A comparison of autoregressive spectral estimation algorithms and order determination methods in ultrasonic tissue characterization

摘要

Several autoregressive (AR) methods for spectral estimation were applied toward the task of estimating ultrasonic backscatter coefficients from small volumes of tissue. Data were acquired from a homogeneous tissue-mimicking phantom and from a normal human liver in vivo. AR methods performed better at short record lengths than the traditional DFT (discrete Fourier Transform) approach. The DFT method consistently underestimated backscatter coefficients at small gate lengths. Burg's algorithm, the Modified Covariance algorithm, and the Recursive Maximum Likelihood Estimation algorithm performed comparably. The Yule-Walker algorithm did not perform as well as these but offered a slight improvement over the DFT. Several order determination methods were tested. These included residual variance (RV), final prediction error (FPE), Akaike information criterion (AIC), and Minimum Description Length (MDL). The AIC and MDL produced misleading results at higher orders. The RV and FPE yielded better results. The autoregressive method offers promise for enhanced spatial resolution and accuracy in ultrasonic tissue characterization and nondestructive evaluation of materials.