Review of the Fundamental Measurement Modalities in Photoacoustic Mechanical Imaging

Abstract

Photoacoustic (PA) imaging is a non-invasive imaging technique with high optical resolution and acoustic penetration depth, which has been widely used in medical and clinical researches. As an important part in functional PA imaging, photoacoustic mechanical imaging (PAMI) has great potential in visualizing and understanding the local development of pathological process. This review provides lots of PA breakthroughs which have been made in elasticity detection, viscosity detection, and viscoelasticity detection through PAMI techniques. The current research problems, challenges and future development directions were discussed.

1. Introduction

Photoacoustic imaging (PAI) is a new non-invasive and non-ionizing biomedical imaging method developed in recent years [1,2,3,4,5,6,7,8,9,10,11], which combines high optical contrast with excellent depth of penetration [12,13]. When a pulsed laser is irradiated into a biological tissue, the optical absorption of the tissue will produce an ultrasonic signal, which we call the photoacoustic (PA) signal generated by photoexcitation. The PA signal carries the optical absorption information of the tissue, and the image of the optical absorption distribution in the tissue can be reconstructed [14,15]. PAI has a wide range of application value in early diagnosis of cancer, oxygen supply detection of various organs and other disease detection and scientific research [16,17,18,19,20,21,22,23,24,25,26].

Functional photoacoustic imaging is one of the most important techniques in PAI and has great potential in visualizing and understanding the local development of pathological process [27,28,29,30,31,32,33,34]. Traditional PA techniques only utilized the optical absorption properties without considering the mechanical reactions of biological tissues, which can be used to provide additional mechanical contrast images. Photoacoustic mechanical imaging (PAMI), as one of the technologies in functional PA imaging, characterized and identified tissues by analyzing the mechanical characteristics, resonance frequency and damping coefficient of tissues, making the detection of various diseases more diverse and accurate.

Biomedical mechanical characterization based on PAMI techniques is still an emerging field of research, experiencing rapid technological development and interest growth. PAMI has the potential to change current diagnostic approaches as a new clinical approach for treatments like tumor detection, blood viscosity measurement, and atherosclerotic plaque assessment etc. In this review, the theory, principle and application of several PAMI techniques were presented. Elasticity, viscosity and viscoelasticity parameters of biological samples can be extracted by these PAMI techniques, which will help in characterization in mechanical properties of tumor tissues, blood viscosity measurement and viscoelasticity analyzation of atherosclerotic plaques [35,36,37,38]. The advantages and characteristics of multiple PAMI techniques are summarized and discussed, as well as future trends explored.

2. Applications of Photoacoustic Mechanical Imaging

2.1. Photoacoustic Elasticity Measurement

2.1.1. PA Elastography

Elastography is a medical imaging technique that can map the elastic distribution of biological tissues noninvasively and reflect the pathological condition [39,40]. Elastography was first achieved by ultrasound technique in 1991, and later elastography was also realized by using magnetic resonance imaging. Further, elastography combined optical coherent tomography to greatly improve the spatial resolution. However, the strong scattering of light limited the imaging quality of optical coherent tomography in deep tissues. Photoacoustic tomography (PAT) can map the optical absorption contrast of biological tissues while maintaining high spatial resolution and excellent depth of penetration. A study has demonstrated the feasibility of PA elastography using a line array PAT system [41]. In the study, a pulsed laser beam was coupled into the fiber bundle, which were placed on either side of the linear ultrasonic transducer array. Precise external compression was provided by a compression plate adjusted by a manual translation stage. Four identical phantoms were designed to demonstrate PA elastography with external compression. The PA signals were then collected to construct the PA images. Microspheres were placed evenly in the model to provide absorption contrast for PAT. The object was evenly illuminated and mechanically effected by the laser, then the displacement of the object was collected. The stress after compression was calculated before and after compression [41]:

$$\sigma ={\displaystyle \frac{g\left(m_a-m_b\right)}{A}}$$

(1)

where σ is pressure, $g$ is the acceleration due to gravity, and $m_a$ and $m_b$ are the scale readings before and after compression.

