson's ratio:
α=2π1(12ν)(0.871.12ν)22(1ν)(1+ν)2
(4)
β=2π1(0.87+1.12ν)2(1+ν)2
(5)
and cR(f)/f is substituted for the Rayleigh wavelength to explicitly show the frequency dependence of the kernel.

For forward computation, Eq. (2) can then be solved iteratively by substituting Eeff(f) into Eq. (1) to obtain the dispersion curve cR(f) for a given E(z). This model shares similarities with other forward models for an effective cR in layered media [15

15. C. Glorieux, W. Gao, S. E. Kruger, K. Van de Rostyne, W. Lauriks, and J. Thoen, “Surface acoustic wave depth profiling of elastically inhomogeneous materials,” J. Appl. Phys. 88(7), 4394–4400 (2000). [CrossRef]

, 25

25. T. L. Szabo, “Obtaining subsurface profiles from surface−acoustic−wave velocity dispersion,” J. Appl. Phys. 46(4), 1448–1454 (1975). [CrossRef]

, 26

26. B. R. Tittmann, L. A. Ahlberg, J. M. Richardson, and R. B. Thompson, “Determination of physical property gradients from measured surface wave dispersion,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 34(5), 500–507 (1987). [CrossRef] [PubMed]

], but these require perturbative variations in E(z). We chose to linearize in E instead of cRE because the conditions of continuity for elastic waves are linear in E, analogous to permittivity for electromagnetic waves. As will be shown in below, our forward model can predict the dispersion curve cR(f) even in highly non-perturbative tissue phantoms.

From this model, we can now straightforwardly solve the inverse problem of determining the elastic profile E(z) from an experimentally measured dispersion curve cR(f). We rewrite Eq. (2) in a discrete vector-matrix form, Eeff = AE, with E being the elastic depth profile to be determined at each discrete depth z, and Eeff a vector representing the measured effective elasticity obtained by inversion of Eq. (1) using the cR(f) measured at multiple frequencies f. The observation matrix A is the sensitivity kernel k(f,z) integrated over each discrete depth interval and normalized. Due to the Laplace transform nature of Eq. (2), the inverse problem is highly unstable, and regularization is needed. We find that Total Variation Regularization [27

27. F. I. Karahanoglu, I. Bayram, and D. Van De Ville, “A signal processing approach to generalized 1-D total variation” IEEE T. Signal Process. 59, 5265–5274 (2011).

, 28

28. J. Dahl, P. Hansen, S. Jensen, and T. Jensen, “Algorithms and software for total variation image reconstruction via first-order methods,” Numer. Algorithms 53(1), 67–92 (2010). [CrossRef]

], which assumes a piecewise-continuous solution, works best with our tissue phantoms. That is, the vector E is obtained by minimizing:
kEEeff22+γ{TV(E)}
(6)
where TV(E)=|ΔE| is the total variation of E, and ΔE=En+1En is the difference between adjacent elements of the Young's modulus vector. γ is a parameter determining the amount of regularization applied, the optimum value of which is chosen by the L-curve method [29

29. P. C. Hansen and D. P. O'Leary, “The use of the L-curve in the regularization of discrete ill-posed problems,” SIAM J. Sci. Comput. 14(6), 1487–1503 (1993). [CrossRef]

].

2.2 Experimental setup

An in-line, image plane, digital holography system, illustrated in Fig. 2(a)
Fig. 2 (a) Digital holography system for monitoring Rayleigh waves on tissue phantoms. f1 = 40mm, f2 = 100mm, f3 = 11mm, f4 = 150mm, f5 = 200mm. (b) Raw image (interferogram) obtained by the high speed camera (SAW frequency = 96Hz, camera framerate = 3kHz (Media 1). (c) The corresponding phase reconstruction (Media 2).
, was used to image the SAWs. Briefly, the system was composed of a Mach-Zehnder interferometer and low-power, long coherence length laser. The beam from a 633 nm, 5mW Helium Neon laser (Thorlabs Inc, Newton, NJ) was passed through a 92:8 beam splitter, with 92% of the power directed along the sample path. The sample beam was then passed through a 2.5 × beam expander composed of two lenses, with the lens spacing adjusted so that the beam was slightly divergent. A downward reflecting mirror illuminated an approximately 30 mm diameter area of the sample, and the diffusely reflected light was directed through a 200 mm focal length, 2 in diameter imaging lens, forming an image onto the sensor of a high speed CMOS camera. A 1:1 magnification was used to image a 17 × 17 mm area of the sample onto the 1024 × 1024 pixel array. An aperture in front of the imaging lens was used to adjust the subjective speckle size to be within the spatial resolution of the camera. The beam along the reference arm was passed through a 14 × beam expander, and coupled through a spatial filter (a converging lens combined with a 15 μm pinhole). The reference and sample beams were combined on-axis, forming an interferogram on the camera sensor. A variable neutral density filter was used in the reference arm to adjust the intensity, and optimize the interference pattern within the dynamic range of the camera. The path lengths of the reference and sample arms were carefully adjusted to be well within the coherence length of the laser (~200 mm). As waves propagated across the phantom surface, out-of-plane (z) surface displacement induced an optical path difference producing a phase shift in the interferogram, while in-plane motion was not observed as it was small compared to the subjective speckle size (typically 30-50 μm).

