Frame-by-frame ultrasound analysis of carotid artery plaques revealed a mean inter-frame coefficient of variation of 5.2% for greyscale median, indicating significant dynamic variations.
Observational (n=19)
No
BACKGROUND: Several studies have found that the ultrasound greyscale median (GSM) of carotid artery plaques may be useful for predicting the risk of cerebrovascular events. However, measurements of GSM are typically performed on still ultrasound images ignoring any variations that may be observed on a frame-by-frame basis. The aim of this study was to establish the existence and investigate the nature and extent of these variations. METHODS: Employing a novel method that enabled plaque boundaries to be tracked semi-automatically, variations in the plaque GSM and observed cross-sectional area were measured for 27 carotid artery plaques (19 consecutive patients, stenosis range 10%-80%) over image sequences of up to 10 seconds in length acquired with a mean frame rate of 32 frames per second. RESULTS: Our results showed a mean inter-frame coefficient of variation (CV) of 5.2% (s.d. 2.5%) for GSM and 4.2% (s.d. 2.9%) for the plaque area. Thirteen of the 27 plaques (48%) exhibited CV in GSM greater than 5% whereas only six plaques (22%) had CV in plaque area of greater than 5%. There was no significant correlation between the CV of GSM and plaque area. CONCLUSIONS: Inter-frame variations in the plaque GSM such as those found in this study have implications on the reproducibility of GSM measurements and their clinical utility. Studies assessing the GSM of carotid artery plaques should consider these variations.
Kanber et al. (Fri,) conducted a observational in Carotid artery plaques (n=19). Frame-by-frame ultrasound greyscale median (GSM) measurement vs. Still ultrasound images was evaluated on Inter-frame coefficient of variation (CV) for greyscale median (GSM). Frame-by-frame ultrasound analysis of carotid artery plaques revealed a mean inter-frame coefficient of variation of 5.2% for greyscale median, indicating significant dynamic variations.
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