Multiple information extracted from photoacoustic radio-frequency signal and the application on tissue classification

Ultrasonics - Sonochemistry 66 (2020) 105095

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Multiple information extracted from photoacoustic radio-frequency signal and the application on tissue classification ⁎

Wei Ruia,b, Chao Taob, , Xiaojun Liua,

T

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a

Key Laboratory of Modern Acoustics, Department of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China b Shenzhen Research Institute of Nanjing University, Shenzhen 51800, China

A B S T R A C T

Photoacoustic imaging is a hybrid biomedical imaging technique, combining rich optical contrasts and good acoustic resolution in deep tissues. As a noninvasive and nonionized imaging method, photoacoustic imaging has shown great potentials in biomedicine in the past decade. In this review, we give a brief introduction of the physical principle and three major implementations of photoacoustic imaging. Then, we present pictures of some recent progress about the extraction of new imaging parameters from photoacoustic radio-frequency signals. These parameters are highly associated with the tissue microstructure characteristics, including characteristic size, number density, and elasticity. This information could give us insight into various properties of tissue in-depth and be applied to tissue classification for basic research and clinical settings.

1. Introduction Biological tissue classification has always been the subject of intense research since its important applications in biology and medicine. In the past few decades, many imaging techniques have been developed for tissue classification, such as computed tomography, magnetic resonance imaging, positron emission tomography. Optical methods are the most-often used techniques for tissue characterization. Benefitted from the unique optical spectra of atoms and molecules, optical techniques have provided rich molecular information of tissues with high contrast and good specificity [1–3]. Various optical modalities have been investigated and shown great success in biomedical imaging, including two-photon microscopy [1], optical coherence tomography [2], and confocal laser scanning microscopy [3]. Whereas, due to the short wavelength in the spectral range from ultraviolet to near-infrared, optical waves always suffer strong scattering when it propagates through biological tissue. The optical mean free path in most tissues is only about ~1 mm. When beyond this depth, random light scattering will prevent precise optical focusing in deep tissue. Therefore, in most highly scattering tissues, optical imaging methods either have limited imaging depth (up to ~1 mm) or suffer from poor spatial resolution (about one-third of the imaging depth, when the depth is larger than 1 mm) [4,5]. Acoustic waves have a much longer wavelength than optical waves. Therefore, the acoustic scattering in soft tissues is usually much lower (two or three orders smaller) than the optical scattering. As a result, the

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acoustic imaging can achieve a resolution of about 1/200 of the imaging depth in deep tissues, which is much better than the optical methods [5]. Usually, the contrast of conventional acoustic imaging methods, such as B-mode ultrasonography, comes from the mechanical properties (e.g., acoustic impedance) of biological tissues. Whereas, the change of acoustic impedance in soft tissues is insignificant. The contrast of acoustic images is often weaker in comparison to the optical contrasts. Therefore, conventional acoustic methods sometimes are not sensitive to tissue types and functions, as well as the optical modalities. Photoacoustic imaging (PAI) is a hybrid noninvasive biomedical imaging technique based on the excitation of ultrasonic waves within tissues, following the absorption of optical energy with varying intensity [5]. Tissues are illuminated by a pulse laser. Optical absorbers in tissue, e.g., blood vessels, absorb the laser energy and emit ultrasound waves due to the transient thermoelastic expansion. This is the so-called photoacoustic effect [6]. The ultrasound is then detected by the ultrasound transducer arranged around the tissue. Finally, by solving the inverse problem, images with the contrast of the optical absorption can be reconstructed from the detected ultrasound. The energy conversion between light and ultrasound brings several advantages in comparison with fluorescence imaging, optical coherence tomography, and ultrasonography, including the broken through optical diffusion limit, the guarantee of no leakage of excitation photons into detectors, and speckle free. Therefore, PAI combines good acoustic spatial resolution in deep tissue with the rich optical contrasts [5,7]. Moreover, the nonionizing radiation used by the PAI system is much safer than the

Corresponding authors. E-mail addresses: [email protected] (C. Tao), [email protected] (X. Liu).

