Nillesen Maartje M, van Dijk Arie P J, Duijnhouwer Anthonie L, Thijssen Johan M, de Korte Chris L
Medical UltraSound Imaging Center (MUSIC), Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands.
Ultrasound Med Biol. 2016 Feb;42(2):596-606. doi: 10.1016/j.ultrasmedbio.2015.10.018. Epub 2015 Nov 28.
Assessment of right ventricular (RV) function is known to be of diagnostic value in patients with RV dysfunction. Because of its complex anatomic shape, automated determination of the RV volume is difficult and strong reliance on geometric assumptions is not desired. A method for automated RV assessment was developed using three-dimensional (3-D) echocardiography without relying on a priori knowledge of the cardiac anatomy. A 3-D adaptive filtering technique that optimizes the discrimination between blood and myocardium was applied to facilitate endocardial border detection. Filtered image data were incorporated in a segmentation model to automatically detect the endocardial RV border. End-systolic and end-diastolic RV volumes, as well as ejection fraction, were computed from the automatically segmented endocardial surfaces and compared against reference volumes manually delineated by two expert cardiologists. The results reported good performance in terms of correlation and agreement with the results from the reference volumes.
右心室（RV）功能评估在右心室功能障碍患者中具有诊断价值。由于右心室解剖形状复杂，自动测定其容积较为困难，且不希望过度依赖几何假设。一种利用三维（3-D）超声心动图自动评估右心室的方法被开发出来，该方法不依赖于心脏解剖的先验知识。应用一种优化血液与心肌区分度的三维自适应滤波技术，以促进心内膜边界检测。将滤波后的图像数据纳入分割模型，以自动检测右心室心内膜边界。根据自动分割的心内膜表面计算出收缩末期和舒张末期右心室容积以及射血分数，并与两位心脏病专家手动勾勒的参考容积进行比较。结果表明，在相关性以及与参考容积结果的一致性方面表现良好。
