2.3.1.

Theory

PA imaging is, fundamentally, based on PA effect, which is the generation of mechanical (sound) waves in a sample material associated with illumination of electromagnetic (EM) radiation for a short duration,^132–134 i.e., it is a phenomenon of the generation of acoustic waves due to transient absorption of EM energy and the subsequent rapid heating in a material sample irradiated with EM waves.⁸⁵ The entire process of the effect can be subgrouped into three distinct stages: (1) transient irradiation of tissue sample of interest with EM (light) waves, (2) generation of mechanical (acoustic) waves in a wide range of frequency (Hz to GHz),^132,135 and (3) propagation of PA waves.

In the transient illumination stage, a narrow pulse of EM waves (pulse-width of a few nanoseconds) with operating wavelengths in visible or near-infrared (NIR) region is delivered to the tissue surface to irradiate a prespecified region-of-interest (RoI) at a certain depth (submillimeters to a few centimeters). This stage is governed by Maxwell’s theory of EM radiation that governs the propagation of EM waves in a (scattering or nonscattering) medium.¹³⁶ In other words, the propagation stage is characterized by the distribution of physical parameters—electric permittivity [$ϵ(\overrightarrow{r})$] and magnetic permeability [$\mu (\overrightarrow{r})$] and, in turn, optical properties [absorption [${\mu }_a(\overrightarrow{r})$] and scattering [${\mu }_s(\overrightarrow{r})$] coefficients, and refractive index [$n(\overrightarrow{r})$]—in propagating medium (tissues, in our case) and characteristics of the illuminating radiation [operating wavelength ($\lambda$) and radiation energy ($E$)]. As EM (light) waves are propagating through tissue medium, optical energy is deposited in tissue that is characterized by ${\mu }_a(\overrightarrow{r})$ and ${\mu }_s(\overrightarrow{r})$. This absorbed energy is converted into heat energy through vibrational and oscillational relaxation of constituent particles/molecules of the medium, therefore resulting in a localized rise of temperature over the radiation-irradiated tissue region.⁸⁵ For a given intensity of the incident optical (laser) beam, initial rise of temperature [$\mathrm{\Delta }T(\overrightarrow{r})$] is dependent on ${\mu }_a(\overrightarrow{r})$. Due to rapid heating, associated with transient illumination of radiation under the constraint of illumination duration to be less than time scales of thermal and stress confinements, irradiated tissue undergoes thermoelastic expansion⁹⁹ inducing a transient rise in pressure. This initial pressure rise is commonly called PA pressure. Shortly, generation of PA waves is fundamentally based on PA effect,¹³⁴ which is the generation of acoustic waves—due to thermoelastic expansion—upon transient irradiation (short duration of a few nanoseconds) of sample material with a light beam. In similar fashion, in thermoacoustic (TA) effect, which is exploited in TA imaging, short-pulse microwaves irradiating a sample induce transient heating due to absorption of EM energy that is in the microwave frequency range.¹³⁵ In both cases, this transient heating—due to absorption of transient EM waves—produces a fractional change in volume ($V$). From thermodynamics, the fractional volume change ($\mathrm{\Delta }V$) is given by^137,138

If $P_0(\overrightarrow{r})$ is initial pressure rise, i.e., initial PA waves, considering residual pressure prior to light illumination as a reference, one can write as follows:^85,139