https://doi.org/10.1016/j.ultsonch.2020.105095 Received 24 January 2020; Received in revised form 15 March 2020; Accepted 23 March 2020 Available online 25 March 2020 1350-4177/ © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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direction perpendicular to the direction of the ultrasound beam. Since the resolution of this kind of PAM is related to the wavelength of detected ultrasound, it is named as acoustic resolution PAM (AR-PAM) [19]. The imaging depth of an AR-PAM can be up to several millimeters. Photoacoustic tomography (PAT) provides an imaging depth up to 6–7 cm with a resolution of 0.88c/Δf, where Δf is the bandwidth for photoacoustic signal detection and is approximately proportional to f0 [20,21]. PAT utilizes an ultrasound array to detect the photoacoustic signals generated by unfocused laser. Its image recovery depends on reconstruction algorithms [13–16]. The spatial resolution and the penetration depth of PAT are scalable with the ultrasonic frequency. These PAI imaging modalities provide multiple scales of imaging depth and resolution. Each atom or molecule corresponds to a specific optical absorption spectrum. Based on this point, PAI has shown great value to classify tissues according to their molecular components. Using pulsed lasers with a different single wavelength or multiple wavelengths, ranging from ultraviolet to near-infrared various PAI systems have successfully image cell nuclei [22], blood vessels [23], oxygen saturation and concentration of hemoglobin [24], lipid and proteins [25,26].

ionizing radiation for biological tissue (e.g., X-ray). Because of these merits, PAI has been developed tremendously and adapted to a variety of biomedical applications, such as osteoarthritis assessment [8], drug delivery monitoring [9], tumor detection [10], vasculature visualization [11]. In this review article, we will briefly introduce the basic principles of PAI. Then, we present a picture of recent studies about the extraction of random microstructure information, including the characteristic size, number density, and elastic parameters, from the photoacoustic radio frequency signal and discuss their application on tissue classification. 2. Principles of photoacoustic imaging A photoacoustic imaging system usually uses a pulse laser to illuminate the biological tissue. Photon absorption makes electrons jump from the ground state to an excited grate. If the laser pulse width is much smaller than the thermal relaxation time, the rapid deposition of laser energy immediately leads to a local pressure increase in the optical absorbers. As a result, the release of the local pressure causes the optical absorber to emit ultrasonic waves, i.e., photoacoustic waves, to the surrounding media. At the position r and time t, the photoacoustic pressure p(r,t) can be predicted as [12],

∇2 p (r, t ) −

1 ∂ ∂τ (t ) p (r, t ) = −p0 (r) with p0 (r0) = Γ(r0) A (r0) c 2 ∂t 2 ∂t

3. Histological information in the radio-frequency signal Besides of biochemical components and molecular information, the microstructure characteristics of biological tissue are also effective indicators of different tissue types. OR-PAM is one candidate to reveal the tissue microstructural characteristics benefitting from its sufficiently high resolution. However, its imaging depth is only ~1 mm for turbid tissues. As a result, OR-PAM is incompetent in examining the microstructure in deep tissue. AR-PAM or PAT has the capability to image tissue structure with acoustic precision in turbid media. Their resolutions mainly depend on the wavelength of the detected signals. Since the ultrasound attenuation in tissue is significantly increased with frequency, high working frequency, i.e., short wavelength, will inevitably decrease the imaging depth. Therefore, due to the randomness and microscales, it is still a challenge to evaluate the microstructure in deep tissue. Recently, a series of work has been reported that multiple information related to tissue microstructure can be extracted from photoacoustic radio-frequency (RF) signals. As a result, these parameters provide new possibilities to classify the tissues according to their microstructures.

(1)

where c is the speed of sound, τ(t) is the waveform of the laser pulse, Γ(r) and A(r) are the distribution of the Grüneisen parameter and the optical absorption coefficient in the region r0 exposed to laser illumination. If the photoacoustic signals are detected by an ultrasound transducer or transducer array, p0(r0) can be calculated to form the image by solving the inverse problem [13–16]. Since p0(r0) is proportional to the optical absorption coefficient A(r0), the recovered photoacoustic images have the contrast of optical absorption. Several PAI modalities have been developed to achieve different spatial resolution and imaging depth, as shown in Fig. 1. Photoacoustic microscopy (PAM) uses a focused laser beam and a spherically focused ultrasound transducer to generate and detect the signal, respectively [17–19]. Images can be formed by scanning the sample point-by-point. When the imaging depth is smaller than the optical mean free path (~1 mm), a PAM can reach a diffraction-limited optically lateral resolution (in the direction perpendicular to the acoustic axis) 0.51λopt/ NA, where NA is the numerical aperture of the optical lens, and λopt is the wavelength of the laser. The optically lateral resolution refers to the resolution in the focal plane of the objective. Since the resolution is determined by the optical focus, this kind of system is named optical resolution PAM (OR-PAM) [17–18]. When imaging depth is beyond 1 mm, optical focusing is poor and the acoustic focus is smaller than the optical one. In this situation, the lateral resolution of a PAM is determined by acoustic focal diameter 0.71 cl/(f0·NAc/2), where NAc, l and f0 are the aperture, the focal length and the central frequency of an ultrasound transducer, respectively. Here, the lateral resolution is defined as the ability of the system to distinguish two points in the