A piezo-electric transducer (Kinetic Ceramics Inc, Hayward, CA) with a maximum travel range of 20 μm was placed just outside the illumination area to excite SAWs on the sample. The contact area between the transducer and sample is considered a point, as its diameter was much smaller than the SAW wavelengths observed. The waves were allowed to reach a steady state (~2-3 seconds after excitation) before the interferograms were recorded. The piezo-electric transducer was sinusoidally driven at various frequencies between 70 Hz and 300 Hz. The framerate of the camera was chosen to be at least 10 × the frequency of the SAW, at values of 1 kHz, 2 kHz or 3 kHz, corresponding to respective array sizes of 1024 × 1024, 640 × 640 and 512 × 512 pixels. This resulted in 17 × 17 mm, 10.6 × 10.6 mm and 8.5 × 8.5 mm imaged areas of the sample, respectively. A typical interferogram obtained by the system is illustrated in Fig. 2(b). Note that the fringes in this image are attributed to a cover glass plate in front of the sensor; these fringes are stationary and do not provide information about the SAWs. Instead, the interference between reference and sample beams can be observed in Media 1 as the slowly changing speckle pattern in response to the SAWs moving across the surface.

The series of images recorded by the camera was then transferred to a computer, where a phase reconstruction program was used to first bandpass filter the time-series of interferograms, and then apply a temporal phase shifting algorithm detailed in Ref. 21

21. S. Li, K. D. Mohan, W. W. Sanders, and A. L. Oldenburg, “Toward soft-tissue elastography using digital holography to monitor surface acoustic waves,” J. Biomed. Opt. 16(11), 116005 (2011). [CrossRef] [PubMed]

. For sinusoidal excitation of the surface, the reconstructed images using this algorithm represent the phase of the surface acoustic wave, modulo π, as illustrated in Fig. 2(c). Note that we do not track SAW amplitude, as only the phase velocity cR is needed for elastographic mapping. The reconstructed SAW phase maps are highly contrasted along wavefronts where the phase wraps. We measured the shortest distance between successive wavefronts, d = λ/2, and computed the speed of the SAWs according to cR = 2df. The mean and standard deviation was computed for measurements at 10 locations for each data set. The experiment was repeated at different frequencies to obtain a dispersion curve, cR(f), for each sample.

2.3 Silicone phantom preparation

To test the accuracy of the proposed inversion technique, silicone elastomer phantoms with optical and mechanical properties that mimic real tissue were prepared according to previously described methods [30

30. A. L. Oldenburg, F. J.-J. Toublan, K. S. Suslick, A. Wei, and S. A. Boppart, “Magnetomotive contrast for in vivo optical coherence tomography,” Opt. Express 13(17), 6597–6614 (2005). [CrossRef] [PubMed]

]. To perform experiments in a context of human breast imaging, we chose a Young’s modulus 4.15 kPa to mimic that of healthy adipose or fibroglandular human breast tissue, and a Young’s modulus 31.9 kPa to mimic invasive ductal carcinoma, according to mechanical measurements of fresh breast tissues reported in [6

6. A. Samani, J. Zubovits, and D. Plewes, “Elastic moduli of normal and pathological human breast tissues: an inversion-technique-based investigation of 169 samples,” Phys. Med. Biol. 52(6), 1565–1576 (2007). [CrossRef] [PubMed]

]. We will respectively refer to these Young’s moduli as “soft” and “stiff.”

