Department of Chemical and Biomedical Engineering, Cleveland State University, Cleveland, OH, USA; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA; Division of Vascular and Interventional Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
Comput Methods Programs Biomed. 2020 Jul;190:105379. doi: 10.1016/j.cmpb.2020.105379. Epub 2020 Feb 1.
Vascular access is the "lifeline" of end-stage renal disease patients, which is surgically constructed to remove blood-waste and return artificially filtered blood into circulation. The arteriovenous shunting causes an abrupt change in blood flow and results in increased fluidic stress, which predisposes to access stenosis and thrombosis. While access flow is crucial to evaluate interventional endpoint, application to measure flow using digital angiogram is not yet available. The goal of this study was to determine the feasibility of flow quantification in dialysis access using a software tool and to guide the design of an imaging protocol.
173 digital subtraction angiographic (DSA) images were retrospectively analyzed to evaluate access flow in a custom-programming environment. Four bolus transit time algorithms and a distance calculation method were assessed for flow computation. Gamma variate function was applied to remove secondary flow and intensity outliers in the bolus time-intensity curves and evaluated for enhancement in computational accuracy. The percent deviations of flow rates computed from dilution of iodinated radio-contrast material were compared with in situ catheter-based flow measurement.
Among the implemented bolus transit time algorithms, quantification error (mean ± standard error) of cross-correlation algorithm without and with gamma variate curve fitting was 35 ± 1% and 22 ± 1%, respectively. All other algorithms had quantification error >27%. The bias and limits of agreement of the cross-correlation algorithm with gamma variate curve fit was -94 ml/min and [-353, 165] mL/min, respectively.
The cross-correlation algorithm with gamma variate curve fit had the best accuracy and reproducibility for image-based blood flow computation. To further enhance accuracy, images may need to be acquired with a dedicated injection protocol with predetermined parameters such as the duration, rate and mode of bolus injection, and the acquisition frame rate.
血管通路是终末期肾病患者的“生命线”，通过手术构建，以清除血液废物并将人工过滤的血液回流循环。动静脉分流导致血流急剧变化，导致流体压力增加，从而导致通路狭窄和血栓形成。虽然流量是评估介入终点的关键，但使用数字血管造影术测量流量的应用尚未普及。本研究旨在确定使用软件工具量化透析通路流量的可行性，并指导成像方案的设计。
回顾性分析了 173 例数字减影血管造影（DSA）图像，以在定制编程环境中评估通路流量。评估了四种脉冲传输时间算法和一种距离计算方法，用于流量计算。伽马变量函数用于去除脉冲时间-强度曲线中的二次流和强度异常值，并评估其对计算精度的增强效果。从碘化放射性对比材料稀释计算得出的流量率的百分比偏差与原位导管流量测量进行了比较。
在所实现的脉冲传输时间算法中，不带和带伽马变量曲线拟合的互相关算法的定量误差（平均值±标准误差）分别为 35±1%和 22±1%。所有其他算法的定量误差均>27%。交叉相关算法与带伽马变量曲线拟合的偏置和协议范围分别为-94ml/min 和 [-353, 165]ml/min。
带伽马变量曲线拟合的互相关算法在基于图像的血流计算中具有最佳的准确性和可重复性。为了进一步提高准确性，可能需要使用具有预定参数（如脉冲注射持续时间、速率和模式以及采集帧率）的专用注射协议来获取图像。