3.1. Characteristic size Characteristic size is one basic property of tissue microstructure. Here, the characteristic size denotes the dimension of the tiny units composing the tissue microstructure, such as the size of cell aggregation [27], the diameter of blood capillary [28], and so on. Since the random nature of tissue microstructure, the waveform of photoacoustic RF signals is always noise-like. Therefore, the relationship between the characteristic size and the RF signal is explored in the spectral domain, instead of analyzing the waveform of the RF signals.

Fig. 1. Implementations of PAI. (a) Photoacoustic tomography [20], (b) acoustical-resolution photoacoustic microscopy (AR-PAM) [19], (c) Optical-resolution photoacoustic microscopy (OR-PAM) [17]. 2

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Fig. 2. Photoacoustic images of gel phantoms containing sub-wavelength microspheres [34]. (a) and (b), Photoacoustic images with the contrast of absorption intensity, where gel inclusions contain (a) 49 μm and (b) 199 μm microspheres, respectively. (c) and (d) Photoacoustic images with the contrast of spectral slope, where gel inclusions contain (c) 49 μm and (d) 199 μm microspheres, respectively.

still available in the low-frequency band of photoacoustic RF signals. The theoretical analysis was soon validated by the experiments using well-controlled phantoms containing optically absorbing microspheres [34–36]. A more interesting finding is that a photoacoustic tomography, using spectral slope as image contrast, can distinguish subwavelength microstructures, therefore, can efficiently classify tissues in the deep according to their different microstructure features, as shown in Fig. 2. [34]. Before long, the theory was extended to microstructure composed of particles with non-uniform sizes [37] or vessel networks [28]. And the similar properties of photoacoustic RF signals were found. The spectral properties of photoacoustic RF signals offer fundamental advantages for addressing a number of practical problems faced by conventional PAI, including the device-independent, quantitative and repeatable measurements of stochastic tissue, and the ability of tissue classification with subwavelength. These merits promise that the spectral parameters could overcome the contradiction between imaging resolution and imaging depth, and provide valuable tools to monitor the characteristic size of random tissue microstructure in the deep. Therefore, the analysis of photoacoustic signals has been found a series of potential applications, including vascular network hyperplasia [28], early dental lesion detection (Fig. 3) [38], liver tissue differentiation [39,40], tumor detection [32], bone examination [41].

Early studies show that the spectral analysis of photoacoustic RF signals could be more effective tools for the characteristics of random tissue [29,30]. Using Monte Carlo simulation and experimental measurements, the spectral properties of photoacoustic signals were found to be related to the erythrocyte aggregation level [27] and the morphology of single red blood cells [31]. Additionally, in ex vivo experiments, significant differences in photoacoustic spectral parameters were observed between cancerous tissue and normal tissue [32]. The theoretical analysis of the relationship between the random microstructure and the photoacoustic spectrum was given later. By assuming tissue microstructure is composed of random distribution particles with a uniform diameter, Yang et al. (2012) theoretically predict the relationship between the characteristic size (particle diameter) and the normalized spectrum [33]:

S (f ) = ϕc−2 (f )

∭ RA (Δr) e jkΔxdxdydz

(2)

where Δx = x' − x, Δr = r' − r, and RA(Δr) = ∫ ∫ ∫ VA(r)A(r')dxdydz is the autocorrelation function of optical absorption function A(r) of particles. Φc(f) is a calibration factor, which can be measured from an object with the known optical absorption distribution. A(r) is a function of particle diameter, Eq. (2) presents the relationship between the spectrum and the characteristic size of the random microstructure. Several basic spectral properties of photoacoustic RF signals have been predicted by Eq. (2): (i) the photoacoustic signals from random tissue have deterministic spectral properties. (ii) the signals from the tissue with the larger characteristic size have narrower bandwidth than those from the tissue with the smaller characteristic size. (iii) The normalized spectral slope separates the effects of system components and tissue properties on image features and delivers system-independent quantitative results [34]. (iv) The spectral discrepancy can be quantified by the slope of the best linear fitting. The slope monotonously decreases with the increase of particle size. Therefore, the spectral slope of photoacoustic RF signals can be used to quantify the characteristic size of tissue microstructure. (v) the above properties are