First, two-layer phantoms were prepared to test the ability to extract E(z) by our inverse method. One phantom was composed of a “soft,” 15 mm layer on top of a “stiff,” 40mm layer, while a second phantom was identical to the first but with the layers switched. These were prepared in two steps. First, the 40 mm lower layer was mixed and allowed to cure in the container. The 15 mm upper layer was then poured over the cured lower layer, and the sample was then allowed to cure for another 24 hours.

Next, a phantom was prepared to simulate the mechanical properties of a tumor-like inclusion embedded inside otherwise healthy breast tissue, to test whether SAWs may be sensitive to tumors based on their mechanical properties. The inclusion was approximately cylindrical with a diameter of 5mm and height of 5mm, and embedded at a depth of ~9 mm below the surface of the phantom. This phantom was prepared in four steps. The first 40 mm, “soft” lower layer was cured in the phantom container. Simultaneously, a 35 mm diameter by 5 mm height “stiff” cylindrical sample was mixed and cured. A 5 mm height cylindrical piece was then cut from this sample to act as the tumor inclusion. This piece was then laid on top of the “soft” layer, surrounded by another 5 mm layer of “soft” mixture, and allowed to cure. Finally, the top-most “soft” layer of approximately 9 mm height was poured on top and allowed to cure. The final result was thus an almost homogeneous phantom of Young’s modulus of 4.15 kPa, with an inclusion in the center at a depth spanning from 9 to 14 mm, with Young’s modulus of 31.9 kPa. B-mode ultrasound images were then acquired of the phantom to verify the location of the inclusion before holographic imaging. The ultrasound system was a SonixTOUCH (Ultrasonix Medical Corporation, Richmond, BC, Canada).

3. Results and discussion

3.1 Elastic depth profiling of two-layer phantoms

First, we investigated the ability to extract E(z) using our model in section 2.1 by imaging SAWs on two-layer phantoms with known Young’s moduli. The experimentally obtained dispersion curves cR(f) for each two-layer phantom are illustrated in Fig. 3
Fig. 3 Dispersion curves obtained for samples with (a) stiff lower layer and (b) stiff upper layer. The corresponding inversions of the dispersion curve in (a) and (b) are shown in (c) and (d), respectively.
. As described in our earlier work [21

21. S. Li, K. D. Mohan, W. W. Sanders, and A. L. Oldenburg, “Toward soft-tissue elastography using digital holography to monitor surface acoustic waves,” J. Biomed. Opt. 16(11), 116005 (2011). [CrossRef] [PubMed]

], the slope of the dispersion curve depends on the relative stiffness change for increasing depth. From Eq. (1), it can be seen that stiffer materials (i.e. higher Young’s modulus) exhibit a higher phase velocity than softer materials. Thus, the SAW speed at higher frequencies (i.e. lower wavelength, and therefore shallower probing) tends towards what would be expected for a homogeneous phantom composed purely of the material in the upper layer. Conversely, at lower frequencies, the speed of the SAW tends towards that of a phantom composed of the material in the lower layer. Thus, for the phantom with a “soft” upper layer and “stiff” lower layer, the dispersion curve has a negative slope, while the “stiff” upper layer and “soft” lower layer phantom dispersion curve has a positive slope.

The use of phantoms with known Young’s moduli now allows us to test our inverse method for quantitatively extracting E(z). First, we compared the measured dispersion curves with those computed using our forward model (Eq. (2)) and the known distribution of E(z). While the experimental results seem to produce consistently larger values of cR, the overall shape of the dispersion curves is in good agreement for both phantoms. Next, we performed the inverse method upon the measured dispersion curves to predict E(z), the results of which are illustrated in Figs. 3(c) and 3(d) in comparison with the known E(z) according to the texture analyzer. For both phantoms, the Young’s modulus distribution obtained by inversion matches the true distribution fairly well (< 2.5 kPa on average), and the transition depth between the layers was accurately predicted within one sampling point (Δz = 2.6 mm). For the phantom with the soft upper layer, the inversion shows a transition from the upper layer to lower layer between 15.7 and 18.3 mm (compared to the true value of 15 mm). The inversion result for the phantom with stiff upper layer shows a tighter match to the true distribution, with a transition between the two layers predicted between 13.1 and 15.7 mm.

