FEM Based 3D Tumor Growth Prediction for Kidney Tumor View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

Xinjian Chen , Ronald Summers , Jianhua Yao

ABSTRACT

It is important to predict the tumor growth so that appropriate treatment can be planned especially in the early stage. In this paper, we propose a finite element method (FEM) based 3D tumor growth prediction system using longitudinal kidney tumor images. To the best of our knowledge, this is the first kidney tumor growth prediction system. The kidney tissues are classified into three types: renal cortex, renal medulla and renal pelvis. The reaction-diffusion model is applied as the tumor growth model. Different diffusion properties are considered in the model: the diffusion for renal medulla is considered as anisotropic, while those of renal cortex and renal pelvis are considered as isotropic. The FEM is employed to simulate the diffusion model. Automated estimation of the model parameters is performed via optimization of an objective function reflecting overlap accuracy, which is optimized in parallel via HOPSPACK (hybrid optimization parallel search). An exponential curve fitting based on the non-linear least squares method is used for multi-time point model parameters prediction. The proposed method was tested on the seven time points longitudinal kidney tumor CT studies from two patients with five tumors. The experimental results showed the feasibility and efficacy of the proposed method. More... »

PAGES

159-168

References to SciGraph publications

  • 2007. A Coupled Finite Element Model of Tumor Growth and Vascularization in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2007
  • 1964-09. Dynamics of Tumor Growth in BRITISH JOURNAL OF CANCER
  • 2008-06. An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects in JOURNAL OF MATHEMATICAL BIOLOGY
  • 2001-10. A general model for ontogenetic growth in NATURE
  • 2005. Finite Element Modeling of Brain Tumor Mass-Effect from 3D Medical Images in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2005
  • Book

    TITLE

    Medical Imaging and Augmented Reality

    ISBN

    978-3-642-15698-4
    978-3-642-15699-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15699-1_17

    DOI

    http://dx.doi.org/10.1007/978-3-642-15699-1_17

    DIMENSIONS

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