Platform for patient-specific finite-element modeling and application for radiofrequency ablation

2012 ◽  
Author(s):  
Christian Rossmann ◽  
Frank Rattay ◽  
Dieter Haemmerich
Keyword(s):  
Finite Element ◽  
Radiofrequency Ablation ◽  
Finite Element Modeling ◽  
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 methodology for constraining power in finite element modeling of radiofrequency ablation

2016 ◽  
Vol 33 (7) ◽  
pp. e2834 ◽  
Author(s):  
Yansheng Jiang ◽  
Ricardo Possebon ◽  
Stefaan Mulier ◽  
Chong Wang ◽  
Feng Chen ◽  
...  
Keyword(s):  
Finite Element ◽  
Radiofrequency Ablation ◽  
Finite Element Modeling ◽  
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.

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 cooled-tip probe radiofrequency ablation processes in liver tissue

2008 ◽  
Vol 38 (6) ◽  
pp. 694-708 ◽  
Author(s):  
Rimantas Barauskas ◽  
Antanas Gulbinas ◽  
Tomas Vanagas ◽  
Giedrius Barauskas
Keyword(s):  
Finite Element ◽  
Radiofrequency Ablation ◽  
Finite Element Modeling ◽  
Liver Tissue ◽  
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.

scholarly journals A Deployable Multi-Tine Endoscopic Radiofrequency Ablation Electrode: Simulation Validation in a Thermochromic Tissue Phantom

2019 ◽  
Author(s):  
Bradley Hanks ◽  
Fariha Azhar ◽  
Mary Frecker ◽  
Ryan Clement ◽  
Jenna Greaser ◽  
...  
Keyword(s):  
Finite Element ◽  
Radiofrequency Ablation ◽  
Finite Element Modeling ◽  
Thermal Ablation ◽  
Ablation Zone ◽  
Element Modeling ◽  
Tissue Phantom ◽  
Electrode Shape ◽  
Endoscopic Radiofrequency Ablation ◽  
Ablation Model

Endoscopic radiofrequency ablation has gained interest for treating abdominal tumors. The radiofrequency ablation electrode geometry largely determines the size and shape of the ablation zone. Mismatch between the ablation zone and tumor shapes leads to reoccurrence of the cancer. Recently, work has been published regarding a novel deployable multi-tine electrode for endoscopic radiofrequency ablation. The prior work developed a thermal ablation model to predict the ablation zone surrounding an electrode and a systematic optimization of the electrode shape to treat a specific tumor shape. The purpose of this work is to validate the thermal ablation model through experiments in a tissue phantom that changes color at ablation temperatures. The experiments highlight the importance of thermal tissue damage in finite element modeling. Thermal induced changes in tissue properties, if not accounted for in finite element modeling, can lead to significant overprediction of the expected ablation zone surrounding an electrode.

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