Theoretical and experimental study of spectral characteristics of the photoacoustic signal from stochastically distributed particles

Abstract

Photoacoustic imaging is an emerging technique which inherits the merits of optical imaging and ultrasonic imaging. However, classical photoacoustic imaging mainly makes use of the time-domain parameters of signals. In contrast to previous studies, we theoretically investigate the spectral characteristics of the photoacoustic signal from stochastic distributed particles. The spectral slope is extracted and used for describing the spectral characteristics of the photoacoustic signal. Both Gaussian and spherical distributions of optical absorption in particles are considered. For both situations, the spectral slope is monotonically decreased with the increase of particle size. In addition, the quantitative relationship between the spectral slope and the imaging system factors, including the laser pulse envelope, directivity of ultrasound transducer, and signal bandwidth, are theoretically analyzed. Finally, an idealized phantom experiment is performed to validate the analyses and examine the instrument independent of the spectral slope. This work provides a theoretical framework and new experimental evidence for spectrum analysis of the photoacoustic signal. This could be helpful for quantitative tissue evaluation and imaging based on the spectral parameters of the photoacoustic signal.

Insights into photoacoustic speckle and applications in tumor characterization

“…These results suggest that the PA SS can be used to monitor changes in size without resolving absorbing structures. This is in agreement with previous PA studies at lower ultrasonic frequencies [58]. Fig.…”

Photoacoustic signal characterization of cancer treatment response: Correlation with changes in tumor oxygenation

“…In the past five years, several groups have employed the use of PA radiofrequency analysis to differentiate prostate adenocarcinoma tumors from normal tissue [31], detect the presence of red blood cell aggregation [30], [32], identify various stages of liver disease [29], [33] and characterize bone and joint microstructure in osteoporosis [34] and rheumatoid arthritis [35]. Theoretical models have also been developed for understanding the spectral features that arise from tissue microstructural changes [36], [37], [38], [39], [40], [41], [42]. Combining functional aspects of PA imaging (based on optical spectroscopy) with structural information (based on RF frequency analysis) can potentially be used to better monitor cancer treatment response.…”

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

“…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. Quantitative analysis of the PA signals in the frequency domain would enable an objective evaluation of the morphologies of optically absorbing objects stochastically distributed in biological tissues [3,11]. PASA offers solutions to a number of practical issues faced by conventional PAI.…”