Chen Li, Mossa-Basha Mahmud, Sun Jie, Hippe Daniel S, Balu Niranjan, Yuan Quan, Pimentel Kristi, Hatsukami Thomas S, Hwang Jenq-Neng, Yuan Chun
Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA.
Department of Radiology, University of Washington, Seattle, WA 98195, USA.
Magn Reson Imaging. 2019 Apr;57:293-302. doi: 10.1016/j.mri.2018.12.007. Epub 2018 Dec 20.
Accurate and reliable vascular features extracted from 3D time-of-flight (TOF) magnetic resonance angiography (MRA) can help evaluate cerebral vascular diseases and conditions. The goal of this study was to evaluate the reproducibility of an intracranial artery feature extraction (iCafe) algorithm for quantitative analysis of intracranial arteries from TOF MRA.
Twenty-four patients with known intracranial artery stenosis were recruited and underwent two separate MRA scans within 2 weeks of each other. Each dataset was blinded to associated imaging and clinical data and then processed independently using iCafe. Inter-scan reproducibility analysis was performed on the 24 pairs of scans while intra-/inter-operator reproducibility and stenosis detection were assessed on 8 individual MRA scans. After tracing the vessels visualized on TOF MRA, iCafe was used to automatically extract the locations with stenosis and eight other vascular features. The vascular features included the following six morphometry and two signal intensity features: artery length (total, distal, and proximal), volume, number of branches, average radius of the M1 segment of the middle cerebral artery, and average normalized intensity of all arteries and large vertical arteries. A neuroradiologist independently reviewed the images to identify locations of stenosis for the reference standard. Reproducibility of stenosis detection and vascular features was assessed using Cohen's kappa, the intra-class correlation coefficient (ICC), and within-subject coefficient of variation (CV).
The segment-based sensitivity of iCafe for stenosis detection ranged from 83.3-91.7% while specificity was 97.4%. Kappa values for inter-scan and intra-operator reproducibility were 0.73 and 0.77, respectively. All vascular features demonstrated excellent inter-scan and intra-operator reproducibility (ICC = 0.91-1.00, and CV = 1.21-8.78% for all markers), and good to excellent inter-operator reproducibility (ICC = 0.76-0.99, and CV = 3.27-15.79% for all markers).
Intracranial artery features can be reliably quantified from TOF MRA using iCafe to provide both clinical diagnostic assistance and facilitate future investigative quantitative analyses.
从三维时间飞跃(TOF)磁共振血管造影(MRA)中提取准确可靠的血管特征有助于评估脑血管疾病和病症。本研究的目的是评估一种颅内动脉特征提取(iCafe)算法对TOF MRA颅内动脉进行定量分析的可重复性。
招募了24例已知颅内动脉狭窄的患者,在两周内对其进行了两次独立的MRA扫描。每个数据集都对相关的影像和临床数据进行了盲法处理,然后使用iCafe进行独立处理。对24对扫描进行扫描间可重复性分析,同时对8例个体MRA扫描评估操作者内/间可重复性和狭窄检测情况。在描绘TOF MRA上显示的血管后,使用iCafe自动提取狭窄部位和其他八个血管特征。血管特征包括以下六个形态学特征和两个信号强度特征:动脉长度(总长度、远端长度和近端长度)、体积、分支数量、大脑中动脉M1段的平均半径以及所有动脉和大垂直动脉的平均归一化强度。一名神经放射科医生独立审查图像以确定狭窄部位作为参考标准。使用科恩kappa系数、组内相关系数(ICC)和受试者内变异系数(CV)评估狭窄检测和血管特征的可重复性。
iCafe检测狭窄的基于节段的敏感性范围为83.3 - 91.7%,特异性为97.4%。扫描间和操作者内可重复性的kappa值分别为0.73和0.77。所有血管特征在扫描间和操作者内均显示出极好的可重复性(所有标记物的ICC = 0.91 - 1.00,CV = 1.21 - 8.78%),在操作者间显示出良好至极好的可重复性(所有标记物的ICC = 0.76 - 0.99,CV = 3.27 - 15.79%)。
使用iCafe可以从TOF MRA中可靠地量化颅内动脉特征,为临床诊断提供帮助,并便于未来的研究性定量分析。