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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_3835_Библиотеки_им_академика_М_И_Перельмана
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Chapter 9
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Acoustic Radiation Force Optical
Coherence Elastography
Yueqiao Qu, Youmin He, Teng Ma, Qifa Zhou and Zhongping Chen
Introduction
Mechanical properties, such as the elasticity and viscosity, are often major indicators
of diseases. The stiffness of tissue changes in unison with the onset of pathology in the
cases of cardiovascular diseases, ocular diseases, and tumor formations. The cellular
composition of the tissues is altered over time, in tune with disease progression.
However, the reported stiffness of a specific type of cell or tissue differs greatly
depending on the type of imaging modality used and the experimental conditions. In
order to accurately distinguish the diseased tissues from healthy ones, it is necessary
to validate the results through both theoretical and experimental methods.
Y. Q u · Y. H e · Z. Chen (B)
Department of Biomedical Engineering, Beckman Laser Institute, University of California, Irvine,
Irvine, CA 92697, USA
e-mail: z2chen@uci.edu
Y. Q u
e-mail: rachelyqu@gmail.com
Y. H e
e-mail: youminh1@uci.edu
T. M a
Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health
Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,
Shenzhen 518055, China
e-mail: teng.ma@siat.ac.cn
Q. Zhou
Roski Eye Institute, University of Southern California, Los Angeles, CA 90033, USA
e-mail: qifazhou@usc.edu
Department of Biomedical Engineering, University of Southern California, Los Angeles, CA
90089, USA
© Springer Nature Singapore Pte Ltd. 2020
Q. Zhou and Z. Chen (eds.), Multimodality Imaging,
https://doi.org/10.1007/978-981- 10-6307-7_9
207
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Cardiovascular diseases have the highest rate of fatalities and account for 30.8% of
all deaths in the USA (Mozaffarian et al. 2016; Dariush et al. 2016). Atherosclerosis,
accounting for 41 deaths per day, is a cardiovascular condition that is associated
with changes in the composition of the blood vessel walls. During the early onset of
disease, the walls of the artery thicken due to fatty deposits, inflammation, cells, and
scar tissue build up (Ross 1999; Hansson 2005). Eventually, the lesions that form,
called plaques, are composed of distinctive necrotic cores and a fibrous cap. If the
plaque is stable with a relatively thick cap and small lipid core, there may be varying
degrees of obstruction to blood flow. However, in the case of a vulnerable plaque,
the cap, containing collagen and smooth muscle cells, becomes less than 65 µmin
thickness and can rupture easily. When there is a plaque rupture, the inflammatory
elements of the necrotic core burst into the artery and can cause blocked arterial flow,
angina, or even myocardial infarction (Virmani et al. 2003; Cheruvu et al. 2007).
Early detection of vulnerable plaques is essential to the health and safety of cardiovascular patients. The structure and composition of t he plaque are largely used
currently to determine its vulnerability. Current clinical imaging techniques include
angiography, angioscopy, ultrasound, and magnetic resonance imaging (MRI) (Amirbekian 2007; Waxman et al. 2006). Angiography allows the physician to visualize
the region of blockage, by inserting a dye into the bloodstream and observing the
mechanisms of flow (Little et al. 1988). Angioscopy helps to examine the surface of
the interior blood vessel to identify areas of damage and abnormality (Sherman et al.
1986; Takano et al. 2001). Ultrasound and MRI allow for visualization through the
depth of the blood vessel walls, at the expense of resolution and cost, respectively
(LaMuraglia et al. 1996). Due to these limitations, current imaging modalities cannot
effectively identify vulnerable plaques with high sensitivity and specificity (Amirbekian 2007; Waxman et al. 2006). Since the change in the composition of the blood
vessel wall is indicative of the early onset of atherosclerosis, it is possible to classify
vulnerability according to the composition. Plaques can be differentiated into three
different types based on their composition: lipid, fibrous, and calcified. The mechanical stiffness of these three components differs by nearly one order of magnitude
(Ebenstein et al. 2009; Inagaki et al. 2006; Baldewsing et al. 2005). Therefore, if
the stiffness of the tissue can be measured, the composition can be determined, and
vulnerable plaques can be isolated.
Mechanical testing methods have been used to observe the differences in the stiffness of lipid, fibrous, and calcified plaque components (Loree et al. 1994; Chai et al.