3.2. Number density Number density is another property of tissue microstructure, which could also serve as an effective indicator of tissue types or diseases. Number density denotes the quantity of the particularly tiny units in unit volume of tissue, e.g., such as the number of microthrombus, cell aggregation [42], blood vessel density [43]. Statistical methods are effective tools to analyze the stochastic signals. It was revealed that the statistical parameters extracted from ultrasound echo have capabilities in detecting liver fibrosis [44], cancellous bone [45], differentiating 3

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Fig. 3. The PAT images of a lesion tooth [38]. The upper row (a) is the specimen with lesions. The middle row [(b), (c), (d)] and lower row [(e), (f), (g)] are photoacoustic images with the contrast of absorption intensity and spectral slope, respectively.

number density. Moreover, the shape parameter m is only related to the number density of tissue microstructure, but independent of the other factor, such as the characteristic size. Based on this factor, it was suggested that the number density of microstructures could be quantified by the Nakagami shape parameter m extracted from photoacoustic signals. Then, combining the Nakagami shape parameter and spectral slope, a photoacoustic tomography modality successfully differentiates phantoms according to their different random structure, as shown in Fig. 4.

benign or malignant tumors [46], assessing breast masses [47]. The envelope histogram of photoacoustic signals generated from a large amount of randomly distributed microspheres can be well fitted by Rayleigh distribution [27]. The statistical parameters or the distribution of photoacoustic envelop histograms can differentiate different tissues [48] or detect melanoma cells mixed in erythrocytes [49]. Recently, a Nakagami statistic was applied to analyze the probability density of envelope R(t) and quantify the number density of random microstructure, that is, [50]:

f (R) =

2mmR2m − 1 m exp ⎛− R2⎞ U (R) H (m)Ωm ⎝ Ω ⎠

3.3. Elastic parameters

(3)

Tissue elasticity is an important biomarker for typing tissues and assessing many diseases, such as prostate cancer [51], atherosclerosis [52], and liver cirrhosis [53]. Many methods have been developed to evaluate the mechanical properties of materials [54–56]. Ultrasound elastography is one noninvasive method widely used in clinics to abnormal elasticity of the tissue [54]. Whereas limited by the wavelength of ultrasound, this method is usually suitable for the tissue with a big size. Resonant ultrasound spectroscopy (RUS) is another often-used technique for elasticity measurement [55]. In RUS systems, excitation and measurement of vibration of samples may be performed with contact transducers or optical methods. Although the measurement process of optical techniques is noncontact, such as a laser-Doppler interferometer, strong scattering of light limits the applicability if

where the signal envelope R can be calculated from the RF signals by using Hilbert Transform, H(·) and U(·) is the Gamma function and the unit step function. The parameters m and Ω determine the shape and scale of the probability density function f(R), respectively. The parameter Ω depends on the intrinsic characteristics of particle clouds and the measurement system, including microsphere size, number density, laser intensity, and system gain. But the parameter m is determined by the distribution shape, which is related to the microstructure characteristics and could be system independent. The optimal parameters m and Ω can be determined by using the least square fit, or can be estimated as Ω = E(R2) and m = [E(R2)]2/E[R2 − E(R2)]2. E(·) represents the mathematical expectation. The following simulation demonstrates that the Nakagami shape parameter m monotonously increases with the 4

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photoacoustic eigen-spectrum technique detects the photoacoustic wave emitted from the vibrating specimen, instead of directly measuring the vibrational surface of the specimen. These merits promise that this method could be more competent for rare, brittle and tiny specimens, or when the specimen is immersed in the turbid or opaque medium.

