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Atlas ultra-haute résolution des noyaux gris centraux et des Thalami à partir d’un template mp2rage à 7t et base normative des temps de relaxation t1
Abstract
Introduction
Apprécier la réalité histo-architecturale in vivo des noyaux gris centraux notamment des thalami est un véritable challenge en imagerie. La définition précise de cette anatomie est cependant un prérequis nécessaire pour mesurer avec précision des altérations structurales parenchymateuses ou réaliser le ciblage stéréotaxique en neurochirurgie [1].
Matériel et méthodes
Des images tridimensionnelles MP2RAGE (Magnetization Prepared with two Rapid Acquisition Gradient Echoes sequence) [2] ont été acquises en 0,6 mm isotropique à 7 T chez 60 volontaires sains. Une méthode de normalisation « group-wize » [3] a permis de créer un template T1 haute résolution (0,5 mm³) à partir de 30 des 60 sujets (Fig. 1). Deux neuroradiologues (R1 et R2) ont réalisé une segmentation de 24 régions de substance gris profonde et de douze noyaux thalamiques dans chaque hémisphère, selon l’atlas de Morel [4]. Un atlas moyenné (7TAMIbrainDGN) à partir des deux segmentations a été validé par un troisième neuroradiologue (Fig. 2). Les structures correspondantes ont également été extraites à partir de deux autres atlas numériques (e-Morel et CIT168). Les valeurs quantitatives moyennes absolues des temps de relaxation T1 ont été ensuite extraites après correction du B1+. Un score de Dice a été calculé pour mesurer la concordance inter-observateur et la variabilité inter-atlas.
Résultats
Les scores de Dice entre les deux neuroradiologues, entre le R1 et 7TAMIbrainDGN et entre R2 et 7TAMIbrainDGN étaient respectivement de 0,74 ± 0,13, 0,87 ± 0,07, 0,86 ± 0,08. Les Dice entre 7TAMIbrainDGN et l’atlas e-Morel étaient de 0,53 ± 0,16 et de 0,80 ± 0,07 entre 7TAMIbrainDGN et CIT168. Les temps de relaxation T1 variaient de 1322 ms à 2003 ms.
Conclusion
Cette étude a permis de créer un atlas des noyaux gris centraux et des thalamis à 7 T et de valider un processus de segmentation automatisé avec extraction des temps de relaxation T1.
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The large spatial inhomogeneity in transmit B(1) field (B(1)(+)) observable in human MR images at high static magnetic fields (B(0)) severely impairs image quality. To overcome this effect in brain T(1)-weighted images, the MPRAGE sequence was modified to generate two different images at different inversion times, MP2RAGE. By combining the two images in a novel fashion, it was possible to create T(1)-weighted images where the result image was free of proton density contrast, T(2) contrast, reception bias field, and, to first order, transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B(1)(+) variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T(1)-weighted images, acquired within 12 min, high-resolution 3D T(1) maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T(1) maps were validated in phantom experiments. In humans, the T(1) values obtained at 7 T were 1.15+/-0.06 s for white matter (WM) and 1.92+/-0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min, the T(1) values obtained (0.81+/-0.03 s for WM and 1.35+/-0.05 for GM) were once again found to be in very good agreement with values in the literature.
Atlas-based segmentation techniques are often employed to encode anatomical information for the delineation of multiple structures in magnetic resonance images of the brain. One of the primary challenges of these approaches is to efficiently model qualitative and quantitative anatomical knowledge without introducing a strong bias toward certain anatomical preferences when segmenting new images. This paper explores the use of topological information as a prior and proposes a segmentation framework based on both topological and statistical atlases of brain anatomy. Topology can be used to describe continuity of structures, as well as the relationships between structures, and is often a critical component in cortical surface reconstruction and deformation-based morphometry. Our method guarantees strict topological equivalence between the segmented image and the atlas, and relies only weakly on a statistical atlas of shape. Tissue classification and fast marching methods are used to provide a powerful and flexible framework to handle multiple image contrasts, high levels of noise, gain field inhomogeneities, and variable anatomies. The segmentation algorithm has been validated on simulated and real brain image data and made freely available to researchers. Our experiments demonstrate the accuracy and robustness of the method and the limited influence of the statistical atlas.
One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.
Multiarchitectonic and stereotactic atlas of the human thalamus
- Morel