Department of Radiology, University of California, San Diego, San Diego, CA 92103-8226, USA.
Magn Reson Imaging. 2011 Apr;29(3):391-400. doi: 10.1016/j.mri.2010.09.003. Epub 2010 Nov 12.
Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique.
时分辨磁共振血管造影术（CE-MRA）提供了血管内的对比动力学，并允许根据时间相关分析进行血管分割。本文提出了一种自动血管分割算法，包括自动生成感兴趣区域（ROI）、互相关和总体样本协方差矩阵分析。动态图像被分成多个相等大小的区域。在每个区域中，使用基于对比到达时间图和对比增强图的迭代阈值算法生成动脉、静脉和背景的 ROI。进行特定于区域的多特征互相关分析和总体协方差矩阵分析，以计算马氏距离（MD），用于自动将动脉与静脉分离。该分割算法应用于双期动态成像采集方案，其中在动态相期间采集低分辨率时分辨图像，随后在稳态相期间进行高频数据采集。然后将分割的低分辨率动脉和静脉图像与 k 空间中的高频数据相结合，并进行傅里叶逆变换，形成最终分割的动脉和静脉图像。志愿者和患者研究的结果表明了这种自动血管分割和双期数据采集技术的优势。
