Blood flow quantification in dialysis access using digital subtraction angiography: A retrospective study. (July 2020) - Koirala, Nischal; McLennan, Gordon
Highlights: Dialysis access blood flow can be quantified using contrast-injected fistulagrams obtained during vascular access interventions. The integration of blood flow measurement function in the current angiographic system permits the endpoint determination of vascular access interventions. The cross-correlation algorithm with gamma variate smoothing function has the potential for precise blood flow computation ( r  = 0.92). Abstract: Background and objective: 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. Methods: 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. Results: 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. Conclusions: 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.