Motion-compensation approach for quantitative digital subtraction angiography and its effect on in-vivo blood velocity measurement.
Motion-compensation approach for quantitative digital subtraction angiography and its effect on in-vivo blood velocity measurement.
复制标题
定量数字减影血管造影的运动补偿方法及其对体内血流速度测量的影响。
DOI：
10.1117/1.jmi.11.1.013501
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Wagner,MartinG
中科院分区：
文献类型：
--
作者：
Whitehead,JosephF;Periyasamy,Sarvesh;Laeseke,PaulF;Speidel,MichaelA;Wagner,MartinG
PurposeQuantitative 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.ApproachThe 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 evaluatedin-vivothrough 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.ResultsCenterline distance errors wererelative to gold standard manual annotations. For the uncompensated approach, the best- and worst-case static centerlines had distance errors ofand, respectively. Linear regression analysis found a high-squared between qDSA-derived blood velocities using gold standard centerlines and motion-compensated centerlines () with a slope of 1.15 and a small offset of. The use of static centerlines resulted in low coefficients of determination for the best case () and worst-case () scenarios, with slopes close to zero.ConclusionsIn-vivovalidation of motion-compensated qDSA analysis demonstrated improved velocity quantification accuracy in vessels with motion, addressing an important clinical limitation of the current qDSA algorithm.
