A patient-specific in vivo tumor model

Wasserman, Richard M.; Acharya, Raj; Sibata, Cláudio H.; Shin, Kyu H.

doi:10.1016/0025-5564(96)00045-4

Cited by 63 publications

(43 citation statements)

References 26 publications

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“…b) Outside The GTV1: Wasserman et al proposed in [33] to model the mechanical expansion of the tumor volume by a pressure P proportional to N/V, with N the total tumor cell count and V the total volume of the tumor. We propose a new equilibrium equation to model the mechanical impact of the tumor on the invaded structures outside the GTV1.…”

Section: A) Inside the Gtv1mentioning

confidence: 99%

Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation

Clatz

Sermesant

Bondiau

et al. 2005

IEEE Trans. Med. Imaging

318

415

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We propose a new model to simulate the 3D growth of glioblastomas multiforma (GBMs), the most aggressive glial tumors. The GBM speed of growth depends on the invaded tissue: faster in white than in gray matter, it is stopped by the dura or the ventricles. These different structures are introduced into the model using an atlas matching technique. The atlas includes both the segmentations of anatomical structures and diffusion information in white matter fibers.We use the finite element method (FEM) to simulate the invasion of the GBM in the brain parenchyma and its mechanical interaction with the invaded structures (mass effect). Depending on the considered tissue, the former effect is modeled with a reaction-diffusion or a Gompertz equation, while the latter is based on a linear elastic brain constitutive equation. In addition, we propose a new coupling equation taking into account the mechanical influence of the tumor cells on the invaded tissues. The tumor growth simulation is assessed by comparing the in-silico GBM growth with the real growth observed on two magnetic resonance images (MRIs) of a patient acquired with six months difference. Results show the feasibility of this new conceptual approach and justifies its further evaluation.

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Section: A) Inside the Gtv1mentioning

confidence: 99%

Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation

Clatz

Sermesant

Bondiau

et al. 2005

IEEE Trans. Med. Imaging

318

415

View full text Add to dashboard Cite

show abstract

“…There have been many different works on characterizing the mechanical properties of the brain tissue, which is deformable but not elastic. In [10] it is said that the brain tissue is a sponge like material, possessing instantaneous properties of elastic materials and time-dependent properties of the viscoelastic ones. Moreover, there is a great variation between elastic parameters of brain tissue within similar tissues as well as between differing tissues.…”

Section: Mechanical Modelsmentioning

confidence: 99%

“…Wasserman et al proposed one of the first mechanical models in [10]. In this 2D model they assume the brain tissue is a linear elastic material for which stress-strain relations can be given by generalized Hooke's law.…”

Section: Mechanical Modelsmentioning

confidence: 99%

Tumor Growth Modeling in Oncological Image Analysis

Konukoğlu

Pennec

Clatz

et al. 2009

Handbook of Medical Image Processing and Analysis

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“…Especially cellular automaton models, that describe the spread and invasion of a tumor on a cell interaction level, have become a popular tool. An approach that simulates the tumor growth on a rather macroscopic level using continuum mechanics was proposed in [10].…”

Section: Biomechanical Modeling Of Deformations Induced By Tumor Growthmentioning

confidence: 99%

A Framework for the Generation of Realistic Brain Tumor Phantoms and Applications

Rexilius

Hahn

Schlüter

et al. 2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

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Abstract.A quantitative analysis of brain tumors is an important factor that can have direct impact on a patient's prognosis and treatment. In order to achieve clinical relevance, reproducibility and especially accuracy of a proposed method have to be tested. We propose a framework for the generation of realistic digital phantoms of brain tumors of known volumes and their incorporation into an MR dataset of a healthy volunteer. Deformations that occur due to tumor growth inside the brain are simulated by means of a biomechanical model. Furthermore, a model for the amount of edema at each voxel is included as well as a simulation of contrast enhancement, which provides us with an additional characterization of the tumor. A "ground truth" is generally not available for brain tumors. Our proposed framework provides a flexible tool to generate representative datasets with known ground truth, which is essential for the validation and comparison of current and new quantitative approaches. Experiments are carried out using a semi-automated volumetry approach for a set of generated tumor datasets.

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A patient-specific in vivo tumor model

Cited by 63 publications

References 26 publications

Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation

Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation

Tumor Growth Modeling in Oncological Image Analysis

A Framework for the Generation of Realistic Brain Tumor Phantoms and Applications

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