Bilayer gelatin phantoms with different concentrations were imaged before and after compression to demonstrate the accuracy of PA elastography (Figure 1a,b). The displacement images were showed in Figure 1c. Microspheres at different depths were displaced equally and fitted by a linear function of each layer (Figure 1d). The slopes of the two linear fittings reflected the strain in both layers. The strain ratio of the two layers was 4.0 ± 0.2, which was consistent with the theoretical value. Finally, in vivo PA imaging of mouse legs was demonstrated. The PA images before and after compression was correlated with each other to obtain the displacement of the tissues (Figure 1e,f). The strain image of PA elastography can be obtained by differentiating the value of the axial displacement (Figure 1g). Ultrasound elastography was tested with the same transducer array, which showed similar strain distribution (Figure 1h). The strain images obtained by PA elastography and ultrasound elastography were then superimposed on the structural PA image and ultrasound image (Figure 1i,j). Thus, PA elastography can be implemented on traditional PAT system to provide more comprehensive mechanical and functional information of biological tissues [42].

2.1.2. PA Remote Sensing Elastography

To realize the non-contact and non-interference PA detection of elastic modulus of biological tissues, photoacoustic remote sensing elastography (PARSE) was proposed [43]. Through obtaining the response time of the PARS signal under laser excitation, the elasticity of the biological tissues can be reflected by the response time due to the nature of elasto-optic effect. During the process of PA effect, then the tissue displacement $u_z$ along the laser excitation satisfied:

$${\displaystyle \frac{{\partial }^2{u}_z}{\partial t^2}}-\left(c_T^2+v{\displaystyle \frac{\partial }{\partial t}}\right){∆}_{\perp }{u}_z=F_Z$$

(2)

where the subscript z is the axial coordinate along the incident laser direction, ${∆}_{\perp }$ is the Laplace operator on the axial coordinate. $c_T=\sqrt{\mu /\rho }$ is the velocity of the shear wave, where μ and $\rho$ are shear elastic modulus and density respectively. This propagating PA wave caused axial compression and stretching. Considering the Gaussian laser as a δ function, through the Hankel transform, the expression for $u_z$ can be obtained:

$$u_z={\displaystyle \frac{\alpha \Gamma I_{0}R\tau }{\rho c_T}}{\displaystyle \frac{\frac{\sqrt{E/3\rho }}{R}t}{1+\frac{4\eta }{\rho R^2}t+{\left(\frac{\sqrt{E/3\rho }}{R}\right)}^2{t}^2}}$$

(3)

where $\Gamma$ is the Grüneisen parameter, $I_0$ is the laser intensity, τ is the laser pulse width and R is the incident laser spot radius. The displacement $u_z$ increased initially in time after excitation and reached its peak u_zmax at $t_{max}=R/\sqrt{E/3\rho }$. Thus, through the rise time of the displacement, the elasticity can be obtained as follows:

$$E=M/t_{max}^2$$

(4)

where $M=K\ast 3R^2\rho$ is an experimental constant, K is a system calibration parameter. By obtaining the rise time of displacement, tissue elasticity can be obtained.

To validate the PARSE technique, heart, kidney and liver sections with different stiffness were imaged. Hematoxylin and eosin staining was used on all the sections to provide absorption contrast for PARS imaging. The PARSE and PARS images of the organ sections were showed in Figure 2a,b respectively. Although the contrasts of these three PARS images are very similar, PARSE images can provide different mechanical detail that the heart tissue is the most elastic compared with the kidney and liver tissues. Figure 2c showed the temporal PARS signal profiles of the three organ sections, then through integration of the signals, the thermoelastic displacement profile can be obtained in Figure 2d. Figure 2e showed the rise times of the displacement from the three organ tissues, which showed the sensitivity of PARSE technique in elasticity characterization. Therefore, PARSE technique will have a wider range of applications in terms of biological elasticity detection.

2.2. Photoacoustic Viscosity Measurement

2.2.1. Frequency-Resolved PA Viscosity Measurement

Viscosity is one of the important physical parameters of biological tissues in clinical pathology [44]. Therefore, to develop technologies that can accurately and quickly measure viscosity is particularly critical. The correlation between PA frequency and viscosity has been demonstrated by measuring the PA signals of liquids in situ through PA effects [45]. When a laser beam is absorbed by a fluid, the rise in temperature of the fluid generally causes an expansion of the heated region. The mechanical motion associated with this expansion then acts as a source that launches a sound wave. The velocity field $v$ is taken to obey the Navier-Stokes equation [45]:

$$\rho {\displaystyle \frac{\partial v}{\partial t}}+\rho \left(v·\mathsf{\nabla }\right)v=-\mathsf{\nabla }p+\eta {\mathsf{\nabla }}^{2}v+(\xi +{\displaystyle \frac{\eta }{3}})\mathsf{\nabla }(\mathsf{\nabla }·v)$$

