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Analysis of Crawling Waves and Estimation of Tissue Elasticity

Liwei An

Advisor : Prof. Kevin Parker

CSB 426

Abstract

The focus of the study is developing signal and imaging processing protocols to obtain and improve the estimation of local tissue elasticity by the Crawling Wave (CrW) sonoelastography method. The task includes two parts: one is to evaluate the frequency dependence, the lesion size bias, and the spatial resolution of the local estimator and to find the optimal estimator parameters; the other is to reduce noise and to improve the contrast of the elasticity map. First of all, a model of crawling waves in the presence of possible artifacts and noise was established and simulated to assist understanding the phenomenon and developing suitable signal and imaging processing algorithms. Secondly, the protocol of the CrW approach was tested on heterogeneous elastic phantoms as a model of prostate cancers. Then the contrast-to-noise ratio of the estimation was calculated iteratively with various estimator parameters and various sequences of algorithms to determine the optimal signal processing settings. Lastly, the optimized signal processing was applied to ex vivo prostate cancer detection. The comparison of the segmented elasticity map; and the histology tumor outline was made by quadrants to evaluate the diagnostic performance of the protocol. Furthermore the CrW approach was combined with amplitude sonoelastography to achieve a higher specificity. The results of this study demonstrated the feasibility of the proposed approach for clinical applications. The system response of the approach was described and characterized byt eh study of tissue-mimicking phantoms. IN the application to ex vivo prostate cancer detection, the established approach was tested on 43 excised prostate glands. The combination of the CrW approach and pseudo-sonoelastography achieved an accuracy of over 80% for finding tumors larger than 4 mm in diameter. The elasticity values and contrast found by the CrW approach were in agreement with the previous findings by mechanical testing. This correspondence further supports the ability of the CrW approach to detect prostate cancer.