In PA imaging, acoustic signals with frequencies of order of MHz (in the case for elastography, $\sim 50\mathrm{MHz}$) are selectively picked up keeping an acoustic sensor (commonly, the ultrasound transducer is used)—that may consist of single element or an array of ultrasound transducer elements—at boundary of sample of interest. Several techniques—including model-based iterative methods that involve solving of differential equation [Eq. (4)] and beam-forming methods—are reported for reconstruction of initial pressure rise [$P_0(\overrightarrow{r})$] from boundary measurement of [$P(\overrightarrow{r})$] being carried out broadly in various ways: (1) tomography-based techniques, which either involve solving differential equation [Eq. (4)] with model-based iterative methods or beam-forming methods, and (2) microscopy techniques, where a tightly focusing ultrasound transducer is employed to detect initial PA signals over the narrow focal region. The detail of PA microscopy is presented in Sec. 2.3.3. Tissue material properties are derived from measured initial pressure signals. Several research studies are reported in the literature on the derivation of ${\mu }_a(\overrightarrow{r})$, $v_L(\overrightarrow{r})$, and $T(\overrightarrow{r})$ from measured PA signals in sample boundary. Our previous experimental study⁸⁵—underscored herein in Sec. 2.3.3—demonstrated that elastic coefficient distribution [$E(\overrightarrow{r})$] of a sample material attributes to the generation of PA waves, i.e., strength of PA signal is dependent on the stiffness of sample material that supports propagation of mechanical acoustic waves through it. Earlier, it was understood that the generation of PA waves is only due to ${\mu }_a(\overrightarrow{r})$.^{132–134,143} Experimental results—in our study—demonstrated the possibility of recovering elastic property of tissues using PA-imaging technique. Our study extends the horizon of PA-imaging technology for diagnosis, assessment, and therapeutic treatments of various life-threatening diseases, which are characterized with a change in the elastic property of tissues with pathological stages.^4,93,94

2.3.2.

Frequency selection of ultrasound sensor for detection of PA-signal contrast due to elastic coefficient

Equation (3) shows that—for a given intensity of incident EM radiation—$P_0(\overrightarrow{r})$ is characterized by ${\mu }_a(\overrightarrow{r})$ as well as $\beta (\overrightarrow{r})$, whereas $\beta (\overrightarrow{r})$ is a characteristic of elastic property [$E(\overrightarrow{r})$] that possesses distinctive frequency response to an externally induced mechanical stimulation. This initial pressure raise gives rise to a generation of acoustic waves with a frequency of the order of Hz to GHz. Studies^{85,134,135,139} showed that frequency response of generated acoustic signals is characterized by associated contrast mechanisms. Distribution of concentration of water and ions is the main source of contrast in acoustic waves with frequency $\sim \mathrm{GHz}$ in association to an irradiation with EM waves in microwave range—called TA waves—whereas constituents of blood (Hb, ${\mathrm{HbO}}_2$, total Hb, and oxygen saturation) and melanin are the major sources of acoustic waves with frequency MHz in response to an irradiation with EM waves in visible or near-infrared region—known as PA waves.¹³⁵ This indicates that one needs to adapt different types of sources and acoustic sensing systems of different central frequencies for measurement and recovery of various components of tissue optical absorption that are of clinical importance for diagnosis and therapeutic treatments. Say, in TA-imaging, microwaves (frequency of a few $\mathrm{GHz}$ and pulse-width of a few microseconds) are used as source and antenna-based microwave sensors are used as signal sensing systems.¹³⁵ In PA-imaging, short-pulse laser with a pulse width of a few nanoseconds is used as a source and US-transducer is used as a detector. Generally, US transducers ($\sim 1$ to 10 MHz) were employed for recovery of ${\mu }_a(\overrightarrow{r})$.^132,134,143 We observed that (experimental results are shown later), in a medium with contrast in spatial distribution of elastic property while maintaining uniform distribution in ${\mu }_a$, US transducers (with operating frequency $\sim 1$ to 10 MHz) are not sensitive to detect light-induced PA signal contrast, and it is detectable with US transducer $\sim 50\mathrm{MHz}$, i.e., the dominant frequency of PA-signals is $\sim 50\mathrm{MHz}$. In other words, the frequency response of light-induced PA-signals is shifted from $\sim 1$ to 10 MHz [in the case of contrast in ${\mu }_a(\overrightarrow{r})$] to $\sim 50\mathrm{MHz}$ [in the case of contrast in $E(\overrightarrow{r})$]. From Classical Physics, in an elastic system consisting of distribution of mass that can be considered as a system of different masses coupled to each other by springs of different spring constants, possible frequencies of natural mode of vibration of the masses are increased in comparison to natural frequencies of vibration of individual masses when they are left uncoupled.¹⁴⁴ This physical system of interconnected springs has a close similarity to a (tissue sample) system of clinical interest that is constituted by biomolecules (including organic) of diverse sizes and shapes, and intermolecular distances. A proper coupling in propagation of mechanical (PA) waves, being facilitated by natural frequency of vibration of a given coupled-mechanical system (biological tissue, in this case), may be attributed by distribution of physical entities (including biomolecules and inhomogeneity) with an intermolecular distance close to acoustic wavelength corresponding to acoustic frequency of interest (MHz, in the case of PA imaging).

