Recent trials have shown promise in intra-arterial thrombectomy after the first 6–24 h of stroke onset. Quick and precise identification of the salvageable tissue is essential for successful stroke management. In this study, we examined the feasibility of machine learning (ML) approaches for differentiating the ischemic penumbra (IP) from the infarct core (IC) by using diffusion tensor imaging (DTI)-derived metrics. Fourteen male rats subjected to permanent middle cerebral artery occlusion (pMCAO) were included in this study. Using a 7 T magnetic resonance imaging, DTI metrics such as fractional anisotropy, pure anisotropy, diffusion magnitude, mean diffusivity (MD), axial diffusivity, and radial diffusivity were derived. The MD and relative cerebral blood flow maps were coregistered to define the IP and IC at 0.5 h after pMCAO. A 2-level classifier was proposed based on DTI-derived metrics to classify stroke hemispheres into the IP, IC, and normal tissue (NT). The classification performance was evaluated using leave-one-out cross validation. The IC and non-IC can be accurately segmented by the proposed 2-level classifier with an area under the receiver operating characteristic curve (AUC) between 0.99 and 1.00, and with accuracies between 96.3 and 96.7%. For the training dataset, the non-IC can be further classified into the IP and NT with an AUC between 0.96 and 0.98, and with accuracies between 95.0 and 95.9%. For the testing dataset, the classification accuracy for IC and non-IC was 96.0 ± 2.3% whereas for IP and NT, it was 80.1 ± 8.0%. Overall, we achieved the accuracy of 88.1 ± 6.7% for classifying three tissue subtypes (IP, IC, and NT) in the stroke hemisphere and the estimated lesion volumes were not significantly different from those of the ground truth (p = .56, .94, and .78, respectively). Our method achieved comparable results to the conventional approach using perfusion–diffusion mismatch. We suggest that a single DTI sequence along with ML algorithms is capable of dichotomizing ischemic tissue into the IC and IP.

中文翻译：

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通过在大鼠模型中使用扩散张量度量，基于机器学习的缺血半影的分割。

最近的试验表明，卒中发生后最初的6-24小时后，在动脉内血栓切除术中有望实现。快速而准确地识别出可拯救组织对于成功的中风管理至关重要。在这项研究中，我们研究了通过使用扩散张量成像（DTI）得出的指标，将机器学习（ML）方法与缺血性半影​​（IP）与梗塞核心（IC）区分的可行性。这项研究包括了十四只遭受永久性大脑中动脉阻塞（pMCAO）的雄性大鼠。使用7 T磁共振成像，可以得出DTI度量，例如分数各向异性，纯各向异性，扩散幅度，平均扩散率（MD），轴向扩散率和径向扩散率。MD和相对脑血流图被共同配准以定义pMCAO后0.5小时的IP和IC。提出了一种基于DTI的度量标准的2级分类器，以将中风半球分类为IP，IC和正常组织（NT）。使用留一法交叉验证评估分类性能。所提出的2级分类器可以准确地对IC和非IC进行细分，其接收器工作特性曲线（AUC）下的面积在0.99至1.00之间，精度在96.3至96.7％之间。对于训练数据集，非IC可以进一步分为IP和NT，其AUC在0.96至0.98之间，准确度在95.0至95.9％之间。对于测试数据集，IC和非IC的分类精度为96.0±2.3％，而IP和NT的分类精度为80.1±8.0％。总体而言，我们对三种组织亚型（IP，IC，和NT）和估计的病灶体积与地面实况没有显着差异（分别为p = .56，.94和.78）。我们的方法使用灌注－扩散失配获得了与传统方法相当的结果。我们建议，单个DTI序列与ML算法一起能够将缺血组织二分为IC和IP。