4. Summary Tissue classification is always an important topic of biomedicine. All the time, there is interest in the application of photoacoustic technology in tissue classification because of its excellent imaging performance in deep tissues. In this mini-review, the physical principle of photoacoustic imaging is introduced and compared to the other imaging technique. Then, we briefly describe three major implementations of PAI including OR-PAM, AR-PAM and PAT, and their capability of tissue imaging and classification. Finally, we focus on the studies to evaluate tissue microstructure characteristics from photoacoustic RF signals. The characteristic size of the microstructure can be extracted from the spectral slope of photoacoustic RF signals. Number density can be evaluated by analyzing the probability density of a photoacoustic envelope. Elasticity can be measured from photoacoustic eigen-spectrum. These methods break through the limitation of the dependence on the frequency and bandwidth of the detected signal of conventional PAI. They could give us insight into various properties of tissue in depth. Many works are still needed to improve these methods before practical biomedical applications. Firstly, it is necessary to perform more in vivo or ex vivo experiments to examine the spectral parameters and statistic parameters for real tissues, especially, for complex tissues mixed with various microstructures. The quantitative relationship between these parameters and the various physiological situation should be explored. Additionally, the photoacoustic eigen-spectrum also has several limitations. For example, the quantitative evaluation of the elasticity relies on some prior-knowledge of the micro-elastomer. It is difficult to detect the photoacoustic eigen-spectrum from the tissue with a high viscosity. Improved photoacoustic eigen-spectrum generation and detection strategies may be utilized for soft biological tissues. In the past decade, the very active researches on PAI have already demonstrated many potential applications in biology and medicine. In the future, it would be expected that more valuable tissue information could be extracted from photoacoustic RF signals. Benefitting from multiple information, PAI as a nonionizing and noninvasive technique will provide more specific imaging techniques for tissue classification and be widely applied from basic research to clinical settings.

Fig. 4. Photoacoustic tomography imaging of the mixture phantom [50]. (a) A brief diagram of the signal measurement and frame cutting method. (b)–(e) Photoacoustic tomography with the imaging contrasts of absorption intensity average energy, spectral slope and Nakagami shape parameter, respectively.

targets are submerged in turbid media. Moreover, this method needs to cut the specimen into a specific shape. Therefore, it is not suitable for turbid live tissues. Atomic force microscopy can provide a high-precision measurement of local elasticity. However, it is restricted to surface or subsurface detection [56]. Recently, a physical phenomenon, named photoacoustic eigenspectrum, was observed from light-absorbing objects [57]. When a light-absorbing micro-elastomer is exposed to a pulse laser, the elastomer will absorb optical energy and emit ultrasound due to thermoacoustic expansion. It is worth noticing that, after the external laser illumination is removed, the elastomer will keep vibrating and emitting ultrasound for a long time owing to its inertia and elasticity, as shown in Fig. 5. It is predicted that the free vibration at this stage is associated with its eigen-mode. And the frequency spectrum of emitted ultrasound can be explained by the eigen-frequencies of the elastomer. That is said the photoacoustic signals hold the eigen-vibration information of the elastomer. Since the eigen-vibration is determined by the elasticity, it was suggested that the elasticity of the elastomer can be estimated from the photoacoustic eigen-spectrum by solving the inverse problem. By using this method, the elasticity of elastomer made by various materials has been successfully measured [57]. Then, this method was applied to monitor the material fatigue according to their elastic changes [58]. In comparison to other methods of elastic measurement, the photoacoustic eigen-spectrum technique has its unique advantages. Firstly, it can probe all elastic constants of the specimen in one test. Secondly, this method is totally noncontact, whether the vibration generation or signal detection. Thirdly, different from laser ultrasound, the

CRediT authorship contribution statement Wei Rui: Writing - original draft, Software, Formal analysis, Investigation, Visualization. Chao Tao: Conceptualization, Methodology, Validation, Resources, Writing - review & editing. Xiaojun Liu: Supervision, Project administration, Writing - review & editing.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment This work was supported by the National Natural Science Foundation of China (11834008 and 11874217). 5

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Fig. 5. Experimental extraction of the eigen-frequencies from photoacoustic signals [[57], Gao]. (a) Experimental setup. (b) Waveform of the detected photoacoustic signal. (c) Time-frequency map of the photoacoustic signal in Fig. 2(b). (d) Normalized power spectral density, where the dashed line and solid line correspond to the head wave and coda wave, respectively.

References [18]

