3D automated quantification of asymmetries on fossil endocasts

International audienceOver the last 15 years computed tomography (CT) has become a common way to obtain high resolution three-dimensional images of cranial endocast of hominids. Among the different features that can be seen on such endocasts, of key interest are their shape asymmetries. In particular, protrusions of the frontal and occipital lobes, as well as differences in their width, have been typically observed in modern humans' brains. These have been often hypothesized to be linked to functional specialization, and especially language and handedness. The imprints of these protrusions on the inner surface of the skull are called the petalia. There is a lack of automated, reproducible and objective methods to quantify these protrusions and to assess (for instance) whether they are present in species other than Homo sapiens. We propose a new method for the automated quantification of 3D endocranial shape asymmetries. We mathematically define the symmetry plane of the endocast as the 3D plane which best superposes the "right" and "left" sides of the endocranial surface. Then, we compute a 3D pointwise deformation field between the two sides of the endocast, allowing to match homologous points, and to assess their relative spatial position. The analysis of this 3D deformation field allows quantifying the shape asymmetries everywhere on the endocast. We illustrate our method on the endocast of Sts 5 (Mrs. Ples, Australopithecus africanus) whose very high resolution CT scan has been segmented using ITK-SNAP. The results suggest an opposite shape asymmetry in the fronto-temporal and occipital regions

3D Wavelet Subbands Mixing for Image Denoising

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. The method proposed in this paper is a fully automatic 3D blockwise version of the nonlocal (NL) means filter with wavelet subbands mixing. The proposed wavelet subbands mixing is based on a multiresolution approach for improving the quality of image denoising filter. Quantitative validation was carried out on synthetic datasets generated with the BrainWeb simulator. The results show that our NL-means filter with wavelet subbands mixing outperforms the classical implementation of the NL-means filter in terms of denoising quality and computation time. Comparison with wellestablished methods, such as nonlinear diffusion filter and total variation minimization, shows that the proposed NL-means filter produces better denoising results. Finally, qualitative results on real data are presented

Computation of the Mid-Sagittal Plane in 3D Brain Images

International audienceWe 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

Computation of the Mid-Sagittal Plane in 3D Medical Images of the Head

We present a new symmetry-based method allowing to compute, reorient and recenter the mid-sagittal plane in anatomical and functional 3D images of the brain. In the literature, there are mainly two definitions of this plane: it is either the plane best fitting the inter-hemispheric fissure of the brain, or the plane best superposing the two sides of the head by reflective symmetry. We use this latter definition in our method, which is composed of two steps. At first, the computation of local similarity measures between the two sides of the brain allows to match 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 superposing at best the points in one side of the head and their counterparts in the other side by reflective symmetry. The estimatio- n of the parameters characterizing the plane is performed by a least trimmed squares optimization scheme. Then, the estimated plane is aligned with the center of the image lattice. This method is fully automated, objective and reproducible. Our method tackles the main issue posed by the sym­me­try-ba- sed approach, that often relies on global similarity measures (such as the cross-correlation) between the intensities of the two flipped versions of the 3D image. The estimation of the mid-sagittal plane can be severely biased when normal or abnormal asymmetries hide the underlying symmetry of the brain or the skull. The computation of local measures of symmetry and the use of a robust estimation technique allow to discriminate between symmetrical and asymmetrical areas. Then, the mid-sagittal plane is mainly computed from the underlying gross symmetry of the brain, because its estimation is robust with respect to normal or abnormal asymmetries which are treated as outliers. We show on a large database of synthetic images that we can obtain a subvoxel accuracy in a CPU time of about 3 minutes, for strongly tilted heads, noisy and biased images. We present results on isotropic or anisotropic anatomical (MR, CT), and functional (SPECT and PET) images

Statistical Analysis of Dissymmetry in Volumetric Medical Images

We present a general method to study the dissymmetry of anatomical structures such as the human brain. Our method relies on the estimate of 3D dissymmetry fields, the use of 3D vector field operators, and $T^2$ statistics to compute significance maps. We also present a fully automated implementation of this method which relies mainly on the intensive use of a 3D non-rigid inter-patient matching tool. Such a tool is applied successively between the images and their symmetric versions with respect to an arbitrary plane, both to realign the images with respect to the mid-plane of the subject and to compute a dense 3D dissymmetry map. Inter-patient matching is also used to fuse the data of a population of subjects. We then describe three main application fields: the study of the normal dissymmetry within a given population, the comparison of the dissymmetry between two populations, and the detection of the significant abnormal dissymmetries of a patient with respect to a reference population. Finally, we present preliminary results illustrating these three applications for the case of the human brain

CLARCS, a C++ Library for Automated Registration and Comparison of Surfaces: Medical Applications

International audienceIn this paper we present the methods implemented in the CLARCS (C++ Library for Automated Registration and Comparison of Surfaces) library. This library allows some basic and high level processing on free-form surfaces, represented as point sets or meshes. Three methods are the "building bricks" of CLARCS; they allow (i) the rigid/affine/non-linear registration of two point sets, (ii) the computation of the mid-sagittal plane of one point set, (iii) the computation of a mean point set from several point sets, and the variability around this mean. These methods are all based on a common methodological framework, in which the point sets/meshes are represented either as a Gaussian mixture model or as a draw of such a model. We propose some applications of the methods implemented in CLARCS on different sets of medical data

Anatomie du tractus cortico-spinal en tractographie : évaluation d'une méthode déterministe

