Research
Brain Tumor Computational Model
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.
Related Articles
- Ender Konukoglu, Olivier Clatz, Pierre-Yves Bondiau, Hervé Delingette, and Nicholas Ayache.
Extrapolating Tumor Invasion Margins for Physiologically Determined Radiotherapy Regions.
MICCAI 2006.
- Olivier Clatz, Emmanuel Mandonnet, Stéphane Chanalet, Christine Lebrun, Ender Konukoglu, Hervé Delingette, Nicholas Ayache, and Pierre-Yves Bondiau.
Modèles Biomathématiques de Croissance Des Gliomes : Recherche en Informatique et Perspectives en Neuro-oncologie.
Neurologies, 2006.
- Olivier Clatz, Maxime Sermesant, Pierre-Yves Bondiau, Hervé Delingette, Simon K. Warfield, Grégoire Malandain, and Nicholas Ayache.
Realistic Simulation of the 3D Growth of Brain Tumors in MR Images Coupling Diffusion with Mass Effect.
IEEE Transactions on Medical Imaging, 2005.
Image Guided Therapy
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.
Related Articles
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Neculai Archip, Olivier Clatz, Stephen Whalen, Dan Kacher, Andriy Fedorov, Andriy Kot, Nikos Chrisochoides, Ferenc Jolesz, Alexandra Golby, Peter Black, and Simon Warfield.
Non-rigid alignment of preoperative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery.
Neuroimage 2007.
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Olivier Clatz, Hervé Delingette, Ion-Florin Talos, Alexandra J. Golby, Ron Kikinis, Ferenc Jolesz, Nicholas Ayache, and Simon Warfield.
Robust Non-Rigid Registration to Capture Brain Shift from Intra-Operative MRI.
IEEE Transactions on Medical Imaging, 2005.
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Nikos Chrisochoides, Andriy Fedorov, Andriy Kot, Neculai Archip, Peter Black, Olivier Clatz, Alexandra Golby, Ron Kikinis, and Simon K. Warfield.
Toward Real-Time, Image Guided Neurosurgery Using Distributed and Grid Computing.
SuperComputing 2006.
Impact of Cellular Phones on Tissues
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.
Related Articles
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Gilles Scarella, Olivier Clatz, Stéphane Lanteri, Grégory Beaume, Steve Oudot, Jean-Philippe Pons, Serge Piperno, Patrick Joly, and Joe Wiart.
Realistic numerical modelling of human head tissue exposure to electromagnetic waves from cellular phones.
Comptes Rendus de l'Académie des Sciences - Physics, 2006.
Hydrocephalus Model for Surgery Simulation
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.
Related Articles
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Olivier Clatz, Stéphane Litrico, Hervé Delingette, and Nicholas Ayache.
Dynamic Model of Communicating Hydrocephalus for Surgery Simulation.
IEEE Transactions on Biomedical Engineering, 2006.
DTMRI for Group Comparisons
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.
Related Articles
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Franciso Javier Sanchez Castro, Olivier Clatz, Julien Dauguet, Neculai Archip, Jean.-Philippe Thiran, Simon Warfield
Evaluation Of Brain Image Nonrigid Registration Algorithms Based on Log-Euclidean MR-DTI Consistency Measures.
ISBI 2006.
DTMRI registration
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.
Related Articles
-
Boon Thye Thomas Yeo, Tom Vercauteren, Pierre Fillard, Xavier Pennec, Polina Golland, Nicholas Ayache, Olivier Clatz.
DTI Registration with Exact Finite-Strain Differential.
In Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'08),Paris, France, May 2008.IEEE.
