Literature DB >> 16685871

Finite element modeling of brain tumor mass-effect from 3D medical images.

Ashraf Mohamed1, Christos Davatzikos.   

Abstract

Motivated by the need for methods to aid the deformable registration of brain tumor images, we present a three-dimensional (3D) mechanical model for simulating large non-linear deformations induced by tumors to the surrounding encephalic tissues. The model is initialized with 3D radiological images and is implemented using the finite element (FE) method. To simulate the widely varying behavior of brain tumors, the model is controlled by a number of parameters that are related to variables such as the bulk tumor location, size, mass-effect, and peri-tumor edema extent. Model predictions are compared to real brain tumor-induced deformations observed in serial-time MRI scans of a human subject and 3 canines with surgically transplanted gliomas. Results indicate that the model can reproduce the real deformations with an accuracy that is similar to that of manual placement of landmark points to which the model is compared.

Entities:  

Mesh:

Year:  2005        PMID: 16685871     DOI: 10.1007/11566465_50

Source DB:  PubMed          Journal:  Med Image Comput Comput Assist Interv


  20 in total

1.  Deformable registration of glioma images using EM algorithm and diffusion reaction modeling.

Authors:  Ali Gooya; George Biros; Christos Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2010-09-27       Impact factor: 10.048

2.  A comparative study of biomechanical simulators in deformable registration of brain tumor images.

Authors:  Evangelia I Zacharaki; Cosmina S Hogea; George Biros; Christos Davatzikos
Journal:  IEEE Trans Biomed Eng       Date:  2008-03       Impact factor: 4.538

3.  ORBIT: a multiresolution framework for deformable registration of brain tumor images.

Authors:  Evangelia I Zacharaki; Dinggang Shen; Seung-Koo Lee; Christos Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2008-08       Impact factor: 10.048

4.  Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy.

Authors:  Jared A Weis; Michael I Miga; Thomas E Yankeelov
Journal:  Comput Methods Appl Mech Eng       Date:  2016-09-01       Impact factor: 6.756

5.  Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference.

Authors:  Jana Lipkova; Panagiotis Angelikopoulos; Stephen Wu; Esther Alberts; Benedikt Wiestler; Christian Diehl; Christine Preibisch; Thomas Pyka; Stephanie E Combs; Panagiotis Hadjidoukas; Koen Van Leemput; Petros Koumoutsakos; John Lowengrub; Bjoern Menze
Journal:  IEEE Trans Med Imaging       Date:  2019-02-27       Impact factor: 10.048

6.  FEM-based 3-D tumor growth prediction for kidney tumor.

Authors:  Xinjian Chen; Ronald Summers; Jianhua Yao
Journal:  IEEE Trans Biomed Eng       Date:  2011-03       Impact factor: 4.538

7.  Pancreatic Tumor Growth Prediction With Elastic-Growth Decomposition, Image-Derived Motion, and FDM-FEM Coupling.

Authors:  Ken C L Wong; Ronald M Summers; Electron Kebebew; Jianhua Yao
Journal:  IEEE Trans Med Imaging       Date:  2016-08-02       Impact factor: 10.048

8.  Kidney tumor growth prediction by coupling reaction-diffusion and biomechanical model.

Authors:  Xinjian Chen; Ronald M Summers; Jianhua Yao
Journal:  IEEE Trans Biomed Eng       Date:  2012-10-02       Impact factor: 4.538

9.  Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growth.

Authors:  Evangelia I Zacharaki; Cosmina S Hogea; Dinggang Shen; George Biros; Christos Davatzikos
Journal:  Neuroimage       Date:  2009-07-01       Impact factor: 6.556

10.  Pancreatic Tumor Growth Prediction with Multiplicative Growth and Image-Derived Motion.

Authors:  Ken C L Wong; Ronald M Summers; Electron Kebebew; Jianhua Yao
Journal:  Inf Process Med Imaging       Date:  2015
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.