Spectrum analysis of photoacoustic signals for characterizing tissue microstructure

This study investigated the feasibility of deriving quantitative estimates from photoacoustic-image data that are sensitive to tissue microstructure. Experiments were conducted using four types of gel-based phantoms (1×1×2 cm) containing uniformly dispersed, black polyethylene spheres (1E5 particles/ml) that had nominal mean diameters of 23.5, 29.5, 42, or 58 μm. A pulsed, 532-nm laser excited the photoacoustic (PA) response. A 33-MHz, F2 transducer with a 12.5-mm focal length was raster scanned over the phantoms to acquire 3D PA data. PA signals were processed using rectangular-cuboid regions-of-interests (ROIs) to yield three quantitative photoacoustic (QPA) estimates associated with tissue microstructure: spectral slope (SS), spectral intercept (SI), and effective-absorber size (EAS). SS and SI were computed using a linear-regression approximation to the normalized spectrum. EAS was computed by fitting the normalized spectrum to the multi-sphere analytical solution. The 3D image volume was divided into 2079 ROIs that had a 50% overlap. The SS decreased and the SI increased with an increase in particle size. While EAS also was correlated with the nominal particle size, aggregation of spheres during phantom preparation resulted in EAS estimates that were approximately a factor of two higher than the nominal size. ANOVA indicated that the means of all three QPA estimates acquired from the four phantoms were statistically different (p<;0.05).