2014; Walsh et al. 2014). However, these tests require extraction and manipulation
of the tissue, which is not possible for in vivo imaging. It is necessary to understand
the change in tissue elasticity in vivo during the early onset and formation of plaques
in order to accurately assess the mechanical properties under the influence of natural environmental factors (Takano et al. 2001; Ebenstein et al. 2009; Inagaki et al.
2006; Baldewsing et al. 2006). The feasibility of such measurements is limited by
the resolution and accuracy of the measurement device, size of the device, and the
accessibility of the plaque in question.
Tissue elastography is a method that has been developed to map out the mechanical
properties of tissues (Schaar et al. 2003; Ophir et al. 1991). There are typically
9 Acoustic Radiation Force Optical Coherence Elastography 209
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Table 9.1 Elastography in three steps
Excitation Detection Parameter estimation
Internal External Mechanical test
Static Dynamic
Magnetic resonance
Ultrasound
Optical
Quantitative
Qualitative
three steps involved as depicted in Table 9.1: excitation, detection, and parameter
estimation (Sun et al. 2011; Manduca et al. 2001; Greenleaf et al. 2003). The tissue
is first excited using an internal or external mechanism, where the tissue itself or
an outside force causes deformation (Greenleaf et al. 2003). The force can be either
static or dynamic in nature, depending on the variable to be measured. For example,
a few external methods include piezoelectric elements, air puff devices, and acoustic
radiation force (ARF) using ultrasound. All of these devices operate by giving a
static, continuing force to analyze a stable deformation state, or by providing a single
or modulated dynamic signal of pulses to analyze the change in deformation over
time. Once the tissue is deformed, a technique is used to visualize and measure the
amount of deformation. Traditionally, mechanical testing using pressure sensors was
implemented to obtain data in ex vivo samples. Magnetic resonance and ultrasound
methods have also been used to detect tissue deformation at the expense of high cost
and low resolution, respectively. In recent years, optical imaging methods, such as
optical coherence elastography, have been developed to detect tissue response (Khalil
et al. 2005; Wang et al. 2006, 2007;Qietal.2012, 2013, 2014; Zhu et al. 2015;Qu
et al. 2016, 2018;Heetal.2019; Kennedy et al. 2015; Liang et al. 2010; Manapuram
et al. 2012; Rogowska et al. 2004; van Soest et al. 2007; Wang and Larin 2015). In
particular, phase resolved Doppler optical coherence tomography (OCT) has been
widely used for detection (Chen et al. 1997a, b; Zhao et al. 2000a, b), with its main
advantages being its high resolution and high displacement sensitivity.
Most parameter estimation methods target the extraction of elasticity by the means
of elastograms or elasticity maps (Khalil et al. 2005; Wang et al. 2006, 2007;Qietal.
2012, 2014;Quetal.2016, 2018; Kennedy et al. 2015; Liang et al. 2010; Manapuram
et al. 2012; Rogowska et al. 2004; van Soest et al. 2007; Wang and Larin 2015;He
et al. 2019). Research has also been done to observe other mechanical properties,
such as the viscosity (Sinkus et al. 2005a,
b; Catheline et al. 2004). The parameter
estimation can be either quantitative or qualitative. Qualitative methods allow users
to obtain relative values for mechanical properties and can be beneficial for the
comparison between healthy and diseased tissues. However, there are often problems
with the calibration accuracy of the system as well as environmental and systematic
changes between measurements that limit the functions of qualitative data. Due to
these factors, quantitative measurements with strong theoretical evidence are always
preferred. A few examples of quantification include shear wave velocity calculations,
strain imaging, and tissue frequency response (Nightingale et al. 2003; Evans et al.
2010; O’Donnell et al. 1994;Qietal.2012, 2013, 2014; Ahmad et al. 2015; Liang
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et al. 2008;Quetal.2018;Heetal.2019). Select methods will be discussed in detail
in the next section.
Intravascular elastography using ultrasound has been widely studied in the past
20 years (Takano et al. 2001; de Korte et al. 1998, 2000; Baldewsing et al. 2004a).
