Automated multi-atlas segmentation of cardiac 4D flow MRI
Automated multi-atlas segmentation of cardiac 4D flow MRI
复制标题
心脏四维流动磁共振成像（MRI）的自动多图谱分割
DOI：
10.1016/j.media.2018.08.003
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发表时间：
2018-10-01
影响因子：
10.9
通讯作者：
Ebbers, Tino
中科院分区：
文献类型：
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作者：
Bustamante, Mariana;Gupta, Vikas;Ebbers, Tino
四维 (4D) 血流磁共振成像 (4D Flow MRI) 能够采集整个心脏和所有主要胸腔血管的时间分辨三向速度数据。这些组织的分割通常使用半自动方法进行。其中一些主要依赖于速度数据并导致仅在收缩期期间对血管进行分割。其他方法主要应用于心脏，依赖于单独采集的平衡稳态自由进动 (b-SSFP) MR 图像，然后将分割叠加在 4D Flow MRI 上。虽然 b-SSFP 图像通常覆盖整个心动周期并具有良好的对比度，但它们存在许多问题，例如切片厚度大、心脏解剖结构覆盖范围有限以及容易出现呼吸运动引起的位移误差。为了解决这些限制，我们提出了一种多图谱分割方法，该方法仅依赖于 4D Flow MRI 数据，自动生成四维分割，其中包括这些数据集中存在的整个胸部心血管系统。该方法在 27 名健康志愿者和 83 名左心室收缩功能轻度受损患者的 4D Flow MR 数据集上进行了评估。收缩末期和舒张末期心腔手动和自动分割的比较显示，与之前报道的 b-SSFP MR 图像自动分割方法的结果相当。此外，整个胸部心血管系统的自动分割改善了 4D Flow MRI 的可视化，并有利于血流动力学参数的计算。 (C) 2018 Elsevier B.V. 保留所有权利。
Four-dimensional (4D) flow magnetic resonance imaging (4D Flow MRI) enables acquisition of time resolved three-directional velocity data in the entire heart and all major thoracic vessels. The segmentation of these tissues is typically performed using semi-automatic methods. Some of which primarily rely on the velocity data and result in a segmentation of the vessels only during the systolic phases. Other methods, mostly applied on the heart, rely on separately acquired balanced Steady State Free Precession (b-SSFP) MR images, after which the segmentations are superimposed on the 4D Flow MRI. While b-SSFP images typically cover the whole cardiac cycle and have good contrast, they suffer from a number of problems, such as large slice thickness, limited coverage of the cardiac anatomy, and being prone to displacement errors caused by respiratory motion. To address these limitations we propose a multi-atlas segmentation method, which relies only on 4D Flow MRI data, to automatically generate four-dimensional segmentations that include the entire thoracic cardiovascular system present in these datasets. The approach was evaluated on 4D Flow MR datasets from a cohort of 27 healthy volunteers and 83 patients with mildly impaired systolic left-ventricular function. Comparison of manual and automatic segmentations of the cardiac chambers at end-systolic and end-diastolic timeframes showed agreements comparable to those previously reported for automatic segmentation methods of b-SSFP MR images. Furthermore, automatic segmentation of the entire thoracic cardiovascular system improves visualization of 4D Flow MRI and facilitates computation of hemodynamic parameters. (C) 2018 Elsevier B.V. All rights reserved.