In our previous report,⁸⁵ we presented an experimental study on the selection of frequency response of PA signals that are generated due to contrast in distributing elastic property of the sample material. We observed that response is biased with maximal peak response at a particular operating frequency of the acoustic sensor, and consequently, detection of PA signals demands a selection in operating frequency of US transducer being employed as an acoustic sensor.

2.3.3.

Photoacoustic microscopy elastography imaging

Figure 2 depicts a schematic diagram of PA microscopy (PAM) imaging system, more specifically acoustic-resolution (AR) PAM system, which we employed as an imaging unit in the study of PAM elastography.^85,89 In PAM imaging modality, a highly energetic optical beam is adapted to illuminate tissue sample of interest over a prespecified region (for a short duration ns), and the resulted initial pressure rise [$P_0$, given by Eq. (3)] is selectively picked-up by employing a tightly focusing ultrasound transducer. In the sense, tightly focusing nature of an ultrasound transducer enables to detect acoustic signal over a narrow region specified by its focal zone (focal spot size $\sim 60\mu \mathrm{m}$). In this way, PAM enables to pick-up initial pressure ($P_0$), in contrast to photoacoustic tomography (PAT), where (secondary) pressure waves ($P$)—given by Eq. (4)—are acquired by keeping the unfocused acoustic sensor in sample boundary. A detail description of PAT elastography imaging, from experimental aspects, is provided in Sec. 2.3.4. Note that both initial pressure rise ($P_0$) and secondary pressure waves ($P$) propagating in an elastic medium provide vital tissue thermal, mechanical, acoustic, and optical properties (as explained in Sec. 2.3.1). From Eqs. (3) and (4), $P_0$ and $P$ are characteristics of acoustic velocity in the propagating medium and hence, bulk-modulus ($B=\rho /v_L^2$). However, elastic parameters (bulk, shear, and Young’s moduli) of the medium are not independent to one another but they are inter-related by Poisson’s ratio [say, Young’s modulus $E=3(1-2\gamma )B$,⁹ where $\gamma$ is Poisson’s ratio]. In the sense, PA signals ($P_0$ and $P$) are sensitive to a variation of Young’s modulus ($E$). Briefly, to describe our AR-PAM, the imaging system comprises two main subunits, i.e., optical pulse illumination and acoustic detection. In illumination, a beam of coherent light (pulse-width $\sim 6\mathrm{ns}$, pulse repetition frequency $\sim 10\mathrm{Hz}$, wavelength $\sim 720\mathrm{nm}$, and energy density $\sim 22{\mathrm{mJ}/\mathrm{cm}}^2$) from a laser source (Surelite OPO Plus, Continuum) was delivered—through an assembly of two optical fibers (as shown in Fig. 2)—to irradiate prespecified RoI of tissue-mimicking (agar) phantom. For picking-up pulse laser-induced PA-signals—selectively, from a narrow focal region of the transducer—a tightly focusing ultrasound transducer was kept in sample boundary. In the experimental studies, various focusing ultrasound transducers—of different operating central frequencies (1, 3.5, 7, and 50 MHz)—were employed. The transducer ($\sim 50\mathrm{MHz}$) was custom-make (focal spot size $\sim 61\mu \mathrm{m}$, focal length 5 mm, and length of the focal zone $\sim 1\mathrm{mm}$), whereas the other transducers (focal length $\sim 10\mathrm{mm}$) were supplied by Panametrics, Olympus Corp. During experiments, optical fibers and transducer—which are housed together in a holding system—were immersed inside an acoustic-coupling medium (water, in our case) in a container for proper coupling of ultrasound transmission. The sample was kept in a fixed position below the water container. It is to note that an acoustic coupling gel was applied over the sample surface for proper coupling of ultrasound transmission. A 3-D image is achieved by raster scanning technique whereby time-resolved measurement at a scanning position gives a 1-D image data. Various focusing transducers with operating frequencies (1, 3.5, 7, and 50 MHz) were employed for the study of the frequency response of PA signals with signal contrast from the elastic property.⁸⁹ In the studies, agar-based tissue-mimicking phantoms—whose physical properties [mechanical and optical (scattering and absorption)] can be tailored independently^145–147—were employed as investigating (imaging) samples.

