Department of Electrical Engineering, University of Washington, Seattle, Washington, USA.
Department of Radiology, University of Washington, Seattle, Washington, USA.
Magn Reson Med. 2018 Jun;79(6):3229-3238. doi: 10.1002/mrm.26961. Epub 2017 Oct 17.
To develop a quantitative intracranial artery measurement technique to extract comprehensive artery features from time-of-flight MR angiography (MRA).
By semiautomatically tracing arteries based on an open-curve active contour model in a graphical user interface, 12 basic morphometric features and 16 basic intensity features for each artery were identified. Arteries were then classified as one of 24 types using prediction from a probability model. Based on the anatomical structures, features were integrated within 34 vascular groups for regional features of vascular trees. Eight 3D MRA acquisitions with intracranial atherosclerosis were assessed to validate this technique.
Arterial tracings were validated by an experienced neuroradiologist who checked agreement at bifurcation and stenosis locations. This technique achieved 94% sensitivity and 85% positive predictive values (PPV) for bifurcations, and 85% sensitivity and PPV for stenosis. Up to 1,456 features, such as length, volume, and averaged signal intensity for each artery, as well as vascular group in each of the MRA images, could be extracted to comprehensively reflect characteristics, distribution, and connectivity of arteries. Length for the M1 segment of the middle cerebral artery extracted by this technique was compared with reviewer-measured results, and the intraclass correlation coefficient was 0.97.
A semiautomated quantitative method to trace, label, and measure intracranial arteries from 3D-MRA was developed and validated. This technique can be used to facilitate quantitative intracranial vascular research, such as studying cerebrovascular adaptation to aging and disease conditions. Magn Reson Med 79:3229-3238, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
开发一种定量颅内动脉测量技术，从 3D 时间飞越磁共振血管成像（MRA）中提取全面的动脉特征。
通过在图形用户界面中基于开放曲线主动轮廓模型半自动地追踪动脉，确定了每条动脉的 12 个基本形态特征和 16 个基本强度特征。然后，使用概率模型的预测将动脉分类为 24 种类型之一。基于解剖结构，特征在 34 个血管组内进行整合，以获得血管树的区域特征。对 8 例颅内动脉粥样硬化的 3D MRA 采集进行评估以验证该技术。
由一位经验丰富的神经放射科医生检查分支和狭窄位置的一致性来验证动脉追踪。该技术对分支的敏感性达到 94%，阳性预测值（PPV）为 85%，对狭窄的敏感性和 PPV 为 85%。可以提取多达 1456 个特征，如每条动脉的长度、体积和平均信号强度，以及每个 MRA 图像的血管组，以全面反映动脉的特征、分布和连通性。该技术提取的大脑中动脉 M1 段的长度与审阅者测量的结果进行比较，组内相关系数为 0.97。
开发并验证了一种从 3D-MRA 追踪、标记和测量颅内动脉的半自动定量方法。该技术可用于促进定量颅内血管研究，例如研究脑血管对衰老和疾病状况的适应。磁共振医学 79：3229-3238，2018。© 2017 国际磁共振医学学会。
