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Nicholas Ayache

Publications and source records attributed to Nicholas Ayache.

27 records · Page 2Linked to original sources
PubMed2005

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

We propose a new model to simulate the three-dimensional (3-D) 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 6 mo difference. Results show the feasibility of this new conceptual approach and justifies its further evaluation.

Algorithms↗
PubMed2004

3D tomographic reconstruction of coronary arteries using a precomputed 4D motion field.

In this paper, we present a new method to perform 3D tomographic reconstruction of coronary arteries from cone-beam rotational x-ray angiography acquisitions. We take advantage of the precomputation of the coronary artery motion, modelled as a parametric 4D motion field. Contrary to data gating or data triggering approaches, we homogeneously use all available frames, independently of the cardiac phase. In addition, we artificially subtract angiograms from their background structures. Our method significantly improves the reconstruction, by removing both motion and background artefacts. We have successfully tested it on the datasets from a synthetic phantom and 10 patients.

Algorithms↗
PubMed2004

Grid powered nonlinear image registration with locally adaptive regularization.

Multi-subject non-rigid registration algorithms using dense deformation fields often encounter cases where the transformation to be estimated has a large spatial variability. In these cases, linear stationary regularization methods are not sufficient. In this paper, we present an algorithm that uses a priori information about the nature of imaged objects in order to adapt the regularization of the deformations. We also present a robustness improvement that gives higher weight to those points in images that contain more information. Finally, a fast parallel implementation using networked personal computers is presented. In order to improve the usability of the parallel software by a clinical user, we have implemented it as a grid service that can be controlled by a graphics workstation embedded in the clinical environment. Results on inter-subject pairs of images show that our method can take into account the large variability of most brain structures. The registration time for images of size 256 x 256 x 124 is 5 min on 15 standard PCs. A comparison of our non-stationary visco-elastic smoothing versus solely elastic or fluid regularizations shows that our algorithm converges faster towards a more optimal solution in terms of accuracy and transformation regularity.

Algorithms↗
PubMed2004

Generalized image models and their application as statistical models of images.

A generalized image model (GIM) is presented. Images are represented as sets of four-dimensional (4D) sites combining position and intensity information, as well as their associated uncertainty and joint variation. This model seamlessly allows for the representation of both images and statistical models (such as those used for classification of normal/abnormal anatomy and for inter-patient registration), as well as other representations such as landmarks or meshes. A GIM-based registration method aimed at the construction and application of statistical models of images is proposed. A procedure based on the iterative closest point (ICP) algorithm is modified to deal with features other than position and to integrate statistical information. Furthermore, we modify the ICP framework by using a Kalman filter to efficiently compute the transformation. The initialization and update of the statistical model are also described. Preliminary results show the feasibility of the approach and its potentialities.

Algorithms↗
PubMed2004

Expert knowledge-guided segmentation system for brain MRI.

We describe an automated 3-D segmentation system for in vivo brain magnetic resonance images (MRI). Our segmentation method combines a variety of filtering, segmentation, and registration techniques and makes maximum use of the available a priori biomedical expertise, both in an implicit and an explicit form. We approach the issue of boundary finding as a process of fitting a group of deformable templates (simplex mesh surfaces) to the contours of the target structures. These templates evolve in parallel, supervised by a series of rules derived from analyzing the template's dynamics and from medical experience. The templates are also constrained by knowledge on the expected textural and shape properties of the target structures. We apply our system to segment four brain structures (corpus callosum, ventricles, hippocampus, and caudate nuclei) and discuss its robustness to imaging characteristics and acquisition noise.

Algorithms↗
PubMed2002

Automatic detection and segmentation of evolving processes in 3D medical images: Application to multiple sclerosis.

The study of temporal series of medical images can be helpful for physicians to perform pertinent diagnoses and to help them in the follow-up of a patient: in some diseases, lesions, tumors or anatomical structures vary over time in size, position, composition, etc., either because of a natural pathological process or under the effect of a drug or a therapy. It is a laborious and subjective task to visually and manually analyze such images. Thus the objective of this work was to automatically detect regions with apparent local volume variation with a vector field operator applied to the local displacement field obtained after a non-rigid registration between two successive temporal images. On the other hand, quantitative measurements, such as the volume variation of lesions or segmentation of evolving lesions, are important. By studying the information of apparent shrinking areas in the direct and reverse displacement fields between images, we are able to segment evolving lesions. Then we propose a method to segment lesions in a whole temporal series of images. In this article we apply this approach to automatically detect and segment multiple sclerosis lesions that evolve in time series of MRI scans of the brain. At this stage, we have only applied the approach to a few experimental cases to demonstrate its potential. A clinical validation remains to be done, which will require important additional work.

Automation↗
PubMed2002

[Non-linear elastic deformable models for real-time surgery simulation].

In this article, we describe the latest developments of the minimally invasive hepatic surgery simulator prototype developed at INRIA. A key problem with such a simulator is the physical modelling of soft tissues. We propose a new deformable model based on non-linear elasticity and the finite element method. This model is valid for large displacements, which means in particular that it is invariant with respect to rotations. This property improves the realism of the deformations and solves the problems related to the shortcomings of linear elasticity, which is only valid for small displacements. We also address the problem of volume variations by adding to our model incompressibility constraints. Finally, we demonstrate the relevance of this approach for the real-time simulation of laparoscopic surgical gestures on the liver.

Computer Simulation↗
PubMed2002

Computation of the mid-sagittal plane in 3-D brain images.

We present a new method to automatically compute, reorient, and recenter the mid-sagittal plane in anatomical and functional three-dimensional (3-D) brain images. This iterative approach is composed of two steps. At first, given an initial guess of the mid-sagittal plane (generally, the central plane of the image grid), the computation of local similarity measures between the two sides of the head allows to identify homologous anatomical structures or functional areas, by way of a block matching procedure. The output is a set of point-to-point correspondences: the centers of homologous blocks. Subsequently, we define the mid-sagittal plane as the one best superposing the points on one side and their counterparts on the other side by reflective symmetry. Practically, the computation of the parameters characterizing the plane is performed by a least trimmed squares estimation. Then, the estimated plane is aligned with the center of the image grid, and the whole process is iterated until convergence. The robust estimation technique we use allows normal or abnormal asymmetrical structures or areas to be treated as outliers, and the plane to be mainly computed from the underlying gross symmetry of the brain. The algorithm is fast and accurate, even for strongly tilted heads, and even in presence of high acquisition noise and bias field, as shown on a large set of synthetic data. The algorithm has also been visually evaluated on a large set of real magnetic resonance (MR) images. We present a few results on isotropic as well as anisotropic anatomical (MR and computed tomography) and functional (single photon emission computed tomography and positron emission tomography) real images, for normal and pathological subjects.

Algorithms↗