Fig. 2

Schematic diagram of AR-PAM imaging system.

Experimental studies were reported⁸⁵ for investigation of variation in strength of (short pulse) laser-induced PA signals with elastic coefficient of targets and, in turn, for studying shift in frequency response of ultrasound-detecting transducer, i.e., change in natural frequency of mechanical vibration of tissue targets, with change in contrast of either optical or mechanical properties of targets. Experimental studies were performed in agar-based tissue-mimicking phantoms—with targets ($y$’s) of various contrasts in optical and mechanical properties [see Fig. 3(a)] and in sizes [Fig. 3(b)] being embedded in a background sample—simulating tissue abnormalities (due to diseases) in normal background tissues. Moreover, in our previously reported studies,⁸⁹ experiments were also carried out—adopting third tissue-mimicking phantom [shown in Fig. 3(c)]—to demonstrate and validate variation in the strength of laser-induced PA-signal with a selection of the operating frequency of US transducer, which is used as an acoustic (signal) sensor, for various types of contrasts (${\mu }_a$ and $E$) of targets. To describe briefly, for all targets [$T_1$ to $T_6$, in Fig. 3(a)], ${\mu }_a$ and ${\mu }_s$ were fixed similar to that of background (0.01 and $1.20{\mathrm{mm}}^{-1}$, respectively) except for the second target ($T_2$) in which ${\mu }_a=0.02{\mathrm{mm}}^{-1}$. Against $E=94\mathrm{kPa}$ (for background), elastic coefficients [Young’s modulus ($E$)] for targets ($T_3$ to $T_6$) were tailored to 345, 269, 202, and 143 kPa, respectively, which were achieved by variation of agar concentration with 2 mg (background, $T_1$, and $T_2$), 4.0 mg ($T_3$), 3.5 mg ($T_4$), 3.0 mg ($T_5$), and 2.5 mg ($T_6$) to 100 mL of water. A detailed description of (agar) sample preparation is provided in Ref. 89. These elastic properties are found to be within the range of elastic property of (cancerous) soft tissues.^9,145 As shown in Fig. 3(b), targets of different cross-sections [$0.5\times 0.5{\mathrm{mm}}^2$, $1.0\times 0.5{\mathrm{mm}}^2$, $2.0\times 0.5{\mathrm{mm}}^2$, and $3.0\times 0.5{\mathrm{mm}}^2$ ($x\times z$) for $T_1$ to $T_4$] were embedded in background phantom for studying the dependence of PA signals on target size while optical and elastic properties of embedded targets were tailored similarly to those of background ($E=94\mathrm{kPa}$). In the third sample [Fig. 3(c)], two strips of target ($t_1$ and $t_2$ with rectangular cross-section [$2.0\times 0.5{\mathrm{mm}}^2$ in $xz$-plane)]—having contrast in elastic coefficient [Young’s modulus, $E=202\mathrm{kPa}$ (with no contrast in ${\mu }_a$, for $t_1$) while having contrast in ${\mu }_a=0.02{\mathrm{mm}}^{-1}$ (with no contrast in mechanical properties, for $t_2$)]—were embedded in background phantom.

