Asclepios Project || INRIA Sophia Antipolis || INRIA France

Research


Brain Tumor Computational Model

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Abstract

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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Image Guided Therapy

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Abstract

We present a new algorithm to register 3D preoperative Magnetic Resonance (MR) images to intra-operative MR images of the brain which have undergone brain shift. This algorithm relies on a robust estimation of the deformation from a sparse noisy set of measured displacements. We propose a new framework to compute the displacement field in an iterative process, allowing the solution to gradually move from an approximation formulation (minimizing the sum of a regularization term and a data error term) to an interpolation formulation (least square minimization of the data error term). An outlier rejection step is introduced in this gradual registration process using a weighted least trimmed squares approach, aiming at improving the robustness of the algorithm. We use a patient-specific model discretized with the finite element method (FEM) in order to ensure a realistic mechanical behavior of the brain tissue. To meet the clinical time constraint, we parallelized the slowest step of the algorithm so that we can perform a full 3D image registration in 35 seconds (including the image update time) on a heterogeneous cluster of 15 PCs. The algorithm has been tested on six cases of brain tumor resection, presenting a brain shift of up to 14 mm. The results show a good ability to recover large displacements, and a limited decrease of accuracy near the tumor resection cavity.

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Impact of Cellular Phones on Tissues

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Abstract

The ever-rising diffusion of cellular phones has brought about an increased concern for the possible consequences of electromagnetic radiation on human health. Possible thermal effects have been investigated, via experimentation or simulation, by several research projects in the last decade. Concerning numerical modeling, the power absorption in a user's head is generally computed using discretized models built from clinical MRI data. The vast majority of such numerical studies have been conducted using Finite Differences Time Domain methods, although strong limitations of their accuracy are due to heterogeneity, poor definition of the detailed structures of head tissues (staircasing effects), etc. In order to propose numerical modeling using Finite Element or Discontinuous Galerkin Time Domain methods, reliable automated tools for the unstructured discretization of human heads are also needed. Results presented in this article aim at filling the gap between human head MRI images and the accurate numerical modeling of wave propagation in biological tissues and its thermal effects.

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Hydrocephalus Model for Surgery Simulation

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Abstract

We propose a dynamic model of cerebrospinal fluid and intracranial pressure regulation. In this model, we investigate the coupling of biological parameters with a 3D model, to improve the behavior of the brain in surgical simulators. The model was assessed by comparing the simulated ventricular enlargement with a patient case study of communicating hydrocephalus. In our model, cerebro-spinal fluid production-resorption system is coupled with a 3D representation of the brain parenchyma. We introduce a new bi-phasic model of the brain (brain tissue and extracellular fluid) allowing for fluid exchange between the brain extracellular space and the venous system. The time evolution of ventricular pressure has been recorded on a symptomatic patient after closing the ventricular shunt. A finite element model has been built based on a CT scan of this patient, and quantitative comparisons between experimental measures and simulated data are proposed.

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DTMRI for Group Comparisons

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Abstract

Several nonrigid registration algorithms have been proposed for inter-subject alignment, used to construct statistical atlases and to identify group differences. Assessment of the accuracy of nonrigid registration algorithms is a essential and complex issue due to its intricate framework and its application-dependent behavior. We demonstrate that the diffusion MRI provides an independent means of assessing the quality of alignment achieved on the structural MRI. Diffusion tensor MRI enables the comparison of the local position and orientation of regions that appear homogeneous in conventional MRI. We carried out inter-subject alignment of conventional T1-weighted MRI with three different registration algorithms. Consequently, we projected DT-MRI of each subject through the same inter-subject transformation. The quality of the inter-subject alignment is assessed by estimating the consistency of the aligned DT-MRI using the Log- Euclidean framework.

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DTMRI registration

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Abstract

We propose an algorithm for the diffeomorphic registration of diffusion tensor images (DTI). Previous DTI registration algorithms using full tensor information suffer from difficulties in computing the differential of the Finite Strain tensor reorientation strategy. We borrow results from computer vision to derive an analytical gradient of the objective function. By leveraging on the closed-formgradient and the one-parameter subgroups of diffeomorphisms, the resulting registration algorithm is diffeomorphic and fast. Registration of a pair of 128x128x60 diffusion tensor volumes takes 15 minutes. We contrast the algorithm with a classic alternative that does not take into account the reorientation in the gradient computation. We show with 40 pairwise DTI registrations that using the exact gradient achieves significantly better registration.

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