FEM based 3D tumor growth prediction for kidney tumor

Authors:
Xinjian Chen;Ronald Summers;Jianhua Yao
Affiliations:
Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health, Bethesda, MD;Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health, Bethesda, MD;Radiology and Imaging Sciences Department, Clinical Center, National Institute of Health, Bethesda, MD
Venue:
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
Year:
2010

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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.