Fig. 3

(a) Imaging targets ($T_1$ to $T_6$) of similar cross-sections ($1.0\mathrm{mm}\times 0.5\mathrm{mm}$ in $xz$-plane) with variation in elastic coefficient were embedded in background phantom [$25\times 20\times 20{\mathrm{mm}}^3$ ($x\times y\times z$)]. (b) Targets of different cross-sections ($0.5\mathrm{mm}\times 0.5\mathrm{mm}$ to $3.0\mathrm{mm}\times 0.5\mathrm{mm}$ for $T_1$ to $T_4$ in $xz$-plane) with optical and mechanical properties similar to that of background phantom. (c) Contrast in $E$ (with similar ${\mu }_a$ for $t_1$) and ${\mu }_a$ (with similar $E$ for $t_2$) with respect to that of background sample.

Figure 4(a) depicts 3-D reconstructed image, which is representative of detected laser-induced PA signals obtained through extending 1-D scanning (along the $x$ axis) toward $y$ axis, and Fig. 4(b) gives a 2-D reconstructed image that is representative to PA signals obtained through 1-D scanning of the sample [shown in Fig. 3(a)] along the $x$ axis in the middle portion of the sample. In these experiments, the focusing transducer of the operating frequency 50 MHz was employed as an acoustic sensor. The image demonstrates that detected PA signals have contrast for targets ($T_2$ to $T_6$) with respect to the background while target ($T_1$) is not detectable, i.e., $T_1$ gives no PA signal contrast. Figure 4(c) gives line-plot showing a variation of PA-signals for all pixels along a marked line [as indicated in Fig. 4(b)]. Observed contrast in PA signals, corresponding to targets ($T_3$ to $T_6$), is due to the contrast in elastic coefficient (Young’s modulus, $E$) of embedded targets (which were tailored to have contrast in the elastic property only while maintaining similar optical properties, i.e., ${\mu }_a$ and ${\mu }_s$) in comparison to that of background phantom. Observed PA-signal contrast corresponding to target ($T_2$), whose elastic coefficient ($E$) was tailored similarly to that of background phantom while contrast being maintained only in ${\mu }_a$, is because of contrast in optical absorption coefficient (${\mu }_a$). Because, for the target ($T_1$) that was tailored with no contrast both in optical and elastic coefficients with respect to background, there is no contrast observable in the reconstructed PA signals. Figure 4(d) presents the variation of measured PA signals (obtained through averaging PA-signals over target regions) with mechanical properties (Young’s modulus). From Figs. 4(a)–4(d), it is observed that (short pulse) light-stimulated PA signals increase with an increase in elastic property (say, Young’s modulus), which is not linear.

Fig. 4

3-D reconstructed image (a) of PA signals obtained from a sequence of 2-D PA-representative images (b) by employing Amira software. (c) Line plot showing the variation of PA signals along the line as marked in (a). (d) Variation of PA signals (as obtained from target regions) with Young’s modulus, $E$. Experiments were performed in sample depicted in Fig. 3(a) while a tightly focusing transducer of operating frequency 50 MHz was employed as acoustic sensor.

Figure 5(a) gives a 3-D reconstructed image of PA signals while the variation of PA signals along a line [as depicted in Fig. 5(a)] in the first frame is shown in Fig. 5(b). The figure demonstrates that the strength of measured PA-signals is similar for all targets [$T_1$ to $T_4$ shown in Fig. 3(b)]. Note that targets ($T_1$ to $T_4$) in the second sample [Fig. 3(b)] had no contrast in elastic and optical properties (among themselves as well as the background) while differing only in their sizes or dimensions of the cross-section in $xz$ plane. Variation of measured PA signals with size of target cross-section is also shown in Fig. 5(c), and this result demonstrates that measured PA signals are not dependent on the size of the target of interest.

