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  • Title: A novel approach for quantification and analysis of the color Doppler twinkling artifact with application in noninvasive surface roughness characterization: an in vitro phantom study.
    Author: Jamzad A, Setarehdan SK.
    Journal: J Ultrasound Med; 2014 Apr; 33(4):597-610. PubMed ID: 24658939.
    Abstract:
    OBJECTIVES: The twinkling artifact is an undesired phenomenon within color Doppler sonograms that usually appears at the site of internal calcifications. Since the appearance of the twinkling artifact is correlated with the roughness of the calculi, noninvasive roughness estimation of the internal stones may be considered as a potential twinkling artifact application. This article proposes a novel quantitative approach for measurement and analysis of twinkling artifact data for roughness estimation. METHODS: A phantom was developed with 7 quantified levels of roughness. The Doppler system was initially calibrated by the proposed procedure to facilitate the analysis. A total of 1050 twinkling artifact images were acquired from the phantom, and 32 novel numerical measures were introduced and computed for each image. The measures were then ranked on the basis of roughness quantification ability using different methods. The performance of the proposed twinkling artifact-based surface roughness quantification method was finally investigated for different combinations of features and classifiers. RESULTS: Eleven features were shown to be the most efficient numerical twinkling artifact measures in roughness characterization. The linear classifier outperformed other methods for twinkling artifact classification. The pixel count measures produced better results among the other categories. The sequential selection method showed higher accuracy than other individual rankings. The best roughness recognition average accuracy of 98.33% was obtained by the first 5 principle components and the linear classifier. CONCLUSIONS: The proposed twinkling artifact analysis method could recognize the phantom surface roughness with average accuracy of 98.33%. This method may also be applicable for noninvasive calculi characterization in treatment management.
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