[1] F. Helmchen, W. Denk, Deep tissue two-photon microscopy, Nat. Methods 2 (2005) 932. [2] A.G. Podoleanu, Optical coherence tomography, J. Microsc. 247 (2012) 209–219. [3] R. Rezakhaniha, A. Agianniotis, J.T.C. Schrauwen, A. Griffa, D. Sage, C.V. Bouten, F. Van De Vosse, M. Unser, N. Stergiopulos, Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy, Biomech. Model. Mechanobiol. 11 (2012) 461–473. [4] J. Culver, V. Ntziachristos, M. Holboke, A. Yodh, Optimization of optode arrangements for diffuse optical tomography: a singular-value analysis, Opt. Lett. 26 (2001) 701–703. [5] L.V. Wang, S. Hu, Photoacoustic tomography: in vivo imaging from organelles to organs, Science 335 (2012) 1458–1462. [6] A.G. Bell, The production of sound by radiant energy, Science 2 (1881) 242–253. [7] L.V. Wang, J. Yao, A practical guide to photoacoustic tomography in the life sciences, Nat. Methods 13 (2016) 627–638. [8] J. Xiao, L. Yao, Y. Sun, E.S. Sobel, J. He, H. Jiang, Quantitative two-dimensional photoacoustic tomography of osteoarthritis in the finger joints, Opt. Express 18 (2010) 14359–14365. [9] J.R. Rajian, M.L. Fabiilli, J.B. Fowlkes, P.L. Carson, X. Wang, Drug delivery monitoring by photoacoustic tomography with an ICG encapsulated double emulsion, Opt. Express 19 (2011) 14335–14347. [10] L. Xi, S.R. Grobmyer, L. Wu, R. Chen, G. Zhou, L.G. Gutwein, J. Sun, W. Liao, Q. Zhou, H. Xie, H. Jiang, Evaluation of breast tumor margins in vivo with intraoperative photoacoustic imaging, Opt. Express 20 (2012) 8726–8731. [11] B. Ning, M.J. Kennedy, A.J. Dixon, N. Sun, R. Cao, B.T. Soetikno, R. Chen, Q. Zhou, K. Kirk Shung, J.A. Hossack, S. Hu, Simultaneous photoacoustic microscopy of microvascular anatomy, oxygen saturation, and blood flow, Opt. Lett. 40 (2015) 910–913. [12] I.G. Calasso, W. Craig, G.J. Diebold, Photoacoustic point source, Phys. Rev. Lett. 86 (2001) 3550. [13] M. Xu, L.V. Wang, Universal back-projection algorithm for photoacoustic computed tomography, Phys. Rev. E 71 (2005) 016706. [14] W. Rui, C. Tao, X. Liu, Imaging acoustic sources through scattering media by using a correlation full-matrix filter, Sci. Rep. 8 (2018) 15611. [15] W. Rui, C. Tao, X.J. Liu, Photoacoustic imaging in scattering media by combining a correlation matrix filter with a time reversal operator, Opt. Express 25 (2017) 22840–22850. [16] J. Yin, C. Tao, P. Cai, X. Liu, Photoacoustic tomography based on the Green's function retrieval with ultrasound interferometry for sample partially behind an acoustically scattering layer, Appl. Phys. Lett. 106 (2015) 234101. [17] S. Hu, K. Maslov, L.V. Wang, Second-generation optical-resolution photoacoustic

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

[27] [28]

[29] [30]