Fig. 5

(a) Three-dimensionally reconstructed image of PA signal for raster scanning in sample (2). (b) Line plot showing variation of detected PA signals along a midline in the first frame of 3-D reconstructed image [marked line as shown in (a)]. (c) Dependence of PA signals on size of targets embedded in background.

Figure 6 shows the results of experiments being conducted in the first sample [shown in Fig. 3(a)] for studying variation in PA-signal strength with a change in elastic coefficient ($E$) of targets.^85,89 Two sets of experiments were conducted, in which two different focusing US transducers with operating frequencies of 50 MHz [experimental results are shown in Figs. 6(a)–6(c)] and 3.5 MHz [shown in Figs. 6(d)–6(f)] were used alternately as an acoustic sensor. Figure 6(a) presents a reconstructed 2-D image that is representative of PA-signal strength obtained through raster scanning of the second sample [Fig. 3(b)] in the middle portion along the $x$-direction and the corresponding 3-D image is shown in Fig. 6(a). From images depicting the strength of detected PA signals (corresponding to $T_2$ through $T_6$), it is observed that targets are clearly detectable (though feebly for $T_6$) in the background while $T_1$ is not detectable in the background without any contrast in measured PA signals. Figure 6(c) displays line-plots of variation in the strength of PA signals along a marked line [indicated in Fig. 6(b)]. In consideration to the description made above [for first sample, 3(a)], observed contrast in the measured PA signal strength for targets ($T_2$ to $T_6$)—having contrast in Young’s modulus ($E$) relative to background elastic coefficient while maintaining no contrast in optical properties—is due to contrast in elastic property of targets relative to that of background. On the other hand, contrast observed for $T_2$—with no contrast in $E$ while tailoring contrast in ${\mu }_a$—is provided due to contrast in ${\mu }_a$. From the figure, target $T_1$ gives no contrast in measured PA signals as there is no contrast in optical as well as elastic properties. From experimental results, it is observed that the strength of measured PA signals increases with an increase in contrast level of elastic coefficient of embedded targets (that are coupled to background). The underlying reason is due to change in natural frequency of vibration of elastically coupled system, constituted by background and targets, toward operating frequency of transducer in higher range ($\sim 50\mathrm{MHz}$).⁸⁹ Figures 6(d) and 6(e) present 3-D and 2-D images obtained from scanning of the second sample with ultrasound transducer of operating frequency of 3.5 MHz, whereas Fig. 6(f) gives variation in measured strength of PA signals along a line marked passing through targets (as indicated). From the figures, it is evident that measured strength of PA signals, as detected by 3.5 MHz, gives contrast only for a target $T_2$ while PA signal strengths corresponding to targets $T_1$ to $T_6$ (with exception of $T_2$) have no contrast irrespective of contrast level in target elastic coefficients. This is due to the increase in natural frequency of mechanical vibration of an elastically coupled system that is far away from the transducer operating frequency ($\sim 3.5\mathrm{MHz}$).

Fig. 6

For experiments performed in second sample, (a) and (d) 3-D and (b) and (e) 2-D images representative of strength of PA signals as detected by focusing US transducers [operating frequencies 50 MHz (a)–(c) and 3.5 MHz (d)–(f)]. Line-plots corresponding to the marked lines are shown in (c) and (f).

Figure 7(b) gives—as shown in our reported study⁸⁵ for experiments being performed in the third sample [Fig. 3(c)]—a variation in measured PA-signal strength with transducer operating frequency. For $t_1$ that has contrast in $E$, measured signal strength is relatively high for higher operating frequency ($\sim 50\mathrm{MHz}$) in comparison to that of lower range ($\sim 3.5\mathrm{MHz}$). PA-signal strength is weak (undetectable) in the background. For $t_2$ having ${\mu }_a$-contrast, PA-signal strength is higher for operating frequency in the lower range ($\sim 3.5\mathrm{MHz}$) than that of ultrasound sensor with higher operating frequency ($\sim 50\mathrm{MHz}$). From the figure, we observe a shift in the frequency response of measured PA-signal strength with variation in nature of contrast—either elastic property ($E$) or optical absorption coefficient (${\mu }_a$)—in targets. Experimental results imply that US transducer of higher operating frequency ($\sim 50\mathrm{MHz}$) is demanded to detect PA signals from targets having contrast in $E$, instead of conventionally used US transducer of operating frequency in the lower range ($\sim 1\mathrm{MHz}$) for detecting PA signals from targets having contrast in ${\mu }_a$.

