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

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.

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

Patient-specific geometrical modeling of orthopedic structures with high efficiency and accuracy for finite element modeling and 3D printing

2015 ◽  
Vol 38 (4) ◽  
pp. 743-753 ◽  
Author(s):  
Huajun Huang ◽  
Chunling Xiang ◽  
Canjun Zeng ◽  
Hanbin Ouyang ◽  
Kelvin Kian Loong Wong ◽  
...  
Keyword(s):  
Finite Element ◽  
3D Printing ◽  
Finite Element Modeling ◽  
High Efficiency ◽  
Patient Specific ◽  
Element Modeling ◽  
Geometrical Modeling

scholarly journals Finite element modeling of shape memory polyurethane foams for treatment of cerebral aneurysms

2021 ◽  
Author(s):  
H. R. Jarrah ◽  
A. Zolfagharian ◽  
M. Bodaghi
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Shape Memory ◽  
Cerebral Aneurysms ◽  
Element Model ◽  
Polyurethane Foams ◽  
Patient Specific ◽  
Structural Cell ◽  
Element Modeling ◽  
Shape Memory Polyurethane

AbstractIn this paper, a thermo-mechanical analysis of shape memory polyurethane foams (SMPUFs) with aiding of a finite element model (FEM) for treating cerebral aneurysms (CAs) is introduced. Since the deformation of foam cells is extremely difficult to observe experimentally due to their small size, a structural cell-assembly model is established in this work via finite element modeling to examine all-level deformation details. Representative volume elements of random equilateral Kelvin open-cell microstructures are adopted for the cell foam. Also, a user-defined material subroutine (UMAT) is developed based on a thermo-visco-elastic constitutive model for SMPUFs, and implemented in the ABAQUS software package. The model is able to capture thermo-mechanical responses of SMPUFs for a full shape memory thermodynamic cycle. One of the latest treatments of CAs is filling the inside of aneurysms with SMPUFs. The developed FEM is conducted on patient-specific basilar aneurysms treated by SMPUFs. Three sizes of foams are selected for the filling inside of the aneurysm and then governing boundary conditions and loadings are applied to the foams. The results of the distribution of stress and displacement in the absence and presence of the foam are compared. Due to the absence of similar results in the specialized literature, this paper is likely to fill a gap in the state of the art of this problem and provide pertinent results that are instrumental in the design of SMPUFs for treating CAs.

An integrated framework for finite-element modeling of mitral valve biomechanics from medical images: Application to MitralClip intervention planning

2012 ◽  
Vol 16 (7) ◽  
pp. 1330-1346 ◽  
Author(s):  
Tommaso Mansi ◽  
Ingmar Voigt ◽  
Bogdan Georgescu ◽  
Xudong Zheng ◽  
Etienne Assoumou Mengue ◽  
...  
Keyword(s):  
Finite Element ◽  
Mitral Valve ◽  
Finite Element Modeling ◽  
Medical Images ◽  
Integrated Framework ◽  
Element Modeling ◽  
Intervention Planning

Patient Specific Finite Element Modeling of a Human Skull

2006 ◽  
Vol 49 ◽  
pp. 227-234 ◽  
Author(s):  
Norio Inou ◽  
Michihiko Koseki ◽  
Koutarou Maki
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Finite Element Modeling ◽  
Image Data ◽  
Element Model ◽  
Biomechanical Study ◽  
Modeling Method ◽  
Patient Specific ◽  
Human Skull ◽  
Element Modeling

This paper presents automated finite element modeling method and application to a biomechanical study. The modeling method produces a finite element model based on the multi-sliced image data adaptively controlling the element size according to complexity of local bony shape. The method realizes a compact and precise finite element model with a desired total number of nodal points. This paper challenges to apply this method to a human skull because of its intricate structure. To accomplish the application of the human skull, we analyze characteristics of bony shape for a mandible and a skull. Using the analytical results, we demonstrate that the proposed modeling method successfully generates a precise finite element model of the skull with fine structures.

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