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Noninvasive Prediction of Renal Stone Surface Irregularities by Numerical Analysis of the Color Doppler Twinkling Artifact: An Ex Vivo Study
Author(s) -
Jamzad Amoon,
Setarehdan Seyed Kamaledin
Publication year - 2018
Publication title -
journal of ultrasound in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 91
eISSN - 1550-9613
pISSN - 0278-4297
DOI - 10.1002/jum.14465
Subject(s) - artifact (error) , medicine , surface roughness , ultrasound , kidney stones , ex vivo , doppler effect , imaging phantom , surface finish , linear regression , intensity (physics) , biomedical engineering , nuclear medicine , radiology , artificial intelligence , optics , in vivo , surgery , mathematics , materials science , computer science , statistics , physics , microbiology and biotechnology , astronomy , biology , composite material
Objectives The physical structures of renal stones are highly correlated with their breakability. Noninvasive estimation of stone roughness will be beneficial for management. The intensity of the twinkling artifact appearing at the site of renal stones on Doppler ultrasound imaging is also influenced by the stone's roughness level. This article proposes a quantitative method for roughness prediction of ex vivo renal stones based on a twinkling analysis of their color Doppler images. Methods Twenty surgically removed renal stones were first spatially modeled by an optical method, and 12 standard roughness measures were extracted from them. Stones were then embedded in an agar‐based phantom and Doppler imaged with a calibrated ultrasound system. The images were preprocessed, and 11 twinkling intensities were measured numerically. The twinkling data along with the roughness labels were then analyzed by multiple linear regressions, and finally, a linear roughness predictor was trained for renal stones. Results The core height measure of roughness had the best linear fit to the twinkling data among other roughness parameters. The results of the multiple linear regression analysis indicated a strong linear relationship between twinkling data and stones' roughness, with an R 2 value of 83.29% and high statistical significance of F (11,868) = 393.36 and P < .001. Conclusions It was possible to predict the core roughness of renal stones using the proposed method and the twinkling artifact data acquired from the color Doppler images ex vivo.