Köhler Benjamin, Preim Uta, Grothoff Matthias, Gutberlet Matthias, Fischbach Katharina, Preim Bernhard
Department of Simulation and Graphics, Otto-von-Guericke University, Magdeburg, Germany.
Department of Diagnostic Radiology, Municipal Hospital, Magdeburg, Germany.
Int J Comput Assist Radiol Surg. 2016 Feb;11(2):169-79. doi: 10.1007/s11548-015-1256-4. Epub 2015 Jul 17.
4D PC-MRI enables the noninvasive measurement of time-resolved, three-dimensional blood flow data that allow quantification of the hemodynamics. Stroke volumes are essential to assess the cardiac function and evolution of different cardiovascular diseases. The calculation depends on the wall position and vessel orientation, which both change during the cardiac cycle due to the heart muscle contraction and the pumped blood. However, current systems for the quantitative 4D PC-MRI data analysis neglect the dynamic character and instead employ a static 3D vessel approximation. We quantify differences between stroke volumes in the aorta obtained with and without consideration of its dynamics.
We describe a method that uses the approximating 3D segmentation to automatically initialize segmentation algorithms that require regions inside and outside the vessel for each temporal position. This enables the use of graph cuts to obtain 4D segmentations, extract vessel surfaces including centerlines for each temporal position and derive motion information. The stroke volume quantification is compared using measuring planes in static (3D) vessels, planes with fixed angulation inside dynamic vessels (this corresponds to the common 2D PC-MRI) and moving planes inside dynamic vessels.
Seven datasets with different pathologies such as aneurysms and coarctations were evaluated in close collaboration with radiologists. Compared to the experts' manual stroke volume estimations, motion-aware quantification performs, on average, 1.57% better than calculations without motion consideration. The mean difference between stroke volumes obtained with the different methods is 7.82%. Automatically obtained 4D segmentations overlap by 85.75% with manually generated ones.
Incorporating motion information in the stroke volume quantification yields slight but not statistically significant improvements. The presented method is feasible for the clinical routine, since computation times are low and essential parts run fully automatically. The 4D segmentations can be used for other algorithms as well. The simultaneous visualization and quantification may support the understanding and interpretation of cardiac blood flow.
四维相位对比磁共振成像（4D PC-MRI）能够对时间分辨的三维血流数据进行无创测量，从而实现血流动力学的量化。每搏输出量对于评估心脏功能以及不同心血管疾病的发展演变至关重要。每搏输出量的计算取决于血管壁位置和血管方向，而在心动周期中，由于心肌收缩和泵血作用，这两者都会发生变化。然而，当前用于定量4D PC-MRI数据分析的系统忽略了这种动态特性，而是采用静态三维血管近似方法。我们对在考虑和不考虑主动脉动态特性的情况下所获得的每搏输出量差异进行了量化。
我们描述了一种方法，该方法使用近似三维分割来自动初始化分割算法，这些算法需要每个时间点血管内部和外部的区域信息。这使得能够使用图割算法来获得四维分割，提取每个时间点包括中心线在内的血管表面，并得出运动信息。使用静态（三维）血管中的测量平面、动态血管内固定角度的平面（这对应于常见的二维PC-MRI）以及动态血管内的移动平面来比较每搏输出量的量化结果。
与放射科医生密切合作，对七个具有不同病变（如动脉瘤和缩窄）的数据集进行了评估。与专家手动估计的每搏输出量相比，考虑运动的量化方法平均比不考虑运动的计算方法性能高1.57%。不同方法获得的每搏输出量之间的平均差异为7.82%。自动获得的四维分割与手动生成的分割重叠率为85.75%。
在每搏输出量量化中纳入运动信息会带来轻微但无统计学意义的改善。所提出的方法对于临床常规操作是可行的，因为计算时间短且关键部分可完全自动运行。四维分割也可用于其他算法。同时进行可视化和量化可能有助于对心脏血流的理解和解读。
