Claudio Simon

Claudio Simon

Dept. of Electrical Engineering and Computer Science

University of Michigan

Thursday, February 13, 1997
4:30-5:30 pm
1003 EECS

Parametric Ultrasonic Imaging: Mean Scatterer Spacing Estimation

Abstract -
Ultrasonic tissue characterization techniques offer the promise of non-invasive discrimination between normal and diseased tissue in real-time by using existing imaging technology and appropriate digital signal processing. Different parameters extracted from the processed ultrasound echo data, such as integrated backscatter, attenuation, reflection coefficients, scatterer distribution, and mean scatterer spacing have been considered in the literature, with varying degrees of success. Mean scatterer spacing (MSS) has been recognized as a tool for tissue characterization for certain kinds of tissues and diseases. Furthermore, it has recently been shown that MSS is a valuable tool for non-invasive estimation of temperature change caused by an externally applied heating field. Most of the MSS estimation algorithms in the literature either are not robust in the presence of irregularities in the scatterer distribution, or are computationally demanding and cannot be used in real-time. We present a computationally efficient and robust algorithm which uses the magnitude and phase information of the ultrasound backscattered echo spectra to estimate the MSS. This algorithm exploits the spectral redundancy present in the echo signal by generating spectral lines through a non-linear (quadratic) transformation of the RF echo signal. Results of simulations comparing the performance of the proposed algorithm and previous approaches from the literature are presented to demonstrate the robustness of the proposed algorithm. Experiments involving phantoms and in vitro tissue samples are also presented. The feasibility of implementing a real-time MSS imaging system based on the proposed method is discussed.

Biosketch -
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