Motion-aware stroke volume quantification in 4D PC-MRI data of the human aorta

Purpose 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. Methods 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. Results 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. Conclusion 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.