Chatterjee Neil R, Ansari Sameer A, Vakil Parmede, Prabhakaran Shyam, Carroll Timothy J, Hurley Michael C
Department of Radiology, Northwestern University Feinberg School of Medicine, 737N Michigan Suite 1600, Chicago, IL, USA.
Department of Neurology, Northwestern University Feinberg School of Medicine, Abbott Hall Suite 1123, 710 N Lake Shore Drive, Chicago, IL, USA.
Magn Reson Imaging. 2015 Jun;33(5):618-23. doi: 10.1016/j.mri.2015.01.009. Epub 2015 Jan 17.
To determine the feasibility of automatic vascular territory region of interest (ROI) construction as a method for standardized quantification of cerebral blood flow (CBF) images.
An algorithm for automatic construction of vascular territory ROIs was performed on 10 healthy controls and 25 patients with perfusion abnormalities identified by retrospective chart review. The ROIs were used to quantify perfusion asymmetry for each territory, and perfusion asymmetry was compared in the two cohorts and against blinded neuroradiologist interpretation. The algorithm was additionally applied to a separate cohort of 23 prospectively enrolled patients and perfusion asymmetry was correlated against clinical variables.
There was significantly greater perfusion asymmetry in territories graded by neuroradiologists as hypoperfused compared to those graded as normally perfused (p<.05) and compared to healthy volunteers (p<.01). An ROC analysis showed that perfusion asymmetry was sensitive and specific for identifying hypoperfusion in vascular territories (84.9% sensitivity and 90.5% specificity for a threshold asymmetry index of .829). In the prospective cohort, perfusion asymmetry was correlated with initial NIH stroke scale (NIHSS) (p<.01) and length of stay (p<.05).
Automatic construction of vascular territory ROIs and calculation of perfusion asymmetry is a feasible method for analyzing CBF images. Because the technique is rapid and minimizes bias, it can facilitate analysis of larger scale research studies.
确定自动构建血管区域感兴趣区(ROI)作为标准化定量脑血流量(CBF)图像方法的可行性。
对10名健康对照者和25名经回顾性病历审查确定存在灌注异常的患者执行了一种自动构建血管区域ROI的算法。这些ROI用于量化每个区域的灌注不对称性,并在两个队列中比较灌注不对称性,并与不知情的神经放射科医生的解读进行对比。该算法还应用于另外一组23名前瞻性入组的患者,并且将灌注不对称性与临床变量进行关联分析。
与神经放射科医生判定为灌注正常的区域相比(p<0.05),以及与健康志愿者相比(p<0.01),神经放射科医生判定为灌注不足的区域存在明显更大的灌注不对称性。一项ROC分析表明,灌注不对称性对于识别血管区域的灌注不足具有敏感性和特异性(对于阈值不对称指数为0.829,敏感性为84.9%,特异性为90.5%)。在前瞻性队列中,灌注不对称性与初始美国国立卫生研究院卒中量表(NIHSS)(p<0.01)和住院时间(p<0.05)相关。
自动构建血管区域ROI并计算灌注不对称性是分析CBF图像的一种可行方法。由于该技术快速且偏差最小,它可促进大规模研究的分析。