Introduction : Si la substance grise a été largement étudiée en IRM fonctionnelle (IRMf), l'étude in vivo des tractus de substance blanche est plus récente. L'IRM en tenseur de diffusion permet désormais d'étudier son anatomie grâce à la tractographie. Notre objectif était l'étude du tractus cortico-spinal (TCS) en tenseur de diffusion et en tractographie chez des sujets sains. Matériel et méthodes : La population concernait 15 volontaires sains droitiers. Une IRM 3T anatomique T1 a permis la détermination des régions d'intérêts (ROI) au niveau du mésencéphale. L'IRMf a été analysée par le logiciel SPM5 afin d'obtenir une carte d'activation représentant l'activation motrice de la main au niveau du cortex moteur. L'IRM de diffusion a servi à reconstruire un tenseur (matrice 3x3) en chaque voxel de l'image. Après recalage des 3 séquences, nous avons effectué une tractographie du TCS par une méthode déterministe utilisant l'algorithme (Mori et al). Les tractographies ont été réalisées entre les deux ROI de chaque côté. Résultat : Cette méthode donne une représentation anatomique du TSC méconnaissent la partie ventro-latérale de la ROI fonctionnelle. Cette partie correspond aux croisements de fibres des autres faisceaux de fibres blanches traversant la région. Conclusion : La limite principale du tenseur se situe au niveau des croisements des fibres, car il ne représente correctement qu'une seule direction de diffusion. Cela ne permet pas actuellement de retrouver l'anatomie des faisceaux de fibres telle que nous la connaissons pas les dissections. Les méthodes déterministes mono-directionnelles ne sont pas suffisantes notamment dans le contexte de la chirurgie guidée par l'image. Elles doivent être enrichies de méthodes multidirectionnelles en utilisant des algorithmes plus complexes

The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery: The DTI Challenge on Tractography for Neurosurgery

Diffusion tensor imaging tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop

étude de la symétrie bilatérale en imagerie cérébrale volumique

no english abstractLe cerveau humain est une structure anatomique à symétrie bilatérale : il existe un plan, appelé plan médian sagittal, par rapport auquel il est (approximativement) symétrique. Certaines structures ou aires cérébrales sont pourtant systématiquement asymétriques. L'étude de ces asymétries et de leurs anomalies est d'un intérêt majeur pour la compréhension de certaines pathologies comme la schizophrénie. Dans cette thèse, nous présentons une méthode permettant de quantifier ces déviations locales par rapport à une symétrie bilatérale parfaite et d'en effectuer une analyse statistique dans des populations de sujets. En raison du positionnement arbitraire de la tête dans l'appareil d'acquisition, le plan médian sagittal est rarement situé au centre des images médicales tridimensionnelles anatomiques (IRM, scanner) ou fonctionnelles (TESP, TEP). Nous proposons une définition objective de ce plan, fondée sur un critère mathématique robuste de type moindres carrés tamisés. Ensuite, après calcul et réalignement du plan médian sagittal, nous montrons comment obtenir en chaque point de l'image un vecteur caractéristique de l'asymétrie de la structure anatomique sous-jacente. Ce champ d'asymétrie est obtenu au moyen d'un outil de recalage non-rigide, qui est également utilisé pour fusionner dans un référentiel géométrique commun les champs calculés sur une population d'individus. Des techniques statistiques classiques (de type test de Hotteling) permettent alors d'étudier l'asymétrie d'une population ou de comparer l'asymétrie entre deux populations. Un problème spécifique aux IRM est celui des variations lentes des intensités de l'image, induites par les interactions du sujet avec le champ magnétique, et qui ne reflétent pas les propriétés physiques des tissus sous-jacents. La structure géométrique de ce champ de biais est elle-même asymétrique, et perturbe substantiellement le calcul de l'asymétrie anatomique. Nous proposons différents algorithmes pour corriger ce biais, fondés sur des modélisations mathématiques du processus d'acquisition de l'image

A new efficient EM-ICP algorithm for non-linear registration of 3D point sets

In this paper, we present a new method for non-linear pairwise registration of point sets. In this method, we consider the points of the first set as the draws of a Gaussian mixture model whose centres are the points of the second set displaced by a deformation. Next we perform {\it maximum a posteriori} estimation of the parameters (which include the unknown transformation) of this model using the expectation-maximisation algorithm. Compared to other methods using the same ''EM-ICP'' paradigm/framework, we propose three key modifications leading to an efficient algorithm allowing for fast registration of large point sets: 1) symmetrisation of the point-to-point correspondences; 2) specification of priors on these correspondences using differential geometry; 3) efficient encoding of deformations using the RKHS theory and the Fourier analysis. The resulting algorithm is efficient and is able to register large data sets. We evaluate the added value of the modifications and compare our method to the state-of-the-art CPD algorithm on synthetic data.Dans cet article, nous présentons une nouvelle méthode pour le recalage non-linéaire de deux nuages de points. Dans cette méthode, nous considérons les points du premier nuage comme la réalisation d'un mélange de gaussiennes dont les centres sont les points du second ensemble déplacés par une déformation. Ensuite, nous estimons cette déformation, sur laquelle nous fixons un a priori, selon le principe du maximum a posteriori en utilisant l'algorithme "expectation-maximisation". Par rapport aux autres méthodes qui utilisent un paradigme similaire, nous proposons de: 1) symétriser le processus de correspondance entre les points des deux nuages, 2) spécifier des a priori sur les correspondances en utilisant des outils de la géométrie différentielle et 3) caractériser la déformation à estimer en utilisant la théorie des espaces de Hilbert à noyaux reproduisants et l'analyse de Fourier. L'algorithme résultant est relativement efficace et permet de recaler des nuages de points de grandes tailles. Enfin, nous évaluons l'impact de ces modifications puis nous comparons notre méthode à une méthode de l'état de l'art