In general, a pressure is applied to the artery, and ultrasound imaging is used for
the detection of tissue displacement, which is then converted to strain measurements
and an elastogram can be generated. Examples of ultrasound elastography techniques
include compression strain imaging and phase-sensitive speckle tracking methods
based on cross-correlation analyses (de Korte et al. 2000). In vivo intravascular
ultrasound elastography studies have also taken place in the past years, along with
modeling methods such as finite element analysis (de Korte et al. 2002; Baldewsing
et al. 2004b, c). However, these methods are often limited by the low ultrasound
resolution of typically 150–300 µm, which allows for the detection of homogenous
plaque types, but are limited in the observation of heterogeneity within small regions,
which is the case for most human plaques (Prati et al. 2001). In addition, most vulnerable plaques are characterized by thin fibrous caps, as little as 65 µm in thickness,
which cannot be accurately measured using ultrasound (Virmani et al. 2003). Using
optical methods, with micron-level resolution, it is possible to detect minute changes
in tissue elasticity within a small region. Finally, due to the nanometer sensitivity of
phase-resolved OCT, only small forces are necessary to induce vibrations, which is
critical in in vivo clinical applications.
Compressional and Shear Wave Methods Using OCE
Optical coherence elastography (OCE) is a technology that uses the principles of optical coherence tomography (OCT) to detect the tissue response to excitation (Huang
et al. 1991; Fujimoto 2001). OCT is based on the interference of backscattered light
signals of the sample and a reference mirror. In regard to OCE, an excitation force,
most often external, is applied to the tissue, while the optical interference information
is extracted (Sun et al. 2011; Kennedy et al. 2015; Liang et al. 2010; Manapuram
et al. 2012; Rogowska et al. 2004; van Soest et al. 2007; Wang and Larin 2015;Qi
et al. 2012, 2014;Quetal.2016). In summary, the A-line interference signal, I (z),
can be summarized using Eq. 9.1, where its magnitude and phase, denoted by φ(z) at
a certain depth z, can be separated. The information provided can be used to measure
the tissue response by using certain parameters such as done in the phase-resolved
method and Doppler variance methods (Zhao et al. 2000a, b).
I
Interference
(z) =|I
Interference
There are primarily two types of tissue responses, which rely on elastic wave
properties, that are studied using OCE: the p-wave and the s-wave (Catheline et al.
1999). When a force is exerted on a sample, the first response consists of the p-wave,
(z)|e
i φ(z)
(9.1)
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also known as the compressional wave, traveling across the sample parallel to the
direction of the force. The p-wave travels at a high speed and essentially compresses
the sample as it passes. The s -wave, also referred to as the secondary or shear wave,
travels perpendicularly to the direction of the initial force and is approximately three
orders of magnitude slower than the p-wave (Gennisson et al. 2005). The s-wave is
directly related to the shear modulus. We will now introduce two different methods
of parameter estimation: (1) Doppler OCE using the p-wave measurements to obtain
the elastic modulus and (2) velocity extraction using s-waves to obtain the shear
modulus.
Doppler OCE
With the extracted phase information shown in Eq. 9.1, the phase shift between 2
A-lines can be calculated. The Doppler frequency shift, f
proportional to the axial velocity denoted by v
cos θ and the measured phase shift,
r
φ(z),asshowninEq.9.2 (Chen et al. 1997a, b; Zhao et al. 2000a, b):
n cos θ
2v
r
=
f
D
λ
0
φ(x, z, t
=
2πt
, is by definition directly
D
)
(9.2)
The variable n refers to the refractive index of the sample, λ
represents the
0
central wavelength of the light source, and t is the period between the A-lines. By
rearranging Eq. 9.2, the Doppler velocity can be defined as a function of the phase
shift between A-lines. The displacement of the sample can be obtained by integrating
the velocity over time as done in Eq. 9.3:
d =
t
2
t
1
vrdt =
t
2
φ(x, z, t)λ
4πnt cos θ
t
1
0
dt (9.3)
In order to calculate the mechanical elasticity, it is necessary to associate the
displacement from the Doppler relationship to the elastic modulus. By definition,
the strain, ε, is linearly proportional to the displacement and inversely proportional
to the change in sample thickness or the compression of the sample in the axial
direction, denoted by z,asshowninEq.9.4:
d
ε =
z
(9.4)
Young’s modulus, Y , is linearly proportional to the stress, σ , and inversely pro-
portional to the strain, ε.InEq.9.5 below, the stress can be written as the force per
area, while the strain is defined as in Eq. 9.4.
