2D vessel contrast dilution gradient (CDG) analysis using 1000 fps high speed angiography (HSA) for velocity distribution estimation

Motion-compensation approach for quantitative digital subtraction angiography and its effect on in-vivo blood velocity measurement

Purpose

Quantitative monitoring of flow-altering interventions has been proposed using algorithms that quantify blood velocity from time-resolved two-dimensional angiograms. These algorithms track the movement of contrast oscillations along a vessel centerline. Vessel motion may occur relative to a statically defined vessel centerline, corrupting the blood velocity measurement. We provide a method for motion-compensated blood velocity quantification.

Approach

The motion-compensation approach utilizes a vessel segmentation algorithm to perform frame-by-frame vessel registration and creates a dynamic vessel centerline that moves with the vasculature. Performance was evaluated in-vivo through comparison with manually annotated centerlines. The method was also compared to a previous uncompensated method using best- and worst-case static centerlines chosen to minimize and maximize centerline placement accuracy. Blood velocities determined through quantitative DSA (qDSA) analysis for each centerline type were compared through linear regression analysis.

Results

Centerline distance errors were 0.3 ± 0.1    mm relative to gold standard manual annotations. For the uncompensated approach, the best- and worst-case static centerlines had distance errors of 1.1 ± 0.6 and 2.9 ± 1.2    mm , respectively. Linear regression analysis found a high R -squared between qDSA-derived blood velocities using gold standard centerlines and motion-compensated centerlines (R 2 = 0.97 ) with a slope of 1.15 and a small offset of - 0.6    cm / s . The use of static centerlines resulted in low coefficients of determination for the best case (R 2 = 0.35 ) and worst-case (R 2 = 0.20 ) scenarios, with slopes close to zero.

Conclusions

In-vivo validation of motion-compensated qDSA analysis demonstrated improved velocity quantification accuracy in vessels with motion, addressing an important clinical limitation of the current qDSA algorithm.