SIGNIFICANCE The effects of varying the indocyanine green injection dose, injection rate, physiologic dispersion of dye, and intravenous tubing volume propagate into the shape and magnitude of the arterial input function (AIF) during intraoperative fluorescence perfusion assessment, thereby altering the observed kinetics of the fluorescence images in vivo. AIM Numerical simulations are used to demonstrate the effect of AIF on metrics derived from tissue concentration curves such as peak fluorescence, time-to-peak (TTP), and egress slope. APPROACH Forward models of tissue concentration were produced by convolving simulated AIFs with the adiabatic approximation to the tissue homogeneity model using input parameters representing six different tissue examples (normal brain, glioma, normal skin, ischemic skin, normal bone, and osteonecrosis). RESULTS The results show that AIF perturbations result in variations in estimates of total intensity of up to 80% and TTP error of up to 200%, with the errors more dominant in brain, less in skin, and less in bone. Interestingly, error in ingress slope was as high as 60% across all tissue types. These are key observable parameters used in fluorescence imaging either implicitly by viewing the image or explicitly through intensity fitting algorithms. Correcting by deconvolving the image with a measured subject-specific AIF provides an intuitive means of visualizing the data while also removing the source of variance and allowing intra- and intersubject comparisons. CONCLUSIONS These results suggest that intraoperative fluorescence perfusion assessment should be corrected by patient-specific AIFs measured by pulse dye densitometry.

中文翻译：

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术中荧光灌注评估应通过测量的受试者特定动脉输入函数进行校正。

意义 在术中荧光灌注评估期间，改变吲哚菁绿注射剂量、注射速率、染料的生理分散和静脉输液管体积的影响会传播到动脉输入功能 (AIF) 的形状和大小，从而改变观察到的动力学体内荧光图像。AIM 数值模拟用于证明 AIF 对源自组织浓度曲线的指标的影响，例如峰值荧光、峰值时间 (TTP) 和出口斜率。方法 组织浓度的正向模型是通过使用代表六个不同组织示例（正常大脑、神经胶质瘤、正常皮肤、缺血性皮肤、正常骨骼和骨坏死）的输入参数将模拟 AIF 与组织均匀性模型的绝热近似进行卷积来产生的。结果 结果表明，AIF 扰动导致总强度估计值的变化高达 80%，TTP 误差高达 200%，其中误差在大脑中占主导地位，在皮肤中较少，在骨骼中较少。有趣的是，所有组织类型的入口斜率误差高达 60%。这些是荧光成像中使用的关键可观察参数，可以通过查看图像隐式地或通过强度拟合算法显式地使用。通过使用测量的特定对象 AIF 对图像进行去卷积进行校正，提供了一种直观的数据可视化方法，同时还消除了方差来源并允许对象内和对象间的比较。结论 这些结果表明，术中荧光灌注评估应通过脉冲染料密度测定法测量的患者特异性 AIF 进行校正。