Fig. 7

Variation of PA signals as obtained from target regions of third sample [Fig. 3(c)] with operating frequency of (focusing) US transducer.

2.3.4.

Photoacoustic tomography elastography imaging

Pengfei et al.^62,91,92 developed a PAT imaging system—that they called quantitative photoacoustic elastography (QPAE) imaging system—for recovery of tissue elastic property noninvasively and nondestructively. Validation studies were carried out both in tissue-mimicking phantom (gelatin) and animal model (mouse). The study demonstrated the feasibility of PAE—with an accuracy less than 5.2% in comparison to theoretical values—by imaging strains induced in various layers of phantoms with various stiffness values. Presented experimental results provide a mapping of fat and muscle based on measured elastic property contrast with the experiment being conducted (in-vivo) on a mouse leg and results were compared with that of USE imaging modality being performed simultaneously.

QPAE imaging system—as shown in Fig. 8 (the schematic diagram is reproduced with permission from Refs. 91 and 92)—employed a commercially available linear-array photoacoustic computed tomography system. In this study, for PA excitation, a pulse laser beam (pulse width $\sim 10\mathrm{ns}$ and optical wavelength $\sim 680\mathrm{nm}$) at a pulse repetition rate of 20 Hz is generated from the pulse laser source. For detection of pulse laser-induced PA-signals, a linear ultrasonic transducer array (LZ250, VisualSonics Inc.) of 256 elements with center frequency $\sim 21\mathrm{MHz}$ was employed. As shown in Fig. 8, a fiber bundle—that was adopted for coupling light from the laser source to tissue sample—is split into two rectangular bars and these two bars are mounted on each side of the linear transducer array. The imaging system provides spatial resolutions $\sim 119\mu \mathrm{m}$ (in lateral direction), $\sim 86\mu \mathrm{m}$ (in the axial direction), and $\sim 1.2\mathrm{mm}$ (in elevational direction) with a frame rate of acquisition of 2-D image [5 Hz as only one quarter (i.e., 64) of 256-transducer elements are utilized in one laser pulse excitation and a full set of data from all of 256 elements is used for generating a 2-D image]. An aluminum plate—larger than sample—was used to exert an axial compressive force on tissue sample of interest and compression plate was equipped with an imaging window at center so as to allow laser-induced PA signals to be detected by transducer array. A manually controlled translation stage was adopted to provide compression to imaging object resting against a rigid object holder. Reading from a high precision digital weighing scale (S200, Ohaus), on which object and object holder was kept, was used to calculate compression stress. The total displacement of tissue sample was obtained from measurement in manual stage readings while stress acting on the sample is estimated (from readings before and after compression) as follows:

Fig. 8

Schematic diagram of QPAE imaging system: (a) at an elevational view and (b) at 3-D view. Lateral and elevational view of illumination of pulse laser beam in QPAE imaging system [(a) and (b)] are depicted in (c). (Figures are reproduced with permission.)