3.2 SAWs on a homogeneous phantom with a tumor-like inclusion

As can be seen, the Rayleigh wavefronts are increasingly perturbed at lower frequencies in the region containing the inclusion. The higher frequency waves and the waves not passing over the inclusion, on the other hand, remain unperturbed, appearing like waves in an elastically homogeneous medium. This is because the Rayleigh wavelengths in the background region decrease from 12 mm at 96 Hz to 5 mm at 228 Hz, so that the sensitivity kernel, which scales as the wavelength, does not extend significantly into the inclusion for the highest frequency waves. Conversely, the lower frequency waves, which have a deeper sensitivity kernel, exhibit scattering and refraction as they pass over the inclusion.

3.3 Imaging of an ex vivo tissue sample

To test the viability of this technique in real tissue, a raw chicken thigh was obtained for holographic imaging. This ex vivo tissue was chosen because of its geometry: the thigh had an ellipsoidal shape, with an approximate length of 105 mm, width of 68 mm and a depth of 40 mm, with a bone running through the middle along the length of tissue at a depth of 20 mm, measured using calipers. The bone, which is much stiffer than the surrounding tissue, is approximately a cylindrical rod of diameter 10 mm. An ultrasound image illustrating the location of the bone and corresponding digital holographic Rayleigh wave phase reconstructions at various frequencies are shown in Fig. 5
Fig. 5 Images of a raw chicken thigh with a bone through the middle. Ultrasound images are shown in (a), with the bone indicated by the dashed circle. SAW phase reconstructions of SAWs over the region containing the bone are shown in (b) for 84Hz (Media 9) (c) for 168Hz (Media 10), and (d) for 432Hz (Media 11). High frequency SAWs do not penetrate to the depth of the bone and appear more circular.
.

Similar to the results obtained with the phantom containing an inclusion, the higher frequency Rayleigh waves are more circular and regularly spaced, with little apparent scattering, suggesting that the tissue at this depth scale (λR ≈11 mm) is elastically homogeneous. The cR at 432 Hz was approximately 4.78 m/s, corresponding to an Eeff of 72 kPa in the soft tissue region above the bone. The lower frequency Rayleigh waves, on the other hand, have a deeper sensitivity kernel and are highly deformed, consistent with the presence of the much stiffer, subsurface bone. Overall the Rayleigh waves appear to be well-behaved in soft tissue, highlighting the feasibility of our approach for tissue elastography.

4. Conclusion

The results shown here demonstrate the potential for a novel elastography method using digital holography to image surface acoustic waves. In two-layer phantoms we were successfully able to reproduce the 1-D elastic depth profiles, up to a depth of 55 mm, using an ad hoc model of Rayleigh wave dispersion behavior. On average, the breast size in the U.S. population would require an imaging depth between 40 and 80 mm to reach the chest wall without pre-compression [31

31. K. Bliznakova, Z. Bliznakov, V. Bravou, Z. Kolitsi, and N. Pallikarakis, “A three-dimensional breast software phantom for mammography simulation,” Phys. Med. Biol. 48(22), 3699–3719 (2003). [CrossRef] [PubMed]

]. To obtain this depth at average breast elasticity [6

6. A. Samani, J. Zubovits, and D. Plewes, “Elastic moduli of normal and pathological human breast tissues: an inversion-technique-based investigation of 169 samples,” Phys. Med. Biol. 52(6), 1565–1576 (2007). [CrossRef] [PubMed]

] would require sampling SAWs between 12 and 25 Hz, which could be obtained in our experimental setup by more onboard memory or by a lower available frame rate than 1 kHz. Importantly, this method provides greater penetration depth than other optical elastography methods reported to date [11

11. K. Daoudi, A.-C. Boccara, and E. Bossy, “Detection and discrimination of optical absorption and shear stiffness at depth in tissue-mimicking phantoms by transient optoelastography,” Appl. Phys. Lett. 94(15), 154103 (2009). [CrossRef]

13

13. 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]

], as it is not limited by the penetration depth of light into the tissue, but rather that of the Rayleigh wave.

It was also shown that real-time monitoring of Rayleigh wave propagation provided the ability to identify the approximate transverse position and depth of a tumor-like inclusion based on multi-frequency data. Thus, while the frequency-dependent behavior of SAWs contains information about the elastic properties as a function of depth, the transverse propagation contains information about the lateral profile. In future work, the application of a more complete elastic scattering model of Rayleigh wave behavior may allow one to obtain 3-D elastograms, through a similar inverse problem approach.

