Autoregressive Signal Processing Applied to High-Frequency Acoustic Microscopy of Soft Tissues

Quantitative acoustic microscopy (QAM) at frequencies exceeding 100 MHz has become an established imaging tool to depict acoustical and mechanical properties of soft biological tissues at microscopic resolutions. In this study, we investigate a novel autoregressive (AR) model to improve signal processing and parameter estimation and to test its applicability to QAM. The performance of the AR model for estimating acoustical parameters of soft tissues (i.e., acoustic impedance, speed of sound, and attenuation) was compared to the performance of the Hozumi model using simulated ultrasonic QAM signals and using experimentally measured signals from thin (i.e., 12 and 6 μm) sections of human lymph-node and pig-cornea tissue specimens. Results showed that the AR and Hozumi methods performed equally well (i.e., produced an estimation error of 0) in signals with low, linear attenuation in the tissue and high impedance contrast between the tissue and the coupling medium. However, the AR model outperformed the Hozumi model in estimation accuracy and stability (i.e., parameter error variation and number of outliers) in cases of 1) thin tissue-sample thickness and high tissue-sample speed of sound, 2) small impedance contrast between the tissue sample and the coupling medium, 3) high attenuation in the tissue sample, and 4) nonlinear attenuation in the tissue sample. Furthermore, the AR model allows estimating the exponent of nonlinear attenuation. The results of this study suggest that the AR model approach can improve current QAM by providing more reliable, quantitative, tissue-property estimates and also provides additional values of parameters related to nonlinear attenuation.