Peter Roman, Korfiatis Panagiotis, Blezek Daniel, Oscar Beitia A, Stepan-Buksakowska Irena, Horinek Daniel, Flemming Kelly D, Erickson Bradley J
Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 65691 Brno, Czech Republic.
Med Phys. 2017 Jan;44(1):192-199. doi: 10.1002/mp.12015. Epub 2017 Jan 8.
Early identification of ischemic stroke plays a significant role in treatment and potential recovery of damaged brain tissue. In noncontrast CT (ncCT), the differences between ischemic changes and healthy tissue are usually very subtle during the hyperacute phase (< 8 h from the stroke onset). Therefore, visual comparison of both hemispheres is an important step in clinical assessment. A quantitative symmetry-based analysis of texture features of ischemic lesions in noncontrast CT images may provide an important information for differentiation of ischemic and healthy brain tissue in this phase.
One hundred thirty-nine (139) ncCT scans of hyperacute ischemic stroke with follow-up magnetic resonance diffusion-weighted (MR-DW) images were collected. The regions of stroke were identified in the MR-DW images, which were spatially aligned to corresponding ncCT images. A state-of-the-art symmetric diffeomorphic image registration was utilized for the alignment of CT and MR-DW, for identification of individual brain hemispheres, and for localization of the region representing healthy tissue contralateral to the stroke cores. Texture analysis included extraction and classification of co-occurrence and run-length texture-based image features in the regions of ischemic stroke and their contralateral regions.
The classification schemes achieved area under the receiver operating characteristic [Az] ≈ 0.82 for the whole dataset. There was no statistically significant difference in the performance of classifiers for the data sets with time between 2 and 8 hours from symptom onset. The performance of the classifiers did not depend on the size of the stroke regions.
The results provide a set of optimal texture features which are suitable for distinguishing between hyperacute ischemic lesions and their corresponding contralateral brain tissue in noncontrast CT. This work is an initial step toward development of an automated decision support system for detection of hyperacute ischemic stroke lesions on noncontrast CT of the brain.
早期识别缺血性中风对治疗以及受损脑组织的潜在恢复具有重要作用。在非增强CT（ncCT）中，超急性期（中风发作后<8小时）缺血性改变与健康组织之间的差异通常非常细微。因此，对双侧半球进行视觉比较是临床评估的重要步骤。基于定量对称性分析非增强CT图像中缺血性病变的纹理特征，可为该阶段缺血性和健康脑组织的鉴别提供重要信息。
收集了139例超急性缺血性中风的非增强CT扫描图像以及后续的磁共振扩散加权（MR-DW）图像。在MR-DW图像中识别出中风区域，并将其与相应的非增强CT图像进行空间对齐。利用一种先进的对称微分同胚图像配准方法对CT和MR-DW进行对齐，以识别各个脑半球，并定位中风核心对侧代表健康组织的区域。纹理分析包括提取和分类缺血性中风区域及其对侧区域中基于共生矩阵和游程长度的纹理图像特征。
对于整个数据集，分类方案在接收器操作特征曲线下的面积[Az]约为0.82。对于症状发作后2至8小时的数据集，分类器的性能没有统计学上的显著差异。分类器的性能不取决于中风区域的大小。
研究结果提供了一组最佳纹理特征，适用于在非增强CT中区分超急性缺血性病变及其相应的对侧脑组织。这项工作是朝着开发用于在脑部非增强CT上检测超急性缺血性中风病变的自动决策支持系统迈出的第一步。
