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Automatic extraction of arterial centerline from whole-body computed tomography angiographic datasets

机译:从全身计算机断层扫描血管造影数据集中自动提取动脉中心线

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The aim of this study was to develop a new, fully-automated approach for the extraction of arterial centerlines from whole-body computed tomography angiographic (CTA) data sets for the planning of transcatheter aortic valve replacement (TAVR) procedures by the transfemoral approach. The method starts with an image pre-processing step to correct contrast inhomogeneities in different image slices, followed by the detection of anatomical landmarks and subsequent wave propagation, generating a cost matrix that represents the wave traveling time. Finally, the arterial centerlines are extracted from the aortic root to the femoral arteries using the conventional Dijkstra algorithm. 36 patients who underwent TAVR procedures with whole-body CTA scans were studied; 15 for training and the remaining 21 for testing. In 1 training case and 2 testing cases, user interaction was needed to correct the centerline (in the femoral artery). In the remaining 33 cases (91.7%), a fully-automated centerline was obtained with excellent results. In all cases, the average root mean square error was 2.55±0.70mm (training 2.26±0.48mm versus testing 2.77±0.77mm, p = 0.03) and the average mean error was 1.63±0.40mm (training 1.46±0.35mm versus testing 1.74±0.39mm, p = 0.02).
机译:这项研究的目的是开发一种新的,全自动的方法,从全身计算机断层摄影血管造影(CTA)数据集中提取动脉中心线,以通过经股动脉方法计划经导管主动脉瓣置换(TAVR)程序。该方法从图像预处理步骤开始,以纠正不同图像切片中的对比度不均匀性,然后检测解剖学界标和随后的波传播,生成表示波传播时间的成本矩阵。最后,使用传统的Dijkstra算法从主动脉根部到股动脉提取动脉中心线。研究了36例行全身CTA扫描的TAVR程序的患者。 15个用于培训,其余21个用于测试。在1个训练案例和2个测试案例中,需要用户交互以校正中心线(在股动脉中)。在其余的33例(91.7%)中,获得了全自动中心线,效果极佳。在所有情况下,平均均方根误差为2.55±0.70mm(训练时为2.26±0.48mm,测试为2.77±0.77mm,p = 0.03),平均均方差为1.63±0.40mm(训练时为1.46±0.35mm 1.74±0.39mm,p = 0.02)。

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