Key result
Vascular vector flow mapping accurately estimates carotid velocities in a phantom model.
Why the study?
The existing vascular vector flow mapping algorithm is limited to bounded regions and cannot estimate velocities regardless of flow geometry.
Extends VFM to unbounded vascular geometries in phantoms; leaves open clinical translation pending in vivo studies.
A vascular vector flow mapping (VFM) method visualizes 2-D cardiac flow dynamics by estimating the radial component of flow from the Doppler velocities and wall motion velocities using the mass conservation equation. Although VFM provides 2-D flow, the algorithm is applicable only to bounded regions. Here, a modified VFM algorithm, vascular VFM, is proposed so that the velocities are estimated regardless of the flow geometry. To validate the algorithm, a phantom mimicking a carotid artery was fabricated and VFM velocities were compared with optical particle image velocimetry (PIV) data acquired in the same imaged plane. The validation results indicate that given optimal beam angle condition, VFM velocitiy is fairly accurate, where the correlation coefficient R between VFM and PIV velocities is 0.95. The standard deviation of the total VFM error, normalized by the maximum velocity, ranged from 8.1% to 16.3%, whereas the standard deviation of the measured input errors ranged from 8.9% to 12.7% for color flow mapping and from 4.5% to 5.9% for subbeam calculation. These results indicate that vascular VFM is reliable as its accuracy is comparable to that of conventional Doppler-flow images.
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Asami et al. (2019) studied this question. Vascular vector flow mapping (VFM) algorithm vs. Optical particle image velocimetry (PIV) was evaluated on Velocity correlation and standard deviation of total error (R 0.95). Vascular vector flow mapping (VFM) demonstrated accurate velocity estimation compared to optical particle image velocimetry (R = 0.95) in a carotid artery phantom model.
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