Cauley Keith A, Mongelluzzo Gino J, Fielden Samuel W
Department of Radiology, Geisinger, Danville, PA 17821, USA.
Department of Imaging Science & Innovation, Geisinger, Danville, PA 17821, USA.
Int J Biomed Imaging. 2019 Aug 21;2019:1720270. doi: 10.1155/2019/1720270. eCollection 2019.
Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density "infarcts," Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.
在卒中发作后的最初几个小时内进行的非增强头部CT扫描中识别早期缺血性改变（EIC），可能对后续治疗具有重要意义，尽管在这些研究中早期卒中的界限并不清晰。早期梗死灶缺乏清晰的病变边界划定，这使得手动测量体积以及使用边缘检测或区域填充算法进行测量都无法实现。我们希望检验一个假设，即图像强度不均匀性校正可能提供一种敏感的方法来识别早期缺血性梗死灶所特有的细微区域低密度。利用图像强度不均匀性校正（IIC）和强度阈值化开发了一种数字图像分析算法。比较了两种不同的IIC算法（FSL和ITK）。使用模拟梗死灶和临床病例对该方法进行了评估。对于合成梗死灶，测量的梗死灶体积与真实病变体积显示出很强的相关性（对于密度降低20%的“梗死灶”，两种算法的Pearson r均为0.998）；两种算法都显示出随着病变大小增加和病变密度降低，准确性有所提高。在临床病例中（30例患者中的41例急性梗死灶），使用FSL IIC计算的梗死灶体积与ASPECTS评分（Pearson r = 0.680）和入院时的美国国立卫生研究院卒中量表（NIHSS）评分（Pearson r = 0.544）相关。计算的梗死灶体积与静脉注射组织型纤溶酶原激活剂（IV-tPA）治疗的临床决策高度相关。将图像强度不均匀性校正应用于非增强头部CT时，可为图像分析提供一种工具，以帮助检测EIC，以及评估和指导扫描质量的改进，以实现对EIC的最佳检测。