(5)

where $\rho$ is the mass density, $p$ is the pressure, $\eta$ is the shear viscosity and $\xi$ is the bulk viscosity. We suppose that the laser incident on the medium is produced by a pulsed source and that the duration of the pulse is short in comparison to the time scale of thermal diffusion. The mass density thus satisfies the continuity equation [46]:

$${\displaystyle \frac{\partial \rho }{\partial t}}+\rho \mathsf{\nabla }·v={\displaystyle \frac{\beta {\mu }_a}{C_p}}I$$

(6)

where $C_p$ is the constant-pressure heat capacity per unit mass, $\beta$ is the coefficient of volume thermal expansion, ${\mu }_a$ is the absorption coefficients, and $I$ is the temporal irradiation intensity of the laser.

Assume a PA pressure with the form of $p\left(x,t\right)=e^{iax}p(t)$ was generated at the position $x$ in a homogeneous medium, where $a$ was the propagation phase constant, and the variable of $x$ can be eliminated and the second order differential equation with variable $t$ was obtained. The temporal irradiation intensity of the laser can be expressed as $I\left(x, t\right)=1/T{\sum }_{k=-\infty }^{+\infty }{e}^{-bx+jk\Omega t}$, where $T$ is the time interval between the laser pulses, where $b$ is the effective attenuation coefficient, $\Omega$ is the angular frequency of the laser. Then we can obtain the equation for PA pressure:

$$p\left(t\right)=e^{-a^2\mu t/2\rho }{(Ae}^{j\omega t}+B{e}^{-j\omega t})+{\displaystyle \frac{c}{j\Omega Z}}{\sum }_{k=-\infty }^{+\infty }{e}^{jk\Omega t}$$

(7)

where A and B are the constants determined by initial conditions, $Z={\displaystyle \frac{a^2\mu }{\rho }}+j\left(\Omega -{\displaystyle \frac{a^2{c_s}^2}{\Omega }}\right)$ is the impedance of the system. Due to the excited PA signal has a wide frequency range, thus the full width half maximum (FWHM) of the PA frequency spectrum can be proportional to the frequency of the PA signal:

$$FWHM\propto \omega =a\sqrt{{c_s}^2-{(a{\displaystyle \frac{\mu }{2\rho }})}^2}$$

(8)

From Equation (8), we can obtain the viscosity of the fluid will result in greater loss of high-frequency components than the low-frequency components. Therefore, we can obtain the relationship between $\mathrm{t}\mathrm{h}\mathrm{e}$ FWHM of the PA frequency spectrum and the viscosity coefficient.

To confirm that the increased viscosity of the liquid will result in the loss of high-frequency components, 4% ink solutions contained glycerol with the concentrations of 20%, 30%, 40% and 50% were tested by frequency-resolved PA viscosity measurement (Figure 3a). Figure 3b showed that the PA frequency spectrum exhibited a significant shift toward lower frequencies with increasing viscosity, which demonstrated that the loss of high frequency components became higher and led to bandwidth reduction with the increase in the glycerol concentration. The PA amplitude and the FWHM of the PA frequency spectrum of each glycerol–water solution were showed in Figure 3c. The absorption coefficients of the solutions were estimated by the UV–Vis spectrometer and the PA signal amplitude gradually increased with the increase in ink concentrations. The effect of the absorption coefficient in the FWHM of the PA frequency spectrum was negligible due to the same glycerol concentration in the solutions. As is shown in Figure 3d, in the case of the same absorption coefficient of the solutions, the amplitude of the PA signals did not change much, but the FWHM of the PA frequency spectrum was directly affected by the viscosity of the solutions. The results indicated that the increased viscosity of the liquid will result in a higher FWHM reduction in the PA frequency spectrum.

To further verify the accuracy of this viscosity measurement, experiments were performed on viscosity characterization of blood in the capillaries in mouse ear (Figure 4a). As can be seen from Figure 4b, the artery and vein can be distinguished by PA imaging [45]. By Fast Fourier Transform, the frequency spectrum of the PA signals obtained from the capillaries can be used to reflect the dynamic changes of viscosity due to the metabolism in blood vessel bifurcations (Figure 4c). The FWHM of the PA frequency spectrum was much higher than that of vein, indicating that the viscosity was much lower in artery. In addition, frequency-resolved PA measurement can also monitor the viscosity changes of blood in real time as shown in Figure 4d. In conclusion, this study provided a frequency-resolved PA method for measuring the viscosity of capillaries. The frequency-resolved PA measurement can provide absorption and viscosity information of different blood vessels at the same time, which can be used for two-parameter functional imaging [47,48,49,50,51,52,53,54,55].