Experiments were conducted in tissue-mimicking gelatin-based phantoms with various concentrations of 40 to $100\mathrm{g}/\mathrm{L}$ (at the step of $20\mathrm{g}/\mathrm{L}$) that provide mechanical properties of the sample at different values. To provide optical contrast for PA imaging, microspheres ($\sim 50\mu \mathrm{m}$)—at a concentration of $\sim 5/{\mathrm{mm}}^3$—are mixed in gelatin phantom. Microsphere-embedded gelatin phantoms were imaged individually before and after mechanical compression with external stress (of 53 Pa) using manually controlled mechanism mentioned above. Time-resolved (A-line) signals—obtained corresponding to each of the transducer elements—before [as shown in Fig. 9(a)] and after compression [presented in Fig. 9(a)] were cross-correlated to estimate axial displacement due to compression. A cross-sectional map of displacements of microspheres after compression is presented in Fig. 9(c). In this cross-correlation-based method, cross-correlation (over a short length of $90\mu \mathrm{m}$) of A-lines—corresponding to before and after compression—was computed and displacements obtained from microspheres at each depth were then averaged. The slope of linear fitting—as shown in Fig. 9(d)—of measured variation of displacement against depth, i.e., the magnitude of the average gradient of displacement, gives average strain of each gelatin phantom. Five sets of experiments are repeated for each of the four gelatin phantoms and measurement of strain with respect to gelatin concentration is plotted in Fig. 9(e).

Fig. 9

Measurement of strain being carried out in gelatin phantom by PAE: (a) and (b) PA images of a bilayer gelatin phantom mixed with 50 μm microspheres acquired (a) before and (b) after compression. (c) Mapping of displacement of embedded microspheres obtained from Figs. 9(a) and 9(b). (d) Variation of average displacement versus depth. Data were fitted by a linear function for each layer. (e) Measured strains of gelatin phantoms with 4%, 6%, 8%, and 10% concentration in weight. Figure shows a fit of the curve with quadratic model. (Figures are reproduced with permission.)

Experimental studies were extended to conduct experiments in (in vivo) animal model—in mouse leg in conformity with laboratory animal protocols approved by the Animal Studies Committee at Washington University in St. Louis—for validation of elastography imaging system to preclinical studies and its applications. In the experiments, the mouse leg was imaged before [shown in Fig. 10(a)] and after [shown in Fig. 10(b)] applying an external compression force ($\sim 12\mathrm{mN}$). From these two images, a displacement image was obtained using the above-mentioned cross-correlation technique, and it is shown in Fig. 10(c). Raw strain image was then superimposed on structural PA image [Fig. 10(e)]. Regions of softer tissue, i.e., fat, give larger strains, which were validated by USE using the same linear-array imaging probe, which showed a similar distribution of strains, as depicted in Figs. 10(d) and 10(f).

Fig. 10

PAE images of a mouse leg in vivo (a) before and (b) after compression. (c) Strain image of mouse leg (in vivo). (d) Strain image of mouse leg obtained by USE. (e) Strain image of mouse leg obtained by PAE superimposed on structural PA image. (f) Strain image of mouse leg obtained by USE on structural ultrasound image. (Figures are reproduced with permission.)

2.3.5.

Challenges for photoacoustic elastography imaging and some technological approaches

PAEI is associated with certain challenges that may be drawn as follows: (1) low PA signal contrast—as detected by acoustic (ultrasound transducer) sensor kept on sample boundary—due to variation of mechanical elastic property distribution of tissue in comparison to that of optical absorption coefficient;^85,89 (2) quantitative assessment, i.e., to quantify measurement, of elastic property; and (3) discrimination of contrast in PA signal due to elastic property variation from that of other parameters (including optical absorption coefficient).

External mechanical stimulation of tissues combined with correlation-based technique—as done in USE imaging⁶—is a potential technological approach to enhance signal contrast. Acoustic burst (mechanical) stimulation technique,⁵² alternative to continuous external excitation, is shown to improve modulated signals significantly due to increased mechanical modulation of tissue—over an ultrasound insonified region—with higher acoustic radiation force driven by an external (tightly) focusing ultrasound transducer. As done in Ref. 60, Young’s modulus can be estimated from resonance peak—corresponding to natural frequency of vibration of tissue materials over ultrasound insonified region—observed in measurement and plots of PA signal strength against the operating frequency of external (ultrasound) mechanical stimulation. In the end, in comparison to the similarity in nature of externally induced mechanical stimulation and investigation of interrogating signals (say, light and acoustic), existing technologies for quantitative recovery of elastic properties (more specifically, in USE and OCE) may be adapted to PAE.