In this demonstration, importantly, the SAW propagation exhibited a well-behaved profile in both phantoms and real tissues that should be amenable to quantitative analysis. The ultimate goal is for the approach to be used to study in vivo human tissue. Before this can be achieved, some experimental and theoretical issues need to be addressed. For example, the effect of tissue motions other than that due to SAWs will need to be investigated. In this work, we found that by bandpass filtering at the excitation frequency, the system became more robust to motion artifacts. Chirped (frequency-swept) SAW excitation may also enable more rapid acquisition to minimize motion artifacts. We note that the nanometer displacement sensitivity of digital holography affords measurements using low strains and strain rates (ε < 10−5 and ε˙ < 0.02 s−1, respectively), which may respectively minimize artifacts due to nonlinear and viscoelastic mechanical properties of breast tissue. Another need will be the use of a larger imaging area and multiple transducers for whole breast imaging. The holography system is easily adaptable to larger field-of-view by modifying the imaging optics, as long as sufficient resolution to observe the smallest desired Rayleigh wavelengths (typically 3-4 mm) is maintained. Furthermore, wide field imaging provides a more complete set of data that leads to high resolution elastic reconstructions.

This elastography technique constitutes a novel, non-invasive approach that takes advantage of the flexibility, nanometer-scale displacement sensitivity, and wide area imaging provided by high frame rate digital holography. Furthermore, the use of SAWs provides depth-dependent elasticity information up to several centimeters into soft materials, which is of high relevance for investigating soft tissues and for cancer detection applications.

Acknowledgments

This work was supported by a junior faculty award and by startup funds from the University of North Carolina at Chapel Hill. We thank Lara Wagner and Ava Pope at UNC-Chapel Hill, and Daniel Marks and David Brady at Duke University, for helpful discussions and technical assistance.

References and links

1.

G. Eskin and J. Ralston, “On the inverse boundary value problem for linear isotropic elasticity,” Inverse Probl. 18(3), 907–921 (2002). [CrossRef]

2.

E. R. Engdahl, R. van der Hilst, and R. Buland, “Global teleseismic earthquake relocation with improved travel times and procedures for depth determination,” Bull. Seismol. Soc. Am. 88, 722–743 (1998).

3.

B. A. Auld, “General electromechanical reciprocity relations applied to the calculation of elastic wave scattering coefficients,” Wave Motion 1(1), 3–10 (1979). [CrossRef]

4.

C. Kim, A. Facchetti, and T. J. Marks, “Polymer gate dielectric surface viscoelasticity modulates pentacene transistor performance,” Science 318(5847), 76–80 (2007). [CrossRef] [PubMed]

5.

C. P. Buckley, C. Prisacariu, and C. Martin, “Elasticity and inelasticity of thermoplastic polyurethane elastomers: Sensitivity to chemical and physical structure,” Polymer (Guildf.) 51(14), 3213–3224 (2010). [CrossRef]

6.

A. Samani, J. Zubovits, and D. Plewes, “Elastic moduli of normal and pathological human breast tissues: an inversion-technique-based investigation of 169 samples,” Phys. Med. Biol. 52(6), 1565–1576 (2007). [CrossRef] [PubMed]

7.

L. Castéra, J. Vergniol, J. Foucher, B. Le Bail, E. Chanteloup, M. Haaser, M. Darriet, P. Couzigou, and V. De Lédinghen, “Prospective comparison of transient elastography, fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C,” Gastroenterology 128(2), 343–350 (2005). [CrossRef] [PubMed]

8.

L. Gao, K. J. Parker, R. M. Lerner, and S. F. Levinson, “Imaging of the elastic properties of tissue--a review,” Ultrasound Med. Biol. 22(8), 959–977 (1996). [CrossRef] [PubMed]

9.

A. Y. Iyo, “Acoustic radiation force impulse imaging - a literature review,” J. Diagn. Med. Sonog. 25(4), 204–211 (2009). [CrossRef]

10.

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]

11.

K. Daoudi, A.-C. Boccara, and E. Bossy, “Detection and discrimination of optical absorption and shear stiffness at depth in tissue-mimicking phantoms by transient optoelastography,” Appl. Phys. Lett. 94(15), 154103 (2009). [CrossRef]

12.

B. F. Kennedy, X. Liang, S. G. Adie, D. K. Gerstmann, B. C. Quirk, S. A. Boppart, and D. D. Sampson, “In vivo three-dimensional optical coherence elastography,” Opt. Express 19(7), 6623–6634 (2011). [CrossRef] [PubMed]

13.

