Eindhoven University of Technology, Den Dolech 2, 5612 AZ, Eindhoven, The Netherlands.
Int J Comput Assist Radiol Surg. 2012 Mar;7(2):217-24. doi: 10.1007/s11548-011-0642-9. Epub 2011 Jul 21.
Quantitative analysis of vascular blood flow, acquired by phase-contrast MRI, requires accurate segmentation of the vessel lumen. In clinical practice, 2D-cine velocity-encoded slices are inspected, and the lumen is segmented manually. However, segmentation of time-resolved volumetric blood-flow measurements is a tedious and time-consuming task requiring automation.
Automated segmentation of large thoracic arteries, based solely on the 3D-cine phase-contrast MRI (PC-MRI) blood-flow data, was done. An active surface model, which is fast and topologically stable, was used. The active surface model requires an initial surface, approximating the desired segmentation. A method to generate this surface was developed based on a voxel-wise temporal maximum of blood-flow velocities. The active surface model balances forces, based on the surface structure and image features derived from the blood-flow data. The segmentation results were validated using volunteer studies, including time-resolved 3D and 2D blood-flow data. The segmented surface was intersected with a velocity-encoded PC-MRI slice, resulting in a cross-sectional contour of the lumen. These cross-sections were compared to reference contours that were manually delineated on high-resolution 2D-cine slices.
The automated approach closely approximates the manual blood-flow segmentations, with error distances on the order of the voxel size. The initial surface provides a close approximation of the desired luminal geometry. This improves the convergence time of the active surface and facilitates parametrization.
An active surface approach for vessel lumen segmentation was developed, suitable for quantitative analysis of 3D-cine PC-MRI blood-flow data. As opposed to prior thresholding and level-set approaches, the active surface model is topologically stable. A method to generate an initial approximate surface was developed, and various features that influence the segmentation model were evaluated. The active surface segmentation results were shown to closely approximate manual segmentations.
通过相位对比 MRI 获得的血管血流的定量分析需要对血管管腔进行准确的分割。在临床实践中，需要检查 2D 电影速度编码切片，并手动分割管腔。然而，时间分辨容积血流测量的分割是一项繁琐且耗时的任务，需要自动化。
仅基于 3D 电影相位对比 MRI（PC-MRI）血流数据，对大的胸动脉进行自动分割。使用快速且拓扑稳定的主动表面模型。主动表面模型需要一个初始表面来近似所需的分割。基于血流速度的体素级时间最大值，开发了一种生成该表面的方法。主动表面模型根据表面结构和从血流数据中得出的图像特征来平衡力。使用志愿者研究验证分割结果，包括时间分辨 3D 和 2D 血流数据。分割表面与速度编码 PC-MRI 切片相交，生成管腔的横截面轮廓。这些横截面与手动在高分辨率 2D 电影切片上描绘的参考轮廓进行比较。
自动方法非常接近手动血流分割，误差距离在体素尺寸范围内。初始表面提供了对所需管腔几何形状的良好近似。这提高了主动表面的收敛速度并方便了参数化。
开发了一种用于血管管腔分割的主动表面方法，适用于 3D 电影 PC-MRI 血流数据的定量分析。与之前的阈值和水平集方法不同，主动表面模型是拓扑稳定的。开发了一种生成初始近似表面的方法，并评估了影响分割模型的各种特征。主动表面分割结果与手动分割非常接近。
