心脏周期
仿射变换
二尖瓣
计算机科学
联轴节(管道)
人工智能
生物医学工程
心脏病学
计算机视觉
数据挖掘
模式识别(心理学)
放射科
数学
医学
工程类
几何学
机械工程
作者
Razvan Ioan Ionasec,Ingmar Voigt,Bogdan Georgescu,Wen‐Ching Yang,Helene Houle,Joachim Hornegger,Nassir Navab,Dorin Comanicìu
标识
DOI:10.1007/978-3-642-04271-3_93
摘要
The anatomy, function and hemodynamics of the aortic and mitral valves are known to be strongly interconnected. An integrated quantitative and visual assessment of the aortic-mitral coupling may have an impact on patient evaluation, planning and guidance of minimal invasive procedures. In this paper, we propose a novel model-driven method for functional and morphological characterization of the entire aortic-mitral apparatus. A holistic physiological model is hierarchically defined to represent the anatomy and motion of the two left heart valves. Robust learning-based algorithms are applied to estimate the patient-specific spatial-temporal parameters from four-dimensional TEE and CT data. The piecewise affine location of the valves is initially determined over the whole cardiac cycle using an incremental search performed in marginal spaces. Consequently, efficient spectrum detection in the trajectory space is applied to estimate the cyclic motion of the articulated model. Finally, the full personalized surface model of the aortic-mitral coupling is constructed using statistical shape models and local spatial-temporal refinement. Experiments performed on 65 4D TEE and 69 4D CT sequences demonstrated an average accuracy of 1.45 mm and speed of 60 seconds for the proposed approach. Initial clinical validation on model-based and expert measurement showed the precision to be in the range of the inter-user variability. To the best of our knowledge this is the first time a complete model of the aortic-mitral coupling estimated from TEE and CT data is proposed.
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