Vessel centerline extraction in phase-contrast MR images using vector flow information
Vessel centerline extraction in phase-contrast MR images using vector flow information
复制标题
使用矢量流信息提取相衬 MR 图像中的血管中心线
DOI：
--
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
R. Unterhinninghofen
中科院分区：
文献类型：
--
作者：
Yoo;S. Ley;R. Dillmann;R. Unterhinninghofen
为了在心血管疾病的情况下获得血流动力学相关参数，速度编码磁共振成像(PC-MRI)被用于无创性地测量三维速度场中的血流。在对这些数据集中的血管管腔进行分割的过程中，由于图像质量降低，传统的分割方法常常失败。本文提出了一种利用从矢量流信息中提取的附加特征提取PC-MR图像中大血管中心线的方法。该算法可分为利用流线和最大特征向量沿船舶航道传播、径向搜索船舶边界、确定船舶横截面中心位置和根据船舶曲率调整传播步长等步骤。这是通过使用形态和流动信息的组合来完成的：Sobel滤波和阈值滤波图像作为形态特征，以及流动向量的相干值和血管内和边界周围的血流流线的行为作为流动特征。该算法在临床PC-MRI数据集上进行了评估，取得了令人满意的结果。在17个被检查的数据集中，有16个成功地提取了整个主动脉的中心点以及相应的边界点。对于血管边界的检测，从流动信息中提取的特征比形态特征提取的结果更可靠。
To obtain hemodynamic-relevant parameters in case of cardiovascular diseases the velocity-encoded magnetic resonance imaging (PC-MRI) is used for the non-invasive measurement of the blood flow in terms of 3D velocity fields. During the segmentation of the vessel lumen in those datasets conventional segmentation methods often fail due to reduced image quality. In this paper we present a method for the centerline extraction of great vessels in PC-MR images using additional features extracted from vector flow information. The proposed algorithm can be divided in the following steps: the propagation along the vessel course by using streamlines and the largest eigenvector, the radial search for the vessel boundary, the determination of the center position in the cross-sectional plane of the vessel and the adjustment of the propagation step size subject to the vessel curvature. This is done by using a combination of morphology and flow information: the Sobel filtered and the threshold filtered image as morphologic features as well as the coherence values of the flow vectors and the behaviour of the blood flow streamlines within the vessel and around the borders as flow features. The developed algorithm was evaluated on clinical PC-MRI datasets with encouraging results. The centerline points of the entire aorta as well as corresponding border points were successfully extracted for 16 out of 17 examined datasets. For the detection of the vessel boundary the features extracted from flow information showed to yield more reliable results than morphology features.