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Machine Learning based 3D Geometry Reconstruction and Modeling of Aortic Valve Deformation Using 3D CT Images

机译：基于机器学习的3D CT图像的3D几何重建和主动脉瓣变形建模

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摘要

To conduct a patient-specific computational modeling of the aortic valve, 3D aortic valve anatomic geometries of an individual patient need to be reconstructed from clinical 3D cardiac images. Currently, most of computational studies involve manual heart valve geometry reconstruction and manual FE model generation, which is both time-consuming and prone to human errors. A seamless computational modeling framework, which can automate this process based on machine learning algorithms, is desirable, as it can not only eliminate human errors and ensure the consistency of the modeling results, but also allows fast feedback to clinicians and permits a future population-based probabilistic analysis of large patient cohorts. In this study, we developed a novel computational modeling method to automatically reconstruct the 3D geometries of the aortic valve from CT images. The reconstructed valve geometries have built-in mesh correspondence, which bridges harmonically for the consequent FE modeling. The proposed method was evaluated by comparing the reconstructed geometries from ten patients to those manually created by human experts, and a mean discrepancy of 0.69 mm was obtained. Based on these reconstructed geometries, FE models of valve leaflets were developed, and aortic valve closure from end systole to mid-diastole was simulated for seven patients and validated by comparing the deformed geometries to those manually created by human experts, and a mean discrepancy of 1.57 mm was obtained. The proposed method offers great potential to streamline the computational modeling process and enables the development of a pre-operative planning system for aortic valve disease diagnosis and treatment.
机译：为了进行主动脉瓣的患者特定计算建模，需要从临床3D心脏图像重建单个患者的3D主动脉瓣解剖结构。当前，大多数计算研究涉及人工心脏瓣膜几何结构重建和人工有限元模型生成，这既费时又容易出现人为错误。需要一个无缝的计算建模框架，该框架可以基于机器学习算法自动执行此过程，因为它不仅可以消除人为错误并确保建模结果的一致性，还可以快速反馈给临床医生并允许将来的人群-基于大型患者队列的概率分析。在这项研究中，我们开发了一种新颖的计算建模方法，可以根据CT图像自动重建主动脉瓣的3D几何形状。重构后的阀门几何形状具有内置的网格对应关系，可为随后的有限元建模谐波地进行桥接。通过将十位患者的重建几何形状与人类专家手动创建的几何形状进行比较，对所提出的方法进行了评估，得出的平均差异为0.69 mm。基于这些重构的几何形状，开发了瓣叶的有限元模型，并模拟了七名患者的从收缩期末期到舒张中期的主动脉瓣关闭，并通过将变形的几何形状与人类专家手动创建的几何形状进行了比较，并验证了平均差异。获得1.57mm。所提出的方法具有极大的潜力来简化计算建模过程，并能够开发用于主动脉瓣疾病诊断和治疗的术前计划系统。

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