2018
DOI: 10.1002/cnm.3103
|View full text |Cite
|
Sign up to set email alerts
|

A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero‐pressure geometry of human thoracic aorta

Abstract: Advances in structural finite element analysis (FEA) and medical imaging have made it possible to investigate the in vivo biomechanics of human organs such as blood vessels, for which organ geometries at the zero-pressure level need to be recovered. Although FEA-based inverse methods are available for zero-pressure geometry estimation, these methods typically require iterative computation, which are time-consuming and may be not suitable for time-sensitive clinical applications. In this study, by using machine… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
40
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
7

Relationship

1
6

Authors

Journals

citations
Cited by 37 publications
(41 citation statements)
references
References 56 publications
1
40
0
Order By: Relevance
“…This assumption, which will be tacked in a future study, could lead to uncertainties on the computed stresses as well as in the tissue compliance [48][49][50]. The pre-stress assumption of the native leaflets might have a small impact on the solution since the transvalvular pressure is very small during TAV deployment.…”
Section: Limitationsmentioning
confidence: 99%
“…This assumption, which will be tacked in a future study, could lead to uncertainties on the computed stresses as well as in the tissue compliance [48][49][50]. The pre-stress assumption of the native leaflets might have a small impact on the solution since the transvalvular pressure is very small during TAV deployment.…”
Section: Limitationsmentioning
confidence: 99%
“…The unit covariance with the sum of the marginal Negentrophy maximization is performed via Lagrange, which is shown in Equation (13)…”
Section: Negentropy Maximization For Independent Feature Extractionmentioning
confidence: 99%
“…Depending upon the given D andL M ð Þ, the feature X with its classification accuracy is measured using a function ψ, and k is the constant, which is similar to Equation (13). The learning rule is explained as below.…”
Section: Negentropy Maximization For Independent Feature Extractionmentioning
confidence: 99%
See 1 more Smart Citation
“…The application of the auto encoder deep learning approach indicates a lower reconstruction error, which is compared with the original FEA model. The similar idea can also be applied to estimate, calibrate, and recover the zero-pressure geometry of the patient's thoracic aorta is developed on the basis of the FEA method [30]. The application of different machine learning methods can simplify, predict, and calibrate the complex FEA computations, thus, solving medical problems.…”
Section: Fea Computation and Calibration With Machine Learningmentioning
confidence: 99%