Spectrum analysis of photoacoustic signals for tissue classification

Parag V. Chitnis; Jonathan Mamou; Ashwin Sampathkumar; Ernest J. Feleppa

doi:10.1117/12.2037929

3 March 2014 Spectrum analysis of photoacoustic signals for tissue classification

Parag V. Chitnis, Jonathan Mamou, Ashwin Sampathkumar, Ernest J. Feleppa

Author Affiliations +

Proceedings Volume 8943, Photons Plus Ultrasound: Imaging and Sensing 2014; 89432J (2014) https://doi.org/10.1117/12.2037929
Event: SPIE BiOS, 2014, San Francisco, California, United States

Abstract

Quantitative ultrasound (QUS) estimates derived from power spectra of pulse-echo signals are sensitive to mi- crostructure and potentially can differentiate among tissues. However, QUS estimates do not provide molecular specificity. We investigated the feasibility of obtaining quantitative photoacoustic (QPA) estimates for sensi- tivity to microstructure and chromophores for tissue classification. QPA methods were tested using gel-based phantoms containing uniformly dispersed, black polyethylene spheres (1E5 particles/ml) with nominal mean diameters of 23.5, 29.5, 42.0, and 58.0 μm. A pulsed, 532-nm laser excited the photoacoustic (PA) response. A single-element, 34-MHz transducer with a 12-mm focal length was raster scanned over the phantom to acquire 3D PA data. Normalized power spectra were generated from the PA signals within 2079, moving (50% overlap), 1-mm-cube regions-of-interest (ROIs) to provide three QPA estimates: 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 in the -6-dB band. EAS was computed by fitting the normalized spectrum in the -20-dB band to the multi-sphere analytical solution. All estimates were correlated with the size of particles dispersed in the phantoms. SS decreased while SI increased with an increase in particle size. EAS was correlated with nominal particle diameter, but particles aggregation and the finite bandwidth of the PAI system resulted in outliers. SS, SI, and EAS for the 23.5-μm-phantom were -0.14±-0.04 dB/MHz, 4.8±1.3 dB, and 25.4±6.3 μm, respectively; the corresponding values for the 58.0-μm phantom were -0.47±-0.03 dB/MHz, 15.6±0.9 dB, and 82.7±0.9 μm.

Citation Download Citation

Parag V. Chitnis, Jonathan Mamou, Ashwin Sampathkumar, and Ernest J. Feleppa "Spectrum analysis of photoacoustic signals for tissue classification", Proc. SPIE 8943, Photons Plus Ultrasound: Imaging and Sensing 2014, 89432J (3 March 2014); https://doi.org/10.1117/12.2037929

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