6

microscopy with improved sensitivity and speed, Opt. Lett. 36 (7) (2011) 1134–1136. J. Yao, L. Wang, J.-M. Yang, K.I. Maslov, T.T.W. Wong, L. Li, C.-H. Huang, J. Zou, L.V. Wang, High-speed label-free functional photoacoustic microscopy of mouse brain in action, Nat. Methods 12 (2015) 407–410. H.F. Zhang, K. Maslov, G. Stoica, L.V. Wang, Functional photoacoustic microscopy for high-resolution and noninvasive in vivo imaging, Nat. Biotechnol. 24 (2006) 848. X. Wang, Y. Pang, G. Ku, X. Xie, G. Stoica, L.V. Wang, Noninvasive laser-induced photoacoustic tomography for structural and functional in vivo imaging of the brain, Nat. Biotechnol. 21 (2003) 803. L. Li, L. Zhu, C. Ma, L. Lin, J. Yao, L. Wang, K. Maslov, R. Zhang, W. Chen, J. Shi, L.V. Wang, Single-impulse panoramic photoacoustic computed tomography of small-animal whole-body dynamics at high spatiotemporal resolution, Nat. Biomed. Eng. 1 (2017) 0071. D.-K. Yao, K. Maslov, K.K. Shung, Q. Zhou, L.V. Wang, In vivo label-free photoacoustic microscopy of cell nuclei by excitation of DNA and RNA, Opt. Lett. 35 (2010) 4139–4141. M. Toi, Y. Asao, Y. Matsumoto, H. Sekiguchi, A. Yoshikawa, M. Takada, M. Kataoka, T. Endo, N. Kawaguchi-Sakita, M. Kawashima, Visualization of tumor-related blood vessels in human breast by photoacoustic imaging system with a hemispherical detector array, Sci. Rep. 7 (2017) 41970. C.L. Bayer, B.J. Wlodarczyk, R.H. Finnell, S.Y. Emelianov, Ultrasound-guided spectral photoacoustic imaging of hemoglobin oxygenation during development, Biomed. Opt. Express 8 (2017) 757–763. P. Wang, P. Wang, H.-W. Wang, J.-X. Cheng, Mapping lipid and collagen by multispectral photoacoustic imaging of chemical bond vibration, J. Biomed. Opt. 17 (2012) 096010. J. Shi, T.T.W. Wong, Y. He, L. Li, R. Zhang, C.S. Yung, J. Hwang, K. Maslov, L.V. Wang, High-resolution, high-contrast mid-infrared imaging of fresh biological samples with ultraviolet-localized photoacoustic microscopy, Nat. Photonics 13 (2019) 609–615. R.K. Saha, M.C. Kolios, A simulation study on photoacoustic signals from red blood cells, J. Acoustical Soc. Am. 129 (2011) 2935–2943. X. Gao, C. Tao, X. Wang, X. Liu, Quantitative imaging of microvasculature in deep tissue with a spectrum-based photo-acoustic microscopy, Opt. Lett. 40 (2015) 970–973. F.L. Lizzi, E.J. Feleppa, S. Kaisar Alam, C.X. Deng, Ultrasonic spectrum analysis for tissue evaluation, Pattern Recogn. Lett. 24 (2003) 637–658. R.E. Kumon, K. Olowe, A.L. Faulx, F.T. Farooq, V.K. Chen, Y. Zhou, R.C.K. Wong, G.A. Isenberg, M.V. Sivak, A. Chak, C.X. Deng, EUS spectrum analysis for in vivo characterization of pancreatic and lymph node tissue: a pilot study, Gastrointest. Endosc. 66 (2007) 1096–1106.

Ultrasonics - Sonochemistry 66 (2020) 105095

W. Rui, et al.

[31] E.M. Strohm, E.S.L. Berndl, M.C. Kolios, Probing red blood cell morphology using high-frequency photoacoustics, Biophys. J. 105 (2013) 59–67. [32] R.E. Kumon, C.X. Deng, X.D. Wang, Frequency-domain analysis of photoacoustic imaging data from prostate adenocarcinoma tumors in a murine model, Ultrasound Med. Biol. 37 (2011) 834–839. [33] Y.Q. Yang, S.H. Wang, C. Tao, X.D. Wang, X.J. Liu, Photoacoustic tomography of tissue subwavelength microstructure with a narrowband and low frequency system, Appl. Phys. Lett. 101 (2012) 034105. [34] S. Wang, C. Tao, Y. Yang, X. Wang, X. Liu, Theoretical and experimental study of spectral characteristics of the photoacoustic signal from stochastically distributed particles, IEEE Trans. Ultrason. Ferroelectr. Freq. Control 62 (2015) 1245–1255. [35] G. Xu, I.A. Dar, C. Tao, X.J. Liu, C.X. Deng, X.D. Wang, Photoacoustic spectrum analysis for microstructure characterization in biological tissue: A feasibility study, Appl. Phys. Lett. 101 (2012) 221102. [36] S.H. Wang, C. Tao, X.D. Wang, X.J. Liu, Quantitative detection of stochastic microstructure in turbid media by photoacoustic spectral matching, Appl. Phys. Lett. 102 (2013) 114102. [37] S. Wang, C. Tao, X. Gao, X. Wang, X. Liu, Quantitative photoacoustic examination of abnormal particles hidden in a mixture of particles with non-uniform sizes, Opt. Express 23 (2015) 32253–32260. [38] R. Cheng, J. Shao, X. Gao, C. Tao, J. Ge, X. Liu, Noninvasive assessment of early dental lesion using a dual-contrast photoacoustic tomography, Sci. Rep. 6 (2016) 21798. [39] G. Xu, Z.X. Meng, J.D. Lin, J. Yuan, P.L. Carson, B. Joshi, X.D. Wang, The functional pitch of an organ: quantification of tissue texture with photoacoustic spectrum analysis, Radiology 271 (2014) 248–254. [40] G. Xu, Z. Meng, J. Lin, C. Deng, P. Carson, J. Fowlkes, C. Tao, X. Liu, X. Wang, High resolution physio-chemical tissue analysis: towards non-invasive in vivo biopsy, Sci. Rep. 6 (2016) 16937. [41] T. Feng, J.E. Perosky, K.M. Kozloff, G. Xu, Q. Cheng, S. Du, J. Yuan, C.X. Deng, X. Wang, Characterization of bone microstructure using photoacoustic spectrum analysis, Opt. Express 23 (2015) 25217–25224. [42] J.K. Armstrong, R.B. Wenby, H.J. Meiselman, T.C. Fisher, The hydrodynamic radii of macromolecules and their effect on red blood cell aggregation, Biophys. J. 87 (6) (2004) 4259–4270. [43] X. Zhang, X. Qian, C. Tao, X. Liu, In Vivo imaging of microvasculature during anesthesia with high-resolution photoacoustic microscopy, Ultrasound Med. Biol. 44 (2018) 1110–1118. [44] P.H. Tsui, M.C. Ho, D.I. Tai, Y.H. Lin, C.Y. Wang, H.Y. Ma, Acoustic structure

