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

ABSTRACT

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 (R2=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 (R2=0.35) and worst-case (R2=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.