Finite Element Modeling of Mitral Valve Dynamic Deformation Using Patient-Specific Multi-Slices Computed Tomography Scans

2012 ◽  
Vol 41 (1) ◽  
pp. 142-153 ◽  
Author(s):  
Qian Wang ◽  
Wei Sun
Keyword(s):  
Computed Tomography ◽  
Finite Element ◽  
Mitral Valve ◽  
Finite Element Modeling ◽  
Dynamic Deformation ◽  
Patient Specific ◽  
Element Modeling ◽  
Computed Tomography Scans

scholarly journals Mitral Valve Patient-Specific Finite Element Modeling from Cardiac MRI: Application to an Annuloplasty Procedure

2011 ◽  
Vol 2 (2) ◽  
pp. 66-76 ◽  
Author(s):  
Marco Stevanella ◽  
Francesco Maffessanti ◽  
Carlo A. Conti ◽  
Emiliano Votta ◽  
Alice Arnoldi ◽  
...  
Keyword(s):  
Finite Element ◽  
Mitral Valve ◽  
Finite Element Modeling ◽  
Cardiac Mri ◽  
Patient Specific ◽  
Element Modeling

scholarly journals Method for patient-specific finite element modeling and simulation of deep brain stimulation

2008 ◽  
Vol 47 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Mattias Åström ◽  
Ludvic U. Zrinzo ◽  
Stephen Tisch ◽  
Elina Tripoliti ◽  
Marwan I. Hariz ◽  
...  
Keyword(s):  
Finite Element ◽  
Deep Brain Stimulation ◽  
Finite Element Modeling ◽  
Modeling And Simulation ◽  
Brain Stimulation ◽  
Patient Specific ◽  
Element Modeling ◽  
Deep Brain

Finite element modeling of nano porous sintered silver material using computed tomography images

2019 ◽  
Vol 50 (5) ◽  
pp. 533-538 ◽  
Author(s):  
B. Meyghani ◽  
M. Awang ◽  
P. Bokam ◽  
B. Plank ◽  
C. Heinzl ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Finite Element ◽  
Finite Element Modeling ◽  
Element Modeling ◽  
Computed Tomography Images ◽  
Sintered Silver

scholarly journals Four-Dimensional Computed Tomography-Based Finite Element Modeling of the Behavior of the Right Coronary Artery

2017 ◽  
Vol 81 (7) ◽  
pp. 1059-1061
Author(s):  
Yoshihisa Nakagawa ◽  
Hidetaka Hayashi ◽  
Chisato Izumi ◽  
Hirokazu Kondo ◽  
Toshihiro Tamura ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Finite Element ◽  
Coronary Artery ◽  
Finite Element Modeling ◽  
Right Coronary Artery ◽  
Element Modeling ◽  
The Right

Finite element modeling of fluid-saturated metallic foams from micro-computed tomography

2018 ◽  
Vol 144 (3) ◽  
pp. 1764-1764
Author(s):  
Mark J. Cops ◽  
James G. McDaniel ◽  
Elizabeth A. Magliula ◽  
David J. Bamford
Keyword(s):  
Computed Tomography ◽  
Finite Element ◽  
Finite Element Modeling ◽  
Metallic Foams ◽  
Micro Computed Tomography ◽  
Element Modeling

A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model

2021 ◽  
Author(s):  
Balaji Rengarajan ◽  
Sourav Patnaik ◽  
Ender A. Finol
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Modeling ◽  
Wall Stress ◽  
Aortic Aneurysms ◽  
Multivariate Adaptive Regression Splines ◽  
Patient Specific ◽  
Clinical Images ◽  
Element Modeling ◽  
Abdominal Aortic

Abstract In the present work, we investigated the use of geometric indices to predict patient-specific abdominal aortic aneurysm (AAA) wall stress by means of a novel neural network (NN) modeling approach. We conducted a retrospective review of existing clinical images of two patient groups: 98 asymptomatic and 50 symptomatic AAA. The images were subject to a protocol consisting of image segmentation, processing, volume meshing, finite element modeling, and geometry quantification, from which 53 geometric indices and the spatially averaged wall stress (SAWS) were calculated. We developed feed-forward NN models composed of an input layer, two dense layers, and an output layer using Keras, a deep learning library in Python. The NN models were trained, tested, and validated independently for both AAA groups using all geometric indices, as well as a reduced set of indices resulting from a variable reduction procedure. We compared the performance of the NN models with two standard machine learning algorithms (MARS: multivariate adaptive regression splines and GAM: generalized additive model) and a linear regression model (GLM: generalized linear model). The NN-based approach exhibited the highest overall mean goodness-of-fit and lowest overall relative error compared to MARS, GAM, and GLM, when using the reduced sets of indices to predict SAWS for both AAA groups. The use of NN modeling represents a promising alternative methodology for the estimation of AAA wall stress using geometric indices as surrogates, in lieu of finite element modeling.

Optimal bone biopsy route to the proximal femur evaluated by computed tomography-based finite element modeling

2021 ◽  
Author(s):  
Kou Hayashi ◽  
Munenori Watanuki ◽  
Yoshihiro Hagiwara ◽  
Nobuyuki Yamamoto ◽  
Masami Hosaka ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Finite Element ◽  
Finite Element Modeling ◽  
Proximal Femur ◽  
Bone Biopsy ◽  
Element Modeling

Female patient-specific finite element modeling of pelvic organ prolapse (POP)

2015 ◽  
Vol 48 (2) ◽  
pp. 238-245 ◽  
Author(s):  
Zhuo-Wei Chen ◽  
Pierre Joli ◽  
Zhi-Qiang Feng ◽  
Mehdi Rahim ◽  
Nicolas Pirró ◽  
...  
Keyword(s):  
Finite Element ◽  
Pelvic Organ Prolapse ◽  
Finite Element Modeling ◽  
Female Patient ◽  
Pelvic Organ ◽  
Patient Specific ◽  
Element Modeling

Finite Element Modeling of 3D Orthogonal Woven C/C Composite Based on Micro-Computed Tomography Experiment

2013 ◽  
Vol 21 (4) ◽  
pp. 603-614 ◽  
Author(s):  
Ai Shigang ◽  
Zhu Xiaolei ◽  
Mao Yiqi ◽  
Pei Yongmao ◽  
Fang Daining
Keyword(s):  
Computed Tomography ◽  
Finite Element ◽  
Finite Element Modeling ◽  
Micro Computed Tomography ◽  
Element Modeling ◽  
3D Orthogonal Woven
Sign in / Sign up

Export Citation Format

Share Document