Winder Anthony, d'Esterre Christopher D, Menon Bijoy K, Fiehler Jens, Forkert Nils D
Department of Radiology, University of Calgary, Calgary, Canada.
Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
Med Phys. 2020 Sep;47(9):4199-4211. doi: 10.1002/mp.14351. Epub 2020 Jul 18.
The computation of perfusion parameter images requires knowledge of the arterial blood flow in the form of an arterial input function (AIF). This work proposes a novel method to automatically identify AIFs in computed tomography perfusion (CTP) and dynamic susceptibility contrast perfusion-weighted MRI (PWI) datasets using a deep convolutional neural network (CNN).
One-hundred CTP and 100 PWI datasets of acute ischemic stroke patients were available for model development and evaluation. For each modality, 50 datasets were used for CNN training and 20 for validation using manually selected AIFs and non-arterial tissue concentration time curves. Model evaluation was performed using the remaining 30 independent validation datasets from each modality with manual AIF selections provided by two experts as ground truth. Additionally, AIFs were also extracted using an established automatic shape-based algorithm for comparison purposes. The extracted AIFs were compared using normalized cross-correlation and shape features as well as using the Dice similarity metric and volume of the corresponding hypoperfusion (Tmax > 6 s) lesions.
The cross-correlation values comparing the manual AIFs and those extracted by the proposed CNN method were significantly greater than those comparing the manual AIFs to the shape-based comparison method. Likewise, hypoperfusion lesions generated using the manually selected AIFs and CNN-based AIFs showed higher Dice values compared to hypoperfusion lesions generated using the comparison AIF extraction method. Shape features for AIFs generated by the proposed method did not differ significantly from the manual AIFs, with the exception that the CNN-derived AIFs for the PWI datasets showed marginally greater peak heights.
Deep convolutional neural network models are viable for the automatic extraction of the AIF from CTP and PWI datasets.
灌注参数图像的计算需要以动脉输入函数（AIF）的形式知晓动脉血流量。本研究提出一种新颖的方法，利用深度卷积神经网络（CNN）在计算机断层扫描灌注成像（CTP）和动态磁敏感对比灌注加权磁共振成像（PWI）数据集中自动识别AIF。
有100例急性缺血性中风患者的CTP和100例PWI数据集可用于模型开发和评估。对于每种模态，50个数据集用于CNN训练，20个用于使用手动选择的AIF和非动脉组织浓度时间曲线进行验证。使用来自每种模态的其余30个独立验证数据集进行模型评估，由两名专家提供的手动AIF选择作为参考标准。此外，为了进行比较，还使用一种既定的基于形状的自动算法提取AIF。使用归一化互相关和形状特征以及Dice相似性度量和相应低灌注（Tmax>6秒）病变的体积对提取的AIF进行比较。
将手动AIF与所提出的CNN方法提取的AIF进行比较的互相关值，显著大于将手动AIF与基于形状的比较方法进行比较的互相关值。同样，与使用比较AIF提取方法生成的低灌注病变相比，使用手动选择的AIF和基于CNN的AIF生成的低灌注病变显示出更高的Dice值。所提出方法生成的AIF的形状特征与手动AIF没有显著差异，唯一的例外是PWI数据集的CNN衍生AIF显示出略高的峰值高度。
深度卷积神经网络模型可用于从CTP和PWI数据集中自动提取AIF。
