A multitaper Generalized Spectrum technique for detection of periodic structures in tissue: Comparison with conventional methods

Quantifying features related to acoustic scatterer periodicity can provide useful information to monitor tissue structural changes, but their detection is hindered by apparent coherence from random scatterers. This work compares the use a multitaper Generalized Spectrum (mtGS) to single-taper and time-average approaches (stGS and taGS, respectively) and to the Singular Spectrum Analysis (SSA) for detecting periodicity in backscattered echo signals when reducing the size of the parameter estimation region. A phantom with diffuse scatterers and an array of 0.1mm-diameter nylon fibers 0.4mm apart was scanned with a Siemens S2000 system using a linear array transducer. Radiofrequency (RF) echo signals from the fiber plane were obtained and Generalized Spectrum (GS) estimates were made either by stGS, taGS or mtGS with Discrete Prolate Spheroidal Sequences. Spectral components corresponding to periodic structures were identified by peaks in the GS Collapsed Average. SSA was implemented by obtaining eigenvalues and eigenvectors of the autocovariance matrix of signal segments. The periodic components of envelope signals were reconstructed using pairs of eigenvectors with similar eigenvalues. The frequency of the periodic component was estimated from the maximum value of its power spectrum. Histograms of frequency components detected by each method were constructed. The conspicuity of the 1.9MHz peak (corresponding to the fiber spacing) was measured as the size of the parameter estimation region was reduced axially and laterally from 20 to 2 correlation lengths. The mtGS improves detection of the relevant frequency components (1.9MHz and its harmonic) compared to stGS, taGSm and SSA by increasing their conspicuity over spurious components. This method also provided the minimum parameter estimation region size (8 pulse lengths axially, 6 uncorrelated scanlines laterally) viable for detection of periodic features.