title: Machine learning-based 3-D geometry reconstruction and modeling of aortic valve deformation using 3-D computed tomography images.
abstract: To conduct a patient-specific computational modeling of the aortic valve, 3-D aortic valve anatomic geometries of an individual patient need to be reconstructed from clinical 3-D cardiac images. Currently, most of computational studies involve manual heart valve geometry reconstruction and manual finite element (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 allow 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 3-D geometries of the aortic valve from computed tomographic 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 10 patients with 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 middiastole was simulated for 7 patients and validated by comparing the deformed geometries with 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 preoperative planning system for aortic valve disease diagnosis and treatment.
authors.affiliation: Tissue Mechanics Laboratory The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.|Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.
authors.forename: Liang
authors.initials: L
authors.lastname: Liang
authors.affiliation: Tissue Mechanics Laboratory The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
authors.forename: Fanwei
authors.initials: F
authors.lastname: Kong
authors.affiliation: Tissue Mechanics Laboratory The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
authors.forename: Caitlin
authors.initials: C
authors.lastname: Martin
authors.affiliation: Tissue Mechanics Laboratory The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
authors.forename: Thuy
authors.initials: T
authors.lastname: Pham
authors.affiliation: Tissue Mechanics Laboratory The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut, USA.|Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA.|Department of Electrical Engineering, Yale University, New Haven, Connecticut, USA.
authors.forename: James
authors.initials: J
authors.lastname: Duncan
authors.affiliation: Tissue Mechanics Laboratory The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
mesh_terms: D001021:Aortic Valve / Q000000981:diagnostic imaging* / Q000473:pathology*; D003198:Computer Simulation; D020342:Finite Element Analysis; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D000069550:Machine Learning; D014057:Tomography, X-Ray Computed
keywords: aortic valve finite element model; aortic valve geometry reconstruction; cardiac image analysis; machine learning