2.2.2. Phase-Domain PA Viscography

Another PA technique for viscosity detection is phase-domain PA viscography [56]. In the case of pulsed excitation, the laser intensity can be approximated by the function $I=1/T{\sum }_{k=-\infty }^{+\infty }{e}^{jk\omega t}$, where $\omega$ is the frequency of excited laser. Optical absorption by the absorber results in a sinusoidal temperature variation in the form of $T=T_0{e}^{i\omega t}$ due to the nonradiative transition, and then causes thermal expansion and shrinkage as well as PA wave generation based on the thermoelastic mechanism. Because of the cyclical changes of the laser, the cyclical heating in the local region induces the thermal stress, and strain generates due to the stress in the form of the force-produced PA wave. Owing to the damping effect of the viscoelasticity of biological tissues, it could be found that the force-produced PA wave would lag behind the stress with different time delay.

When an unbounded isotropic viscoelastic solid is excited by a periodically variational point heat source, the radial thermal stress in the spherical coordinate system can be expressed as:

$$\sigma =-{\displaystyle \frac{\partial G}{r}}{\displaystyle \frac{\partial \Phi }{\partial r}}+\rho {\displaystyle \frac{{\partial }^2\Phi }{\partial t^2}}$$

(9)

where $G$ is the Green’s function of thermal conduction; $\Phi$ is the thermoelastic displacement potential and defined by $∆\Phi =\alpha T(1+\nu )/(1-\nu )$; $\alpha$ is the coefficient of linear expansion; $\nu$ is the Poisson’s ratio. In the condition of temperature $T=T_0{e}^{i\omega t}$, the stress $\sigma$ can be rewritten as [57]:

$$\sigma =-E\alpha T_0{e}^{i\omega t}/(1-\nu )$$

(10)

where $E$ is the Young’s modulus, $\omega$ is the modulation frequency, and the differential constitutive equation of viscoelastic material is: $P\sigma =Q\epsilon$, where $\epsilon$ is the strain, then we can obtain the strain response as [58]:

$$\epsilon \left(t\right)={\epsilon }_A{e}^{i(\omega t+\delta )}$$

(11)

where ${\epsilon }_A$ is the amplitude of complex dynamic strain, which is determined by the amplitude of dynamic stresses, material constants and the modulation frequency. The physical significance of Equation (11) indicates that $\delta$ is the phase delay of the strain response to the stress.

Consider the generation procedure of the PA signal, the rheological Kelvin-Voigt model is chosen to represent the biological tissue with viscoelasticity, which consists of a spring and a dashpot model. Based on the derivation of elasticity in PARSE, through the rise time of the displacement, the elasticity can be expressed as $E=M/t_{max}^2$, then we can obtain the expression of viscosity $\mathrm{a}\mathrm{s}$:

$$\eta ={\displaystyle \frac{E\mathit{tan}\delta }{\omega }}$$

(12)

Therefore, we can know the relationship between phase delay $\delta$ and the viscosity $\eta$, which will be used to realize the phase-domain PA viscography.

The validation of phase-domain PA viscosity measurement was performed on the agar-gelatin phantoms with different concentrations. Figure 5a plotted the PA response from the phantoms with varying concentrations (15, 20, 25, and 30 g/L). Different PA phase delay was also observed as shown in Figure 3b. As shown in Figure 5c, viscosity increased with the increase in the agar concentration, which can be observed in the 2D reconstructed PA viscography. Meanwhile, the viscosity of the phantom was tested by conventional rheometer compared with PA measurement (Figure 5d). Finally, the independent between the optical absorption and the viscosity during phase-domain PA viscosity measurement was shown in Figure 5e, which opened a new perspective for PA technology through PA phase information.

2.3. Photoacoustic Viscoelasticity Imaging

2.3.1. PA Lock-In Viscoelasticity Measurement

Viscoelasticity is one of the most important mechanical properties in medical and biological fields. Most biological tissues show viscoelastic and rheological behavior with time-dependent mechanical responses [59]. When the biological tissue was irradiated by the pulsed laser source, the optical absorption of the biological tissue produced a non-radiative transition, resulting in thermal expansion and PA waves. Due to the viscoelasticity of biological tissues, the PA signals will have a phase delay compared to the laser trigger signal. From Equation (12), the phase delay can be expressed as:

$$\delta =\mathit{arctan}({\displaystyle \frac{\eta \omega }{E}})$$

(13)

where $E$ is Young’s modulus, $\eta$ is the viscosity coefficient and $\omega$ is the laser frequency. Thus the viscoelasticity ratio properties of biological tissues can be characterized by the PA phase delay. The lock-in detector can simultaneously record the amplitude and phase of the PA signals to reconstruct the image of optical absorption and viscoelasticity ratio distribution.