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]

14.

F.-C. Lin, M. P. Moschetti, and M. H. Ritzwoller, “Surface wave tomography of the western United States from ambient seismic noise: Rayleigh and Love wave phase velocity maps,” Geophys. J. Int. 173(1), 281–298 (2008). [CrossRef]

15.

C. Glorieux, W. Gao, S. E. Kruger, K. Van de Rostyne, W. Lauriks, and J. Thoen, “Surface acoustic wave depth profiling of elastically inhomogeneous materials,” J. Appl. Phys. 88(7), 4394–4400 (2000). [CrossRef]

16.

T. J. Royston, H. A. Mansy, and R. H. Sandler, “Excitation and propagation of surface waves on a viscoelastic half-space with application to medical diagnosis,” J. Acoust. Soc. Am. 106(6), 3678–3686 (1999). [CrossRef] [PubMed]

17.

X. Zhang and J. F. Greenleaf, “Estimation of tissue’s elasticity with surface wave speed,” J. Acoust. Soc. Am. 122(5), 2522–2525 (2007). [CrossRef] [PubMed]

18.

S. Schedin, G. Pedrini, and H. J. Tiziani, “Pulsed digital holography for deformation measurements on biological tissues,” Appl. Opt. 39(16), 2853–2857 (2000). [CrossRef] [PubMed]

19.

M. S. Hernández-Montes, C. Pérez-López, and F. M. Santoyo, “Finding the position of tumor inhomogeneities in a gel-like model of a human breast using 3-D pulsed digital holography,” J. Biomed. Opt. 12(2), 024027 (2007). [CrossRef] [PubMed]

20.

S. J. Kirkpatrick, R. K. Wang, D. D. Duncan, M. Kulesz-Martin, and K. Lee, “Imaging the mechanical stiffness of skin lesions by in vivo acousto-optical elastography,” Opt. Express 14(21), 9770–9779 (2006). [CrossRef] [PubMed]

21.

S. Li, K. D. Mohan, W. W. Sanders, and A. L. Oldenburg, “Toward soft-tissue elastography using digital holography to monitor surface acoustic waves,” J. Biomed. Opt. 16(11), 116005 (2011). [CrossRef] [PubMed]

22.

M. Leclercq, M. Karray, V. Isnard, F. Gautier, and P. Picart, “Quantitative evaluation of skin vibration induced by a bone-conduction device using holographic recording in the quasi-time-averaging regime,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2012), paper DW1C.2.

23.

I. A. Viktorov, Rayleigh and Lamb Waves: Physical Theory and Applications (Plenum Press, 1967) p. 3.

24.

N. A. Haskell, “The dispersion of surface waves on multilayered media,” Bull. Seismol. Soc. Am. 43, 17–34 (1953).

25.

T. L. Szabo, “Obtaining subsurface profiles from surface−acoustic−wave velocity dispersion,” J. Appl. Phys. 46(4), 1448–1454 (1975). [CrossRef]

26.

B. R. Tittmann, L. A. Ahlberg, J. M. Richardson, and R. B. Thompson, “Determination of physical property gradients from measured surface wave dispersion,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control 34(5), 500–507 (1987). [CrossRef] [PubMed]

27.

F. I. Karahanoglu, I. Bayram, and D. Van De Ville, “A signal processing approach to generalized 1-D total variation” IEEE T. Signal Process. 59, 5265–5274 (2011).

28.

J. Dahl, P. Hansen, S. Jensen, and T. Jensen, “Algorithms and software for total variation image reconstruction via first-order methods,” Numer. Algorithms 53(1), 67–92 (2010). [CrossRef]

29.

P. C. Hansen and D. P. O'Leary, “The use of the L-curve in the regularization of discrete ill-posed problems,” SIAM J. Sci. Comput. 14(6), 1487–1503 (1993). [CrossRef]

30.

A. L. Oldenburg, F. J.-J. Toublan, K. S. Suslick, A. Wei, and S. A. Boppart, “Magnetomotive contrast for in vivo optical coherence tomography,” Opt. Express 13(17), 6597–6614 (2005). [CrossRef] [PubMed]

31.