[45]

[46]

[47]

[48] [49] [50]

[51]

[52]

[53]

[54] [55] [56]

[57]

[58]

7

quantification by using ultrasound Nakagami imaging for assessing liver fibrosis, Sci. Rep. 6 (2016) 33075. C. Liu, R. Dong, B. Li, Y. Li, F. Xu, D. Ta, W. Wang, Ultrasonic backscatter characterization of cancellous bone using a general Nakagami statistical model Chin, Phys. B. 28 (2) (2019) 024302. P.H. Tsui, C.K. Yeh, Y.Y. Liao, C.C. Chang, W.H. Kuo, K.J. Chang, C.N. Chen, Ultrasonic Nakagami imaging: a strategy to visualize the scatterer properties of benign and malignant breast tumors, Ultrasound Med. Biol. 36 (2) (2010) 209–217. P.M. Shankar, V.A. Dumane, J.M. Reid, V. Genis, F. Forsberg, C.W. Piccoli, B.B. Goldberg, Classification of ultrasonic B-mode images of breast masses using Nakagami distribution, IEEE Trans. Ultrason. Ferroelectr. Freq. Control 48 (2001) 569–580. E. Hysi, D. Dopsa, M.C. Kolios, Photoacoustic tissue characterization using envelope statistics and ultrasonic spectral parameters, Proc. SPIE 8943 (2014) 89432E. R.K. Saha, Computational modeling of photoacoustic signals from mixtures of melanoma and red blood cells, J. Acoust. Soc. Am. 136 (4) (2014) 2039–2049. X. Gao, N. Dai, C. Tao, X. Liu, Quantification of number density of random microstructure from noise-like photoacoustic signal by using Nakagami statistics, Opt. Lett. 44 (13) (2019) 2951–2954. K. Hoyt, B. Castaneda, M. Zhang, P. Nigwekar, P.A. di Sant'Agnese, J.V. Joseph, J. Strang, D.J. Rubens, K.J. Parker, Tissue elasticity properties as biomarkers for prostate cancer, Cancer Biomarkers 4 (2008) 213–225. T. Hirai, S. Sasayama, T. Kawasaki, S. Yagi, Stiffness of systemic arteries in patients with myocardial infarction. A noninvasive method to predict severity of coronary atherosclerosis, Circulation 80 (1989) 78–86. J. Foucher, E. Chanteloup, J. Vergniol, L. Castera, B. Le Bail, X. Adhoute, J. Bertet, P. Couzigou, V. de Ledinghen, Diagnosis of cirrhosis by transient elastography (FibroScan): a prospective study, Gut 55 (2006) 403–408. J.-L. Gennisson, T. Deffieux, M. Fink, M. Tanter, Ultrasound elastography: principles and techniques, Diagn. Intervent. Imag. 94 (2013) 487–495. J. Maynard, Resonant ultrasound spectroscopy, Phys. Today 49 (1996) 26–31. E.K. Dimitriadis, F. Horkay, J. Maresca, B. Kachar, R.S. Chadwick, Determination of elastic moduli of thin layers of soft material using the atomic force microscope, Biophys. J. 82 (2798) (2002) 2798–2810. X. Gao, C. Tao, X. Liu, X. Wang, Photoacoustic eigen-spectrum from light-absorbing microspheres and its application in noncontact elasticity evaluation, Appl. Phys. Lett. 110 (2017) 054101. X. Gao, C. Tao, R. Zhu, X. Liu, Noninvasive low-cycle fatigue characterization at high depth with photoacoustic eigen-spectrum analysis, Sci. Rep. 8 (2018) 7751.