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Brachial Artery: Measurement of Flow-mediated Dilatation with Cross-sectional US—Technical Validation¹

¹ From the Departments of Radiology (Y.H.K., P.H.A., C.M.S.) and Medicine (E.R.M.), University of Pennsylvania Medical Center, 1 Silverstein, 3400 Spruce St, Philadelphia, PA 19104. Received August 1, 2002; revision requested October 7; revision received November 8; accepted December 10. Address correspondence to C.M.S. (e-mail: sehgal@oasis.rad.upenn.edu).

View larger version (27K): [in a new window]   Figure 1. Schematic diagram of UGABD algorithm. Left: The inner and outer circles represent the inner and outer boundaries of an artery. From the center of gravity, computed by using the inner boundary, 360 radial lines are drawn at equispacing of 1° per line. Only eight radial lines are shown. Right: Gray-level intensity profile (Gt) of a radial line from the brachial artery image is displayed as a function of rasterized pixels of coordinates (XR,YR). L = lumen, V = blood vessel.

View larger version (17K): [in a new window]   Figure 2. Graph of cross-sectional area measurements in a phantom performed across 100 frames of US images obtained in pulsatile and steady flow conditions. a = cross-sectional area measurements of the phantom during pulsatile conditions reflect a cyclic pattern. The peak-to-peak difference is 6% of the mean cross-sectional area. b = cross-sectional area measurements of the phantom during steady flow conditions reflect peak-to-peak variation of less than 0.5%.

View larger version (69K): [in a new window]   Figure 3. US images of brachial artery obtained, A, before, B, 2 minutes after, and, C, 5 minutes after arterial occlusion with a pressure cuff. D-F, Arterial boundaries as outlined by the UGABD algorithm on A, B, and C, respectively. These images demonstrate a close fit between the boundaries detected by the UGABD algorithm and the actual arterial wall.

View larger version (33K): [in a new window]   Figure 4. Comparison between manual and UGABD-derived measurements of cross-sectional area. A, Graph depicts change in area with time during pulsatile flow in the flow phantom. Manual tracing (solid line) shows 8.8% peak-to-peak variation, versus 5.7% for UGABD tracing (dashed line). On average, the UGABD-detected area is 3.2% larger than that defined with manual tracing. B, Graph depicts change in area with time during pulsatile flow in the brachial artery. Manual tracing (solid line) shows 20.8% peak-to-peak variation, versus 9.4% for UGABD tracing (dashed line). With this set of images, the area defined with manual tracing is, on average, 8.3% larger. In both experiments, manual tracing shows larger frame-to-frame variation; therefore, the change in area is not as smooth with manual tracing as that with UGABD. However, both boundary detection methods have the sensitivity to detect cyclic changes.

View larger version (23K): [in a new window]   Figure 5. Graphs show correlation of manual and UGABD (computer) tracings. The solid lines represent the least-squares fit of the data to the linear model Y = mX, where m represents the slope of the line. A, Graph shows correlation of combined data from the brachial artery and phantom studies. Slope = 1.04, Pearson regression coefficient = 1.00. B and C, Graphs show correlation of individual data sets from the brachial artery and phantom studies, respectively.

View larger version (23K): [in a new window]   Figure 6. Graphs show, A, mean area change and, B, mean diameter change during flow-mediated dilation of the brachial artery. The bars on each data point represent the peak-to-peak cross-sectional area or diameter value of arterial pulsation. After deflation of the pressure cuff, the lumen area increased gradually, peaking at 2 minutes. Cross-sectional area measurements show a change of 3,738 pixels from baseline (prepressure) measurements because of flow-mediated dilatation, while diameter measurements show a change of a mere 23 pixels from baseline.