Fan Shengyu, Bian Yueyan, Wang Erling, Kang Yan, Wang Danny J J, Yang Qi, Ji Xunming
School of Sino-Dutch Biomedical and Information Engineering, Northeastern University, Shenyang, China.
Neusoft Institute of Intelligent Medical Research, Shenyang, China.
Front Neuroinform. 2019 Jul 5;13:49. doi: 10.3389/fninf.2019.00049. eCollection 2019.
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.
动脉输入函数（AIF）是从灌注图像中估计出来的，作为后续去卷积过程的基础曲线，用于计算血流动力学变量以评估组织的血管状态。然而，目前AIF的估计基于带有先验知识的手动标注。我们提出了一种基于多流3D卷积神经网络（CNN）的灌注图像中AIF的自动估计方法，该方法将空间和时间特征结合起来以估计AIF感兴趣区域（ROI）。该模型通过手动标注进行训练。所提出的方法使用100例灌注加权成像进行训练和测试。结果通过骰子相似系数进行评估，该系数达到了0.79。训练后的模型比传统方法具有更好的性能。在分割出AIF ROI后，通过ROI中所有体素的平均值来计算AIF。我们将AIF结果与手动和传统方法进行了比较，以及在“结果”部分计算的AIF进一步处理参数，如组织残留函数最大值的时间（Tmax）、相对脑血流量和错配体积。结果具有更好的性能，平均错配体积达到手动方法的93.32%，而其他方法分别为85.04%和83.04%。我们已将该方法应用于云平台Estroke及其本地版本软件NeuBrainCare，当错配率异常时，该软件可以评估缺血半暗带体积、梗死核心体积以及灌注与扩散图像之间的错配率，以帮助做出治疗决策。
