Photoacoustic Speckle and Spectral analysis of Vasculature Trees

Abstract

This paper discusses the use of ultrasound tissue characterization methods to photoacoustic (PA) data to differentiate between tissues with different vasculature structures. The dominant source responsible for ultrasound scattering in soft tissues (including tumors) is not well known in pulse echo ultrasound. PA imaging is based on the detection of ultrasound waves generated by optical absorption. Since the hemoglobin in blood dominates the absorption of light in soft tissues (at particular laser wavelengths), the PA signal is dominated by the contribution of the vasculature. Organized and chaotic vasculature trees were simulated using a fractal model to represent normal and tumor vasculature, respectively. The generated PA signals from these vascular were simulated through the solution of the photoacoustic wave equation using the Green’s function approach. Ultrasound resolution PA signals generated were detected by simulating a 256 element transducer with a 10-50 MHz bandwidth. Reconstructed images were acquired using a delay and sum method. Image analysis was performed by fitting the image probability density functions (PDF) of Rayleigh, Nakagami (NG), and Generalized Gamma (GG) distributions to the histogram of the reconstructed images. The speckle sizes of the reconstructed PA images were calculated using autocovariance. Spectrum analysis of the ultrasound frequency components of the photoacoustic signals was performed by linear regression analysis of the power spectrums of the radiofrequency (RF) data, as done in conventional ultrasound tissue characterisation. The results suggest that the Rayleigh, NG distributions, and the power spectrum regression analysis can be used to differentiate between the simulated normal and tumor vasculatures. The changes in these parameters correlate to a higher density of tumor vasculatures than normal vasculatures.