Arterial input function (AIF) is estimated from perfusion images as a basic curve for the following deconvolution process to calculate hemodynamic variables to evaluate vascular status of tissues. However, estimation of AIF is currently based on manual annotations with prior knowledge. We propose an automatic estimation of AIF in perfusion images based on a multi-stream 3D CNN, which combined spatial and temporal features together to estimate the AIF ROI. The model is trained by manual annotations. The proposed method was trained and tested with 100 cases of perfusion-weighted imaging. The result was evaluated by dice similarity coefficient, which reached 0.79. The trained model had a better performance than the traditional method. After segmentation of the AIF ROI, the AIF was calculated by the average of all voxels in the ROI. We compared the AIF result with the manual and traditional methods, and the parameters of further processing of AIF, such as time to the maximum of the tissue residue function (Tmax), relative cerebral blood flow, and mismatch volume, which are calculated in the Section Results. The result had a better performance, the average mismatch volume reached 93.32% of the manual method, while the other methods reached 85.04 and 83.04%. We have applied the method on the cloud platform, Estroke, and the local version of its software, NeuBrainCare, which can evaluate the volume of the ischemic penumbra, the volume of the infarct core, and the ratio of mismatch between perfusion and diffusion images to help make treatment decisions, when the mismatch ratio is abnormal.

中文翻译：

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基于多流3D CNN的动脉输入功能自动估计

根据灌注图像估计动脉输入函数（AIF），作为后续反卷积过程的基本曲线，以计算血流动力学变量以评估组织的血管状态。然而，AIF 的估计目前基于先验知识的手动注释。我们提出了一种基于多流 3D CNN 的灌注图像中 AIF 的自动估计，它将空间和时间特征结合在一起来估计 AIF ROI。该模型通过手动注释进行训练。所提出的方法经过 100 例灌注加权成像的训练和测试。通过骰子相似系数评价结果，达到0.79。经过训练的模型比传统方法具有更好的性能。对 AIF ROI 进行分割后，通过 ROI 中所有体素的平均值计算 AIF。我们将AIF结果与手动和传统方法进行比较，并计算AIF进一步处理的参数，例如达到组织残留函数最大值的时间（Tmax）、相对脑血流量和错配体积，这些参数在部分结果。结果表现较好，平均错配量达到手动方法的93.32%，而其他方法分别达到85.04和83.04%。我们将该方法应用在云平台EStrike及其本地版软件NeuBrainCare上，可以评估缺血半暗带的体积、梗死核心的体积以及灌注和弥散图像的不匹配比例当错配率异常时，帮助做出治疗决策。