Quantitative characterization of color Doppler images: reproducibility, accuracy, and limitations.
A computer-based quantitative analysis for color Doppler images of complex vascular formations is presented. The red-green-blue-signal from an Acuson XP10 is frame-grabbed and digitized. By matching each image pixel with the color bar, color pixels are identified and assigned to the corresponding flow velocity (color value). Data analysis consists of delineation of a region of interest and calculation of the relative number of color pixels in this region (color pixel density) as well as the mean color value. The mean color value was compared to flow velocities in a flow phantom. The thyroid and carotid artery in a volunteer were repeatedly examined by a single examiner to assess intra-observer variability. The thyroids in five healthy controls were examined by three experienced physicians to assess the extent of inter-observer variability and observer bias. The correlation between the mean color value and flow velocity ranged from 0.94 to 0.96 for a range of velocities determined by pulse repetition frequency. The average deviation of the mean color value from the flow velocity was 22% to 41%, depending on the selected pulse repetition frequency (range of deviations, -46% to +66%). Flow velocity was underestimated with inadequately low pulse repetition frequency, or inadequately high reject threshold. An overestimation occurred with inadequately high pulse repetition frequency. The highest intra-observer variability was 22% (relative standard deviation) for the color pixel density, and 9.1% for the mean color value. The inter-observer variation was approximately 30% for the color pixel density, and 20% for the mean color value. In conclusion, computer assisted image analysis permits an objective description of color Doppler images. However, the user must be aware that image acquisition under in vivo conditions as well as physical and instrumental factors may considerably influence the results.
German Cancer Research Center, Department of Radiological Diagnostics and Therapy, Heidelberg, Germany.
S
S
Delorme
G
G
Weisser
I
I
Zuna
M
M
Fein
A
A
Lorenz
G
G
van Kaick
D000465:Algorithms; D015982:Bias; D001783:Blood Flow Velocity; D001808:Blood Vessels / Q000000981:diagnostic imaging; D002339:Carotid Arteries / Q000000981:diagnostic imaging; D003116:Color; D003199:Computer Systems; D003626:Data Display; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods / Q000706:statistics & numerical data; D008961:Models, Structural; D015588:Observer Variation; D011673:Pulsatile Flow; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013961:Thyroid Gland / Q000098:blood supply / Q000000981:diagnostic imaging; D018615:Ultrasonography, Doppler, Color / Q000706:statistics & numerical data