IEEE Trans Med Imaging. 2023 Aug;42(8):2360-2373. doi: 10.1109/TMI.2023.3251734. Epub 2023 Aug 1.
We present a method to automatically segment 4D flow magnetic resonance imaging (MRI) by identifying net flow effects using the standardized difference of means (SDM) velocity. The SDM velocity quantifies the ratio between the net flow and observed flow pulsatility in each voxel. Vessel segmentation is performed using an F-test, identifying voxels with significantly higher SDM velocity values than background voxels. We compare the SDM segmentation algorithm against pseudo-complex difference (PCD) intensity segmentation of 4D flow measurements in in vitro cerebral aneurysm models and 10 in vitro Circle of Willis (CoW) datasets. We also compared the SDM algorithm to convolutional neural network (CNN) segmentation in 5 thoracic vasculature datasets. The in vitro flow phantom geometry is known, while the ground truth geometries for the CoW and thoracic aortas are derived from high-resolution time-of-flight (TOF) magnetic resonance angiography and manual segmentation, respectively. The SDM algorithm demonstrates greater robustness than PCD and CNN approaches and can be applied to 4D flow data from other vascular territories. The SDM to PCD comparison demonstrated an approximate 48% increase in sensitivity in vitro and 70% increase in the CoW, respectively; the SDM and CNN sensitivities were similar. The vessel surface derived from the SDM method was 46% closer to the in vitro surfaces and 72% closer to the in vitro TOF surfaces than the PCD approach. The SDM and CNN approaches both accurately identify vessel surfaces. The SDM algorithm is a repeatable segmentation method, enabling reliable computation of hemodynamic metrics associated with cardiovascular disease.
我们提出了一种通过使用标准化差值（SDM）速度来识别净流动效应来自动分割 4D 流磁共振成像（MRI）的方法。SDM 速度量化了每个体素中净流动与观察到的流动脉动之间的比率。使用 F 检验进行血管分割，识别出 SDM 速度值明显高于背景体素的体素。我们将 SDM 分割算法与体外脑动脉瘤模型和 10 个体外环 Willis（CoW）数据集的 4D 流测量的伪复数差（PCD）强度分割进行了比较。我们还将 SDM 算法与 5 个胸血管数据集的卷积神经网络（CNN）分割进行了比较。体外流幻影的几何形状是已知的，而 CoW 和胸主动脉的真实几何形状分别来自高分辨率时间飞跃（TOF）磁共振血管造影和手动分割。SDM 算法比 PCD 和 CNN 方法更具鲁棒性，可以应用于其他血管区域的 4D 流数据。SDM 与 PCD 的比较表明，体外的敏感性分别增加了约 48%，CoW 增加了 70%；SDM 和 CNN 的敏感性相似。SDM 方法得出的血管表面比 PCD 方法更接近体外表面，分别为 46%和 72%；SDM 和 CNN 方法都能准确地识别血管表面。SDM 算法是一种可重复的分割方法，能够可靠地计算与心血管疾病相关的血流动力学指标。