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FA
Y =
dz
In compressional OCE, an elastogram is generated based on the inverse relationship between the displacement and Young’s modulus. For excitation methods such
as ARF or air puff, it is difficult to quantify the force applied per area. Although we
can calculate ARF in a well-defined geometry, it is difficult to get the precise value
of ARF for in vivo applications where the distance between the transducer and tissue
changes. In other words, the stress is difficult to be quantified, so a qualitative map
is produced. For qualitative imaging purposes, the elasticity of healthy tissue and
plaque areas can be differentiated by the differences in displacement values, with a
high displacement corresponding to softer tissue. Since the difference in elasticity
is often at least one order of magnitude, qualitative information is helpful in disease
diagnosis.
An example of an experiment done using compressional OCE is shown in Fig. 9.1
(Qi et al. 2012). The excitation mechanism was ARF. Since the resonance frequency
of phantom is within 300 Hz, a pulsed excitation was used with a modulation
of 500 Hz to avoid the effects of resonance. The force was applied to a side-byside agarose phantom with Young’s moduli of 83.6 kPa on the right-hand side and
265.7 kPa on the left side. Figure 9.1a shows the structural OCT image, where the
boundary between the two different phantoms cannot be identified. Using Eq. 9.1 to
isolate the phase shift, an elastogram is generated in Fig. 9.1b. It is evident that the
500 Hz modulation can be clearly observed, and the right side had a much higher
phase response than the left, as shown in the amplitude plot in 1c. The measured
response ratio between the left to the right side is 1:3.05. Figure 9.1d–f shows the
3D reconstruction of the same data. Since phase and displacement are proportional,
it can be concluded that the right-hand side is approximately three times softer than
its counterpart.
Although the compressional OCE method allows users to approximate the ratio
between sample compositions, it is unable to directly offer quantitative elasticity.
This is problematic when comparisons and diagnoses must be made between two
different images or between different time points. Due to changes in experimental
conditions and noise within the system, the displacement map cannot be effectively
used to make conclusions between different samples and at different acquisition
times. Because the ARF on the sample cannot be accurately measured for in vivo
application, the absolute Young’s modulus cannot be extracted. This leads to methods
of quantification, which will be discussed in the next section.
σ
=
ε
(9.5)
Shear Velocity Estimation
The shear wave is the second parameter that has been widely studied in OCE techniques (Fujimoto 2001; Catheline et al. 1999; Gennisson et al. 2005). Since it travels
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Fig. 9.1 Side-by-side agarose phantom results using compressional OCE method. a OCT intensity
image. b OCE phase image. c OCE amplitude plot of data in the red box. d 3D OCT reconstruction.
e 3D OCE reconstruction. f Fused 3D OCT and OCE images. Scale bar: 500 µm (Qi et al. 2012)
much slower than compressional waves, it is possible to analyze its speed of motion
in different mediums. We can approximate the shear wave speed, v
Voigt model for a homogeneous medium consisting of a single spring and damper.
In Eq. 9.6 below, μ represents the shear modulus, ω is the shear wave angular frequency, η is the shear viscosity, and ρ is the tissue density. ω can also be defined to
be twice the shear wave frequency (Razani et al. 2012).
During OCE experiments, it is possible to use the phase maps to calculate the displacement of the shear wave at different locations. In order to calculate the mechanical
(ω),usinga
s
2μ2+ ω2η
=
v
(ω)
s
ρμ +
μ
2
+ ω2η
2
2
(9.6)
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elasticity, we can first relate the shear modulus to the shear wave velocity information. Intuitively, the stiffer the material, the faster the shear wave propagation. In
addition, the tissue density must also be considered. This relationship is shown in
Eq. 9.7.
μ = ρC
2
s
(9.7)
Assuming that the tissue in question is incompressible, the Young’s modulus,
which is the direct measure of elasticity, is approximately three times the value of
the shear modulus.
E ≈ 3μ (9.8)
Using the above model, it is possible to obtain the elasticity map directly from
the shear wave velocity. Both phase resolved Doppler and Doppler variance measurements can be used to detect the propagation of the shear wave (Zhu et al. 2015;
Xu et al. 2016; Zhang et al. 2009; Razani et al. 2012; Chen et al. 2004; Yamakoshi
et al. 1990;Quetal.2018;Heetal.2019).