K. Bliznakova, Z. Bliznakov, V. Bravou, Z. Kolitsi, and N. Pallikarakis, “A three-dimensional breast software phantom for mammography simulation,” Phys. Med. Biol. 48(22), 3699–3719 (2003). [CrossRef] [PubMed]

OCIS Codes
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(090.1995) Holography : Digital holography

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: May 14, 2012
Revised Manuscript: July 2, 2012
Manuscript Accepted: July 10, 2012
Published: August 2, 2012

Virtual Issues
Vol. 7, Iss. 10 Virtual Journal for Biomedical Optics

Citation
Karan D. Mohan and Amy L. Oldenburg, "Elastography of soft materials and tissues by holographic imaging of surface acoustic waves," Opt. Express 20, 18887-18897 (2012)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=oe-20-17-18887


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. G. Eskin and J. Ralston, “On the inverse boundary value problem for linear isotropic elasticity,” Inverse Probl.18(3), 907–921 (2002). [CrossRef]
  2. E. R. Engdahl, R. van der Hilst, and R. Buland, “Global teleseismic earthquake relocation with improved travel times and procedures for depth determination,” Bull. Seismol. Soc. Am.88, 722–743 (1998).
  3. B. A. Auld, “General electromechanical reciprocity relations applied to the calculation of elastic wave scattering coefficients,” Wave Motion1(1), 3–10 (1979). [CrossRef]
  4. C. Kim, A. Facchetti, and T. J. Marks, “Polymer gate dielectric surface viscoelasticity modulates pentacene transistor performance,” Science318(5847), 76–80 (2007). [CrossRef] [PubMed]
  5. C. P. Buckley, C. Prisacariu, and C. Martin, “Elasticity and inelasticity of thermoplastic polyurethane elastomers: Sensitivity to chemical and physical structure,” Polymer (Guildf.)51(14), 3213–3224 (2010). [CrossRef]
  6. A. Samani, J. Zubovits, and D. Plewes, “Elastic moduli of normal and pathological human breast tissues: an inversion-technique-based investigation of 169 samples,” Phys. Med. Biol.52(6), 1565–1576 (2007). [CrossRef] [PubMed]
  7. L. Castéra, J. Vergniol, J. Foucher, B. Le Bail, E. Chanteloup, M. Haaser, M. Darriet, P. Couzigou, and V. De Lédinghen, “Prospective comparison of transient elastography, fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C,” Gastroenterology128(2), 343–350 (2005). [CrossRef] [PubMed]
  8. L. Gao, K. J. Parker, R. M. Lerner, and S. F. Levinson, “Imaging of the elastic properties of tissue--a review,” Ultrasound Med. Biol.22(8), 959–977 (1996). [CrossRef] [PubMed]
  9. A. Y. Iyo, “Acoustic radiation force impulse imaging - a literature review,” J. Diagn. Med. Sonog.25(4), 204–211 (2009). [CrossRef]
  10. 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,” Science269(5232), 1854–1857 (1995). [CrossRef] [PubMed]
  11. K. Daoudi, A.-C. Boccara, and E. Bossy, “Detection and discrimination of optical absorption and shear stiffness at depth in tissue-mimicking phantoms by transient optoelastography,” Appl. Phys. Lett.94(15), 154103 (2009). [CrossRef]
  12. B. F. Kennedy, X. Liang, S. G. Adie, D. K. Gerstmann, B. C. Quirk, S. A. Boppart, and D. D. Sampson, “In vivo three-dimensional optical coherence elastography,” Opt. Express19(7), 6623–6634 (2011). [CrossRef] [PubMed]
  13. S. J. Kirkpatrick, R. K. Wang, and D. D. Duncan, “OCT-based elastography for large and small deformations,” Opt. Express14(24), 11585–11597 (2006). [CrossRef] [PubMed]
  14. F.-C. Lin, M. P. Moschetti, and M. H. Ritzwoller, “Surface wave tomography of the western United States from ambient seismic noise: Rayleigh and Love wave phase velocity maps,” Geophys. J. Int.173(1), 281–298 (2008). [CrossRef]
  15. C. Glorieux, W. Gao, S. E. Kruger, K. Van de Rostyne, W. Lauriks, and J. Thoen, “Surface acoustic wave depth profiling of elastically inhomogeneous materials,” J. Appl. Phys.88(7), 4394–4400 (2000). [CrossRef]
