Mean scatterer spacing (MSS) has been recognized to be a useful tool for tissue characterization. Most of the work in this area either uses the amplitude or the phase information of the spectrum of the backscattered ultrasound echo to estimate the MSS. Simulations have shown that the latter approach is more robust in the presence of irregularities in the scatterer distribution. However, most of the algorithms based on the phase information of the spectrum 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 spectrum to estimate the MSS. This algorithm exploits the spectral redundancy present in the backscattered echo signal by generating spectral lines through a nonlinear (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.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control|
|State||Published - 1997|
Bibliographical noteFunding Information:
Manuscript received September 5, 1996; accepted February 25, 1997. This work was funded in part by National Science Foundation Young Investigator Award ECS 9358301 and in part by Grant CA66602 from the National Institutes of Health.
The authors thank Osama Haddadin for helpful discussions and continuous encouragement, Ramon Erkamp for helping with the data acquisition system, and the anony- mous reviewers for their comments and suggestions. The first author thanks CAPES (Brazil) for partially supporting him in his graduate studies at the University of Michigan. We thank ATL for providing the HDI-Ultramark 9 real-time imaging system used in these studies.