In the following experiment, ARF was used as the method of excitation while
a swept-source OCT system was used for the detection of shear wave (Zhu et al.
2015). The sample was an ex vivo rabbit cornea. Excitation ARF was applied in a
diagonal direction to the cornea while the detection occurred from the top. There
was a shear wave that propagated from the middle of the cornea to the two sides.
The results are shown in Fig. 9.2. Figure 9.2a shows the B-mode OCT image, while
Fig. 9.2b represents t he propagation of the shear wave over time at each X location.
The slope of the curve in Fig. 9.2b represents the distance over time or the velocity
of the s hear wave propagation. Figure 9.2c shows the raw data of the shear wave
location at different sampling times, where the shear wave moves from the center of
the cornea to the outer boundaries continuously. In order to gather all the necessary
parameters, M-mode imaging was performed at each x location.
Two limitations of the above setup include the speed of the imaging device and the
effects of tissue boundary conditions. The sampling rate must be faster than the shear
wave propagation speed according to the Nyquist theory. For stiffer tissues, such as
calcified atherosclerotic plaques, the wave propagation is much faster, and tracing
the wave with a faster light source is necessary. The location of the excitation makes
a difference in the boundary conditions, which is also determined by the geometry
of the structure. There may be interference between different waves that are induced
and also with the interfaces of the tissue l ayers. The alignment of the excitation and
detection is also crucial in generating shear waves in the intended direction with
minimal boundary influences. Several studies have co-aligned the excitation and
detection so that it can be adapted to in vivo tissue imaging (Nguyen et al. 2014; Wang
and Larin 2014). In addition, modeling and simulations may be necessary to study
the wave dynamics in different tissues. In the case of intravascular and cardiovascular
imaging with live pathological tissue, heartbeat and breathing motions may have a
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Fig. 9.2 Ex vivo rabbit cornea imaging. a OCT B-mode scan. b Spatial-temporal map of shear
wave propagation. c Raw spatial data of shear wave propagation over time
large effect on data acquisition, and the mechanical structure may prove to be much
more complex.
However, since only a single-excitation pulse is necessary to scan the entire field
of view, shear wave OCE is suitable for cardiovascular applications. The pulse power
is much lower than that of compressional OCE and can be kept within the federal
safety limit for the Mechanical Index (MI). Also, the imaging time required is much
shorter when detecting a single pulse, so large area intravascular acquisition can be
performed to identify the pathological vessels within a long stretch. The successful
translation of shear wave OCE imaging to in vivo studies and clinical trials would
have the potential to make a great leap in the diagnosis of cardiovascular diseases.
Quantitative ARF-OCE Using Compressional Wave
The methodology of the compressional wave ARF-OCE has been outlined in the
previous section. The feasibility of this method for vascular imaging will be examined. The schematic diagram of an ARF-OCE system is shown in Fig. 9.3a (Qi et al.
2013). A 4 MHz ultrasound transducer was used for excitation, driven by a function
generator, and a radiofrequency amplifier. Individual pulses were given to the sample with a 50% duty cycle, 60 V excitation voltage, and 500 Hz. The OCT detection
of the tissue response occurs on the opposite side of the sample. The OCT system
consists of an 890 nm light source, with a high axial resolution of 3.5 µm. The light
is split into the reference arm, where it is reflected back with a mirror, and the sample
arm, where it interacts with the tissue sample. Galvanometer mirrors are used for
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Fig. 9.3 Vascular imaging using compressional wave ARF-OCE system. a System schematic dia-
gram. b OCT structural image of cadaver coronary artery. c OCE phase image under compressional
wave excitation. d H&E histology of corresponding segment. e Close-up view of lesion in yellow
box. Scale bar: 1 mm (Qi et al. 2012, 2013)
scanning the sample. The backscattered light from both the sample and reference
arms travel back through the same path, into the detector arm, where their spectrums
are detected using a line scan CCD camera. The interference signal is analyzed, and
each A-line is obtained accordingly. The intensity and phase information for each
A-line can be extracted by means of Eq. 9.1, so the OCT and Doppler OCE images
can be obtained.
Using the system outlined in Fig. 9.3a, human cadaver coronary arteries were
studied (Qi et al. 2012). The sample was placed between the ultrasound transducer
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