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Published on: December 5, 2020
Influence of Ultrasound System and Gain on Grayscale Median Values
Catherine N Steffel1, Roger Brown2, Claudia E Korcarz3
1Department of Medical Physics, University of Wisconsin Atherosclerosis Imaging Research Program, Madison, Wisconsin USA.
Objectives:
The purpose of this study was to determine the reliability of grayscale median (GSM) measurements across different ultrasound (US) systems and effects of gain on GSM values.
Methods:
Two vessels in a grayscale vascular phantom were imaged with 7 US systems at 3 gain settings. Two human participants were imaged at 3 gain settings. Each image was normalized, standardized, and segmented by expert and novice readers using grayscale analysis software. The concordance correlation coefficient (CCC) assessed agreement of GSM values for each system across gain settings and vessels and between readers. The intraclass correlation coefficient (ICC) assessed system-level reader concordance across gain settings and vessels. A general linear mixed model for repeated measures was used to assess within- and between-system mean GSM values.
Results:
Grayscale median measurements performed on images from the same US system yielded excellent (CCC) (95% confidence intervals): 0.85 (0.75, 0.92) to 0.96 (0.92, 0.98). ICC per system were 0.94 to 0.98 for the expert reader and 0.85 to 0.95 for the novice reader. Gain adjustments above and below an optimal setting contributed to significantly different intrasystem GSM values on 4 of 7 systems in the near zone and 5 of 7 systems in the far zone (P < .05). Intersystem GSM values differed on 5 of 7 systems (P < .05). Images from the human participants showed differences in GSM values at optimum gain values ± 10 dB/%.
Conclusions:
Grayscale median measurements are highly reproducible when obtained from the same US system with similar gain settings. Grayscale median values differ significantly across gain values and between systems. Researchers should consider the impact of US system and gain settings on GSM values when working to minimize system- and operator-dependent factors.
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