Photoacoustic spectrum analysis for microstructure characterization in biological tissue: A feasibility study

Abstract

This study investigates the feasibility of characterizing the microstructures within a biological tissue by analyzing the frequency spectrum of the photoacoustic signal from the tissue. Hypotheses are derived from theoretical analyses on the relationships between the dimensions/concentrations of the photoacoustic sources within the region-of-interest and the linear model fitted to the power spectra of photoacoustic signals. The hypotheses are validated, following the procedures of ultrasound spectrum analysis, by simulations and experiments with phantoms fabricated by embedding the polyethylene microspheres in porcine gelatin, indicating that photoacoustic spectrum analysis could be a potential tool for characterizing microstructures in biological samples. Photoacoustic (PA) imaging is a non-invasive modality that physically combines the high resolution of ultrasonography and the functional contrast of optical imaging. Although, in some cases, the macroscopic transitions of tissue optical properties in PA images can be evaluated through visual observation, the less significant contrast fluctuations within each seemingly homogeneous region is usually ignored. These small signal fluctuations, excluding the system noises, actually encode the dimensions and optical absorption contrasts of the microstructures within the imaged domain. In former research, the extraction and visualization of the microstructure information from ultrasound (US) signals has been extensively investigated using the methods of spectrum analysis. US spectrum analysis has shown promise in the detection and characterization of cancer 1,2 as well as diseased tissues in liver 3 and blood vessel. 4 The principle of US spectrum analysis is to characterize the acoustic scattering properties of the microstructures within the region of interest (ROI) by observing several key factors, e.g., slope, intercept, and midband fit, of the linear models fitted to the truncated signal power spectra within a predetermined frequency interval. 5 The utilization of Linear model is due to the fact that the spectra of US signals in dB usually monotonically increase or decrease following quasi-linear shapes.3 Former studies to validate the capability of US spectrum analysis in quantifying the dimensions and concentrations of acoustic back-scatterers within biological tissues indicated that the slopes of the linear models reflect the dimensions of the ultrasonic scatterers, and the intercepts encode both the dimensions and concentrations of the scatterers within the ROI. [6][7][8]

Characterizing cellular morphology by photoacoustic spectrum analysis with an ultra-broadband optical ultrasonic detector

“…Aiming at quantitative imaging and tissue characterization, a technique termed 'photoacoustic spectral analysis (PASA)' has been developed. Similar to ultrasound spectral analysis (USSA), PASA uses the slope, mid-band fit, and intercept of the linear regression model to characterize the main features of the signal power spectrum [4]. PASA is based on the fact that the frequency components of the PA signals are closely correlated to the morphological properties of optically absorbing objects in tissues, including their sizes, shapes, and densities [4,10].…”

“…Similar to ultrasound spectral analysis (USSA), PASA uses the slope, mid-band fit, and intercept of the linear regression model to characterize the main features of the signal power spectrum [4]. PASA is based on the fact that the frequency components of the PA signals are closely correlated to the morphological properties of optically absorbing objects in tissues, including their sizes, shapes, and densities [4,10]. For example, smaller objects generate shorter PA signals in the time domain, which leads to broader power spectra containing more high frequency components than the PA signals from larger objects.…”

“…A majority of previous studies on PAI were focused on measurements of photoacoustic (PA) signal amplitude which is related to the overall optical absorption of the target tissue. Recent studies have demonstrated that the power spectrum of the broadband radio frequency (RF) PA signal contains the micro-structural information of the tissue [1][2][3][4][5][6][7][8][9]. Aiming at quantitative imaging and tissue characterization, a technique termed 'photoacoustic spectral analysis (PASA)' has been developed.…”

Quantifying Gleason scores with photoacoustic spectral analysis: feasibility study with human tissues

“…The power spectra derived from all sliding steps and all sensors for the same Gleason pattern were averaged to formulate smooth power spectra representing the microscopic architecture of each Gleason pattern. The frequency dependent attenuation was calculated by the method presented in our previous studies [44,45]. As illustrated by the red dashed line in Fig.…”

“…Frequency domain analysis of PA measurements, namely PA spectral analysis (PASA) [44,45], has demonstrated the potential of assessing microscopic features in biological tissue [46][47][48][49][50]. PASA follows the framework of QUS, as described in detail in our previous publications [44,45].…”