Experiments were performed to image gelatin phantoms with different absorption coefficients and viscoelastic coefficients. Figure 6a showed the photograph of gelatin phantoms. The optical absorption image of phantom by PA imaging was shown in Figure 6b. Due to the different viscoelasticity of the phantoms, it was found that the viscoelasticity image showed the high-contrast (Figure 6c). At the same time, the result in Figure 6d showed that the viscoelasticity ratio distribution was independent from the optical absorption image, which can compensate for each other’s deficiencies. To confirm the reliability of this method, experiments with mouse tumor tissue was designed found that the optical absorption in tumor tissue was higher than that of normal tissue, and the viscoelasticity ratio of tumor area was lower, as shown in Figure 6e,f. Figure 6g showed a photograph of the mouse imaged in the PA experiment. A comparison of the mean amplitude and phase delay between the tumor region and the normal region can be seen in Figure 6h. Densely staining nuclei was shown in the tumor tissue compared with the normal tissue in Figure 6i,j.

In summary, this study demonstrated a novel two-parametric imaging method based on PA lock-in measurement. This method realized a complementary comparison mechanism for optical absorption and viscoelasticity ratio simultaneously, which provided important information for non-invasive structural and functional diagnosis and clinical characterization in the biomedical field [60,61,62,63].

2.3.2. PA Microrheology

Both elasticity and viscosity are linked in the development of disease process, where pathological tissues often exhibited a repeatable cyclic mechanical behavior. Being able to measure these two parameters accurately and non-destructively helps to quickly understand the pathological process. Generally, photoacoustic elastography required the tissue to be deformed by external force compression, which will cause deformation and the elasticity can be overestimated. However, photoacoustic microrheology (PAMR) could realize the detection of viscosity and elasticity at the micron level, which was an extension of traditional PA techniques [64]. Based on the principle of thermal expansion, PAMR used a pulsed laser to irradiate tissues to generate PA waves, and from Equation (4) the surface modulus of elasticity can be obtained as:

$$E=\rho {\left({\displaystyle \frac{R}{t_{max}}}\right)}^2$$

(14)

where $R$ is the waist radius of the Gaussian laser beam, $\rho$ is the initial laser power density along the beam axis, $t_{max}$ is the time elapsed from the focal point to the maximum displacement. From Equation (12), the surface viscosity can be estimated as:

$$\eta ={\displaystyle \frac{E\mathit{tan}\delta }{\omega }}$$

(15)

where $\delta$ is the phase delay, $E$ is Young’s modulus and $\omega$ is the laser frequency. Then the elasticity and viscosity of the biological tissues can be obtained by collected the rise time of the PA displacement and phase delay of the PA signals.

The theory was validated by imaging of three agar-gelatin phantoms with different concentrations. The PA signals of the phantoms can be seen in Figure 7a. Figure 7b showed that the rise time decreased with increase of agar-gelatin concentration, which revealed a significant increase in elasticity. The relationship between the rise time and amplitude of the PA signals is shown in Figure 7c. Then the displacement data in Figure 7d was calculated compared with the theoretical value. A comparison of the elasticity measurement by PAMR and conventional rheometer was shown in Figure 7e, also PAMR can provide elasticity contrast with the traditional PAI simultaneously as shown in Figure 7f. From Figure 7g, it can be seen that the viscosity increased with the increase in agar-gelatin concentration. The viscosity measured by PAMR and conventional rheometer was shown in Figure 7h. Through the PA lock-in measurement, the phase delay can be obtained to reconstruct the viscoelasticity ratio image. Figure 7i demonstrated the viscoelasticity ratio distribution and the viscosity mapping, and PAMR exhibited a more stark contrast and also illustrated the advantages for multi-parameter extraction.

