Nicholas Ayache

Tracking and motion analysis of the left ventricle with deformable superquadrics.

We present a new approach to analyse the deformation of the left ventricle of the heart based on a parametric model that gives a compact representation of a set of points in a 3-D image. We present a strategy for tracking surfaces in a sequence of 3-D cardiac images. Following tracking, we then infer quantitative parameters which characterize: left ventricle motion, volume of left ventricle, ejection fraction, amplitude and twist component of cardiac motion. We explain the computation of these parameters using our model. Experimental results are shown in time sequences of two modalities of medical images, nuclear medicine and X-ray computed tomography (CT). Video sequences presenting these results are on the CD-ROM.

Matching 3D MR angiography data and 2D X-ray angiograms

In this paper we present a new algorithm for matching a 3D MR angiography volume image with two 2D X-ray angiograms without using artificial markers. The goal is to prove the feasibility of such a technique, and the long-term aim is to be able to report the catheter position in a 3D pre-operative image, during an X-ray angiographic examination. First, vessels centerlines are computed using a novel algorithm. Then matching is performed using an extension of the ICP algorithm. Special care is taken in dealing with outliers. Comparing the results on real data of our algorithm with a stereotactic frame-based registration, the accuracy of our method is proved to be better than 2 mm in the worse case with good repeatability. This makes it usable for clinical applications.

Application of an Automatically Built 3D Morphometric Brain Atlas: Study of Cerebral Ventricle Shape

In this paper we present new results on the automatic building of a 3D morphometric brain atlas from volumetric MRI images and its application to the study of the shape of cerebral structures. In particular, we show how it is possible to define “abnormal” deformations of the cerebral ventricles with a small set of parameters.

A spatio-temporal model-based statistical approach to detect evolving multiple sclerosis lesions

The effects of new treatments need to be assessed: in the case of multiple sclerosis it is possible to measure those effects by studying the evolutions of temporal lesions in time series of MRIs. But it is a laborious task to manually analyze such sets of images. This article proposes a new method to statistically analyze a series of T2-weighted MRIs of a patient with multiple sclerosis lesions taking both temporal and spatial coherence into account. The main idea of the method is to fit a temporal parametric model of intensity evolution on each voxel of the series; these estimations give different parameter values in the case of normal and pathological areas. A statistical inference stage makes it possible to determine significant sets of connected voxels corresponding to pathological evolving areas. The significancy is estimated using permutation tests. Promising results show the feasibility of our approach. On our data sets the evolving lesions were detected and their temporal behavior could be quantified.

A New Kinetic Modeling Scheme for the Human Left Ventricle Wall Motion with MR-Tagging Imaging

Magnetic resonance tagging has proved to be an efficient non-invasive imaging technique for the study of the heart motion, producing a sharp and dense pattern on the tissues. Up to now, the quality of information derived from it has been unequalled by other modalities. It seems to be the perfect modality for the building of heart motion models. However, its main drawback was the tediousness of the image processing tasks it requires to extract the information. Recent achievements [1,9] have overcome that. In previous work [2], we have presented a novel parametric class of deformation for the 2D heart motion in the short axis planes. From it, we can compute a very accurate and compact analytical expression of the walls motion from the grid information. We present here this kinetic model, and its applications to heart modeling. In particular, we build a decomposition basis of the 2D motion with only three orthogonal modes.