Machine learning–based 3-D geometry reconstruction and modeling of aortic valve deformation using 3-D computed tomography images

Liang Liang, Fanwei Kong, Caitlin Martin, Thuy Pham, Qian Wang, James Duncan, Wei Sun

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

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.

Original languageEnglish (US)
Article numbere2827
JournalInternational Journal for Numerical Methods in Biomedical Engineering
Volume33
Issue number5
DOIs
StatePublished - May 2017
Externally publishedYes

Keywords

  • aortic valve finite element model
  • aortic valve geometry reconstruction
  • cardiac image analysis
  • machine learning

ASJC Scopus subject areas

  • Software
  • Biomedical Engineering
  • Modeling and Simulation
  • Molecular Biology
  • Computational Theory and Mathematics
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Machine learning–based 3-D geometry reconstruction and modeling of aortic valve deformation using 3-D computed tomography images'. Together they form a unique fingerprint.

Cite this