In addition, to confirm the feasibility of PAMR technique for viscoelastic detection, experiments were performed from cellular levels up to organ levels. Adipocytes and myocytes were imaged to demonstrate the capability of PAMR on the cellular scale, as shown in Figure 8a. Figure 8b showed that adipocytes were much stickier than myocytes. The opposite distribution of elastic modulus and viscosity indicated that these two parameters were independent of each other. Figure 8c showed the PA image, elastography and viscography of the fat, muscle and bone tissues to demonstrate the performance of PAMR technique for tissue characterization. Muscle tissue showed a much stronger PA signal amplitude due to the stronger optical absorption compared to the fat and bone tissues at 1064 nm excitation. The bone tissue showed clear elasticity contrast and the fat tissue is much easier to identify due to its higher viscosity [Figure 8d]. Ex vivo imaging of rat liver and in vivo imaging of mouse liver were performed to show the capability of PAMR on organ level. The elasticity distribution of liver reconstructed by PA elastography matched well with the images obtained from the conventional PA image and the clinically ultrasound elastography [Figure 8e]. Furthermore, as shown in Figure 8f, the mouse liver can be clearly identified by in vivo PA elastography, in which the elasticity distribution coincided well with the liver morphology.

Compared with traditional PA techniques, PAMR can simultaneously measure the viscosity and elasticity of different type cells, tissues and organs. This technique utilized multi-characteristics of PA signals for biomechanical investigation, which may potentially expand its biomedical application in the future.

3. Conclusions and Outlook

Photoacoustic mechanical imaging (PAMI), as one of the important PA technologies in the field of biomedical research, makes the diagnosis data more diverse by analyzing the mechanical properties of tissues, which also makes these techniques has great application potential in the visualization and evaluation of pathological process (

Table 1. Summary of Many Key Features of Photoacoustic Mechanical Imaging Modalities.

Summary of Many Key Features of Photoacoustic Mechanical Imaging Modalities
Technology	Measurement	Mechanism	Lateral Resolution
PA elastography	Elasticity	Strain	119 μm
PA remote sensing elastography	Elasticity	Rise time (Thermoelastic displacement)	10.36 μm
Frequency-resolved PA viscosity measurement	Viscosity	FWHM (Frequency spectrum)	NA
Phase-domain PA viscography	Viscosity	Phase delay	9.7 μm
PA lock-in viscoelasticity measurement	Viscoelasticity ratio	Phase delay	100 μm
PA Microrheology	Elasticity & Viscosity	Rise time & Phase delay	38.5 μm

). Both elasticity and viscosity are important parameters to characterize the mechanical properties of biological tissues. PAMI can provide elasticity and viscosity information on the basis of existing traditional PA imaging, which will give multi-parameter information and make a more comprehensive characterization of the development of different diseases [65,66,67,68,69].

However, there are still some challenges to overcome. For example, PA elastography needs to be carried out under specific compression conditions, and external compression force may cause a certain degree of damage to the tissue. The current linear PA scanning probe also needs to be continuously optimized to improve the measurement accuracy. Elastography can also be achieved by PARSE without compression, but it has limitation in imaging depth. Therefore, the synergistic complementary imaging with these two techniques can greatly expand the application range of PA elasticity characterization. During Frequency-resolved PA viscosity measurement, the attenuation of the ultrasonic sound in propagation will affect the accuracy of the experimental results. In the future, according to the property of the transmission medium, further compensation of the PA signals can effectively improve the detection precision. Furthermore, PAMR technique can only be applied to the elasticity and viscosity detection of the surface from the biological tissues, currently, due to its poor resolution in depth direction, which greatly limited the biomedical application of this technique. By using ultrasonic focusing transducer, PA signals at different depth positions can be extracted for viscoelastic analysis, which will help to realize three-dimensional elastography and viscography. Solution of these problems can greatly promote the development and application of PAMI techniques in biomedical and clinical applications.

Multi-parameter PA characterization will be a major development direction of PA medical diagnosis in the future. PAMI can be combined with other PA functional imaging modalities to provide a variety of functional parameters for pathological diagnosis. For example, PA elastography combined with oxygen-metabolic PA microscopy can provide elasticity and oxygen concentration of hemoglobin for more comprehensive diagnosis and treatment of tumor. PA viscosity measurement can provide viscosity of the blood with the blood flow velocity acquired by PA Doppler transverse blood flow imaging. The applicability of PAMR needs to be expanded more comprehensively, such as the elasticity and viscosity detection and analysis of cancer cells, and the mechanical characterization of atherosclerotic vulnerable plaques, which are all of great significance.

In conclusion, PAMI techniques have opened up new avenues for traditional PA imaging based on optical absorption. In addition to providing tissue elasticity, viscosity and viscoelasticity, it can also provide morphological characteristics and multi-parameter information for the assessment of pathological processes. Due to its non-invasiveness and relative simplicity, PAMI will develop to a unique tool for questioning biomechanical properties in future biomedical and clinical applications.