Atlas-based analysis of 4D flow CMR: automated vessel segmentation and flow quantification.
Atlas-based analysis of 4D flow CMR: automated vessel segmentation and flow quantification.
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DOI：
10.1186/s12968-015-0190-5
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发表时间：
2015-10-05
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影响因子：
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通讯作者：
Ebbers T
中科院分区：
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作者：
Bustamante M;Petersson S;Eriksson J;Alehagen U;Dyverfeldt P;Carlhäll CJ;Ebbers T
胸椎大血管的流量定量用于几种心血管疾病的评估。临床上，它通常是基于二维电影相对比心血管磁共振（CMR）图像对整个心脏周期的血管进行半自动分割。三维（3D）、具有三方向速度编码的时间分辨相位对比CMR （4D flow CMR）允许在时间分辨3D体的任何位置回顾性评估净流量和流型。然而，对这些数据集的分析可能要求很高。本研究的目的是开发和评估一种全自动分割和分析胸腔大血管血流CMR数据的方法。该方法利用基于图谱的分割方法对收缩时期的胸大血管进行分割，并在心脏周期的不同时间框架之间进行配准，以便随着时间的推移对这些血管进行分割。此外，在感兴趣的位置自动计算净流量。将该方法应用于11名健康志愿者和10名心力衰竭患者的4D血流CMR数据集。对该方法进行了目测评估，并通过比较自动（使用提出的方法）和半自动获得的升主动脉净流量进行了评估。通过比较主动脉、肺动脉和腔静脉不同位置自动获得的净流量来进一步评估。除了一个数据集之外，对生成的分割结果进行视觉评估对所有主要血管都产生了良好的结果。自动与半自动获得的升主动脉净流量比较，相关性非常高（r2=0.926）。此外，在其他血管位置自动获得的净流量的比较也产生了预期的高相关性：肺动脉干vs近端升主动脉（r2=0.955），肺动脉干vs肺分支（r2=0.808），肺动脉干vs腔静脉（r2=0.906）。提出的方法允许自动分析四维流动CMR数据，包括血管分割，评估感兴趣位置的流量，以及四维流动可视化。这是促进4D血流CMR临床应用的重要一步。本文的在线版本（doi:10.1186/s12968-015-0190-5）包含补充材料，可供授权用户使用。
Flow volume quantification in the great thoracic vessels is used in the assessment of several cardiovascular diseases. Clinically, it is often based on semi-automatic segmentation of a vessel throughout the cardiac cycle in 2D cine phase-contrast Cardiovascular Magnetic Resonance (CMR) images. Three-dimensional (3D), time-resolved phase-contrast CMR with three-directional velocity encoding (4D flow CMR) permits assessment of net flow volumes and flow patterns retrospectively at any location in a time-resolved 3D volume. However, analysis of these datasets can be demanding. The aim of this study is to develop and evaluate a fully automatic method for segmentation and analysis of 4D flow CMR data of the great thoracic vessels. The proposed method utilizes atlas-based segmentation to segment the great thoracic vessels in systole, and registration between different time frames of the cardiac cycle in order to segment these vessels over time. Additionally, net flow volumes are calculated automatically at locations of interest. The method was applied on 4D flow CMR datasets obtained from 11 healthy volunteers and 10 patients with heart failure. Evaluation of the method was performed visually, and by comparison of net flow volumes in the ascending aorta obtained automatically (using the proposed method), and semi-automatically. Further evaluation was done by comparison of net flow volumes obtained automatically at different locations in the aorta, pulmonary artery, and caval veins. Visual evaluation of the generated segmentations resulted in good outcomes for all the major vessels in all but one dataset. The comparison between automatically and semi-automatically obtained net flow volumes in the ascending aorta resulted in very high correlation (r2=0.926). Moreover, comparison of the net flow volumes obtained automatically in other vessel locations also produced high correlations where expected: pulmonary trunk vs. proximal ascending aorta (r2=0.955), pulmonary trunk vs. pulmonary branches (r2=0.808), and pulmonary trunk vs. caval veins (r2=0.906). The proposed method allows for automatic analysis of 4D flow CMR data, including vessel segmentation, assessment of flow volumes at locations of interest, and 4D flow visualization. This constitutes an important step towards facilitating the clinical utility of 4D flow CMR. The online version of this article (doi:10.1186/s12968-015-0190-5) contains supplementary material, which is available to authorized users.