Photoacoustic spectral analysis (PASA), i.e., systemically analyzing the power spectrum of photoacoustic (PA) radio-frequency signals, has demonstrated the capability of characterizing the histological microstructures in biological tissues. The purpose of this study is to theoretically validate the method of PASA
. The analytical solution to the power spectrum of PA signals generated by identical microspheres following discrete uniform random distribution in space was derived. The power spectrum was decomposed into several independent factors, of which the explicit analytical expressions were individually derived. The analytical expressions in combination allow the quantification of the power spectrum profiles and the PASA parameters. The simulation and experiment validation of analytical solution include: 1) the power spectrum profile of a single microsphere with a diameter of 300 μm; and 2) the PASA parameters of the PA signals generated by randomly distributed microspheres of diameters of 100, 200, 300, 400 and 500 μm, and at concentrations of 30, 60, 120, 240, 480 per 1.53 cubic centimeter in the observation range of [0.5, 13 MHz].
The single microsphere experiment confirmed our hypothesis that the periodical fluctuations of the PA signal power spectrum can be used to quantify the dimension of the microspheres. The multiple microsphere experiment validated that the PASA could quantify the dimensions and concentrations of the optical absorbers. In addition, slope, in comparison with other PASA parameters, is more robust as it is less affected by the uncertainties brought in by the optical illumination.
Quantitative detection of stochastic microstructure in turbid media remains a challenge to both optical and acoustical observation. A method of photoacoustic spectral matching is proposed to solve this problem. This method allows us to quantitatively detect the characteristic dimension of stochastic microstructures using a long wavelength. Using a working wavelength of about 375 μm, we accurately measure the dimensions (49, 94.8, and 199 μm) of particles hidden in turbid phantoms. Since stochastic microstructures composed of particles commonly appear in tissue, this method might provide an insight into the physiological and pathological processes deep within organisms.
We explored the potential of an emerging laser-based technology, photoacoustic imaging (PAI), for bladder cancer
diagnosis through high resolution imaging of microvasculature in the interior bladder tissues. Images of ex vivo canine
bladders demonstrated the excellent ability of PAI to map three-dimensional microvasculature in optically scattering
bladder tissues. By comparing the results from human bladder specimens affected by cancer to those from the normal
control, the feasibility of PAI in differentiating malignant from benign bladder tissues was explored. The reported
distinctive morphometric characteristics of tumor microvasculature can be seen in the images from cancer samples,
suggesting that PAI may allow in vivo assessment of neoangiogenesis that is closely associated with bladder cancer
generation and progression. By presenting subsurface morphological and physiological information in bladder tissues,
PAI, when performed in a similar way to that in conventional endoscopy, provides an opportunity for improved
diagnosis, staging and treatment guidance of bladder cancer.
The characteristic microstructures in biological tissues could be used to differentiate tissue types, such as tumor vs.
normal tissue. The spatial resolution of classical photoacoustic tomography (PAT) mainly depends on the wavelengths of
the detected ultrasonic signals. In order to present the very detailed microstructures in a biological sample, the receiving
bandwidth of the PAT system needs to be extremely wide. Another challenge in detecting the high frequency signals
associated with microstructures is the strong acoustic attenuation which increases quadratically with ultrasound
frequency.
In this study, we propose a novel photoacoustic spectral analysis (PSA) technique which evaluates the microstructures in
tissues by analyzing the spectral parameters of detected photoacoustic signals. Experimental result verified that, using a
limited 1-5 MHz working bandwidth, PSA could effectively differentiate two melanoma-mimicking phantoms
containing different microstructures (49 μm and 199 μm absorber sizes respectively). In comparison, since the physical
scales of the microstructures are too small and beyond the spatial resolution of the PAT system, classical tomographic
imaging could not differentiate the two phantoms. The findings from this study suggest that the proposed PSA technique
could help distinguish different tissue types, by evaluating the characteristic microstructures in tissues, without relying
on the detection of high frequency signals which is extremely challenging when the target object is deep.
This study investigates the feasibility of characterizing the microstructures within a biological tissue by analyzing the frequency spectrum of the photoacoustic signal from biological tissue. Hypotheses are derived from theoretical analyses on the relationship between the dimensions as well as concentrations of the optical absorbing sources within the region-of- interest and the linear model fitted to the power spectra of photoacoustic signals. The hypotheses are afterwards validated, following the procedures of ultrasound spectrum analysis, by simulations and experiments with phantoms fabricated by embedding the optically absorbing polyethylene microspheres in porcine gelatin. Quantitatively comparable simulation and experiment results substantiated that photoacoustic spectrum analysis could be a potential tool for characterizing microstructures and optical properties in biological samples.
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