  16. T. J. Royston, H. A. Mansy, and R. H. Sandler, “Excitation and propagation of surface waves on a viscoelastic half-space with application to medical diagnosis,” J. Acoust. Soc. Am.106(6), 3678–3686 (1999). [CrossRef] [PubMed]
  17. X. Zhang and J. F. Greenleaf, “Estimation of tissue’s elasticity with surface wave speed,” J. Acoust. Soc. Am.122(5), 2522–2525 (2007). [CrossRef] [PubMed]
  18. S. Schedin, G. Pedrini, and H. J. Tiziani, “Pulsed digital holography for deformation measurements on biological tissues,” Appl. Opt.39(16), 2853–2857 (2000). [CrossRef] [PubMed]
  19. M. S. Hernández-Montes, C. Pérez-López, and F. M. Santoyo, “Finding the position of tumor inhomogeneities in a gel-like model of a human breast using 3-D pulsed digital holography,” J. Biomed. Opt.12(2), 024027 (2007). [CrossRef] [PubMed]
  20. S. J. Kirkpatrick, R. K. Wang, D. D. Duncan, M. Kulesz-Martin, and K. Lee, “Imaging the mechanical stiffness of skin lesions by in vivo acousto-optical elastography,” Opt. Express14(21), 9770–9779 (2006). [CrossRef] [PubMed]
  21. S. Li, K. D. Mohan, W. W. Sanders, and A. L. Oldenburg, “Toward soft-tissue elastography using digital holography to monitor surface acoustic waves,” J. Biomed. Opt.16(11), 116005 (2011). [CrossRef] [PubMed]
  22. M. Leclercq, M. Karray, V. Isnard, F. Gautier, and P. Picart, “Quantitative evaluation of skin vibration induced by a bone-conduction device using holographic recording in the quasi-time-averaging regime,” in Digital Holography and Three-Dimensional Imaging, OSA Technical Digest (Optical Society of America, 2012), paper DW1C.2.
  23. I. A. Viktorov, Rayleigh and Lamb Waves: Physical Theory and Applications (Plenum Press, 1967) p. 3.
  24. N. A. Haskell, “The dispersion of surface waves on multilayered media,” Bull. Seismol. Soc. Am.43, 17–34 (1953).
  25. T. L. Szabo, “Obtaining subsurface profiles from surface−acoustic−wave velocity dispersion,” J. Appl. Phys.46(4), 1448–1454 (1975). [CrossRef]
  26. B. R. Tittmann, L. A. Ahlberg, J. M. Richardson, and R. B. Thompson, “Determination of physical property gradients from measured surface wave dispersion,” IEEE Trans. Ultrason. Ferroelectr. Freq. Control34(5), 500–507 (1987). [CrossRef] [PubMed]
  27. F. I. Karahanoglu, I. Bayram, and D. Van De Ville, “A signal processing approach to generalized 1-D total variation” IEEE T. Signal Process.59, 5265–5274 (2011).
  28. J. Dahl, P. Hansen, S. Jensen, and T. Jensen, “Algorithms and software for total variation image reconstruction via first-order methods,” Numer. Algorithms53(1), 67–92 (2010). [CrossRef]
  29. P. C. Hansen and D. P. O'Leary, “The use of the L-curve in the regularization of discrete ill-posed problems,” SIAM J. Sci. Comput.14(6), 1487–1503 (1993). [CrossRef]
  30. A. L. Oldenburg, F. J.-J. Toublan, K. S. Suslick, A. Wei, and S. A. Boppart, “Magnetomotive contrast for in vivo optical coherence tomography,” Opt. Express13(17), 6597–6614 (2005). [CrossRef] [PubMed]
  31. K. Bliznakova, Z. Bliznakov, V. Bravou, Z. Kolitsi, and N. Pallikarakis, “A three-dimensional breast software phantom for mammography simulation,” Phys. Med. Biol.48(22), 3699–3719 (2003). [CrossRef] [PubMed]

Cited By

Alert me when this paper is cited

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

Figures

Fig. 1 Fig. 2 Fig. 3
 
Fig. 4 Fig. 5
 

Supplementary Material


» Media 1: AVI (1923 KB)     
» Media 2: AVI (1795 KB)     
» Media 3: AVI (3220 KB)     
» Media 4: AVI (2812 KB)     
» Media 5: AVI (1802 KB)     
» Media 6: AVI (3220 KB)     
» Media 7: AVI (2812 KB)     
» Media 8: AVI (1802 KB)     
» Media 9: AVI (3220 KB)     
» Media 10: AVI (2812 KB)     
» Media 11: AVI (1802 KB)     

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited