Fabrice Poupon

Altered structural connectivity of cortico-striato-pallido-thalamic networks in Gilles de la Tourette syndrome

See Jackson (doi:10.1093/brain/awu338) for a scientific commentary on this article. The neural substrate of Gilles de la Tourette syndrome is unknown. Worbe et al. use probabilistic tractography to demonstrate widespread structural abnormalities in cortico-striato-pallido-thalamic white matter pathways—likely arising from abnormal brain development—in patients with this syndrome.

Multi-object Deformable Templates Dedicated to the Segmentation of Brain Deep Structures

We propose a new way of embedding shape distributions in a topological deformable template. These distributions rely on global shape descriptors corresponding to the 3D moment invariants. In opposition to usual Fourier-like descriptors, they can be updated during deformations at a relatively low cost. The moment-based distributions are included in a framework allowing the management of several simultaneously deforming objects. This framework is dedicated to the segmentation of brain deep nuclei in 3D MR images. The paper focuses on the learning of the shape distributions, on the initialization of the topological model and on the multi-resolution energy minimization process. Results are presented showing the segmentation of twelve brain deep structures.

Spherical wavelet transform for ODF sharpening

The choice of local HARDI reconstruction technique is crucial for discerning multiple fiber orientations, which is itself of substantial importance for tractography, and reliable and accurate assessment of white matter fiber geometry. Due to the complexity of the diffusion process and its milieu, distinct diffusion compartments can have different frequency signatures, making the HARDI signal spread over multiple frequency bands. Therefore, we put forth the idea of multiscale analysis with localized basis functions, ensuring that different frequency ranges are probed. With the aim of truthful recovery of fiber orientations, we reconstruct the orientation distribution function (ODF), by incorporating a spherical wavelet transform (SWT) into the Funk-Radon transform. First, we apply and validate our proposed SWT method on real physical phantoms emulating fiber bundle crossings. Then, we apply the SWT method to a real brain data set. The analysis of the real data set suggests that different angular frequencies may capture different information, thus stressing the importance of multiscale analysis. For both phantom and real data, we compare the SWT reconstruction with state-of-the-art q-ball imaging and spherical deconvolution reconstruction methods. We demonstrate the algorithm efficiency in diffusion ODF denoising and sharpening that is of particular importance for applications to fiber tracking (especially for probabilistic approaches), and brain connectome mapping. Also, the algorithm results in considerable data compression that could prove beneficial in applications to fiber bundle segmentation, and for HARDI based white matter morphometry methods.

3D boundary extraction of the left ventricle by a deformable model with a priori information

In medical imaging, 3D boundary extraction is a preliminary requisite for a coherent shape analysis of an organ. Deformable objects, like the heart cavities, are often hard to detect because of the artefacts caused by the motion. The authors present a 3D deformable surface model based on a parameterized representation combined with a random process of deformation. The solution is searched for by the minimization of an energy function through simulated annealing. The authors also discuss the introduction of a priori shape information about the object. The boundary extraction algorithm is applied to 3D CT data of a dogs heart.

Multi-contrast deep nuclei segmentation using a probabilistic atlas

In this paper we propose a new hybrid segmentation approach of the deep brain structures based on a multi-contrast deformable model of regions in competition, with deformations preserving the topology of the structures, as well as their shape and position, using a probabilistic atlas and some prior morphological information. The accuracy of our method was evaluated by comparing the results obtained on a base of T1-weighted data contrast with those of FREESURFER and FSL-FIRST. Besides giving very good results from only one contrast, we show that the multi-contrast aspect of our method allows exploiting the complementary contributions of different contrasts, like T1 and diffusion tensor (DT) contrasts, in order to provide a more robust segmentation.

Analysis of the striato-thalamo-cortical connectivity on the cortical surface to infer biomarkers of huntington's disease

The deep brain nuclei play an important role in many brain functions and particularly motor control. Damage to these structures result in movement disorders such as in Parkinsons disease or Huntingtons disease, or behavioural disorders such as Tourette syndrome. In this paper, we propose to study the connectivity profile of the deep nuclei to the motor, associative or limbic areas and we introduce a novel tool to build a probabilistic atlas of these connections to the cortex directly on the surface of the cortical mantel, as it corresponds to the space of functional interest. The tool is then applied on two populations of healthy volunteers and patients suffering from severe Huntingtons disease to produce two surface atlases of the connectivity of the basal ganglia to the cortical areas. Finally, robust statistics are used to characterize the differences of that connectivity between the two populations, providing new connectivity-based biomarkers of the pathology.

Defining cortical sulcus patterns using partial clustering based on bootstrap and bagging

The cortical folding patterns are very different from one individual to another. Here we try to find folding patterns automatically using large-scale datasets by non-supervised clustering analysis. The sulci of each brain are detected and identified using the brain VIS A open software. The 3D moment invariants are calculated and used as the shape descriptors of the sulci identified. A partial clustering algorithm using bootstrap sampling and bagging (PCBB) is devised for cortical pattern mining. Partial clusters are found using a modified hierarchical clustering method constrained by an objective function which looks for the most compact and dissimilar clusters. Bagging is used to increase stability. Experiments on simulated and real datasets are used to demonstrate the strength and stability of this algorithm compared to other standard approaches. Some cortical patterns are found using our method. In particular, the patterns found for the left cingulate sulcus are consistent with the patterns described in the atlas of Ono.

Mean q-Ball Strings Obtained by Constrained Procrustes Analysis with Point Sliding

The idea underpinning the work we present herein is to design robust and objective tools for brain white matter (WM) morphometry. We focus on WM tracts, and propose to represent them by their mean lines, to which we associate the attributes derived from high-angular resolution diffusion imaging (HARDI). The definition of the tract mean line derives directly from the geometry of the tract fibres. We determine the fibre point correspondences and impact factors of individual fibres, upon which we estimate average HARDI models along the tract mean lines. This way we obtain a compact tract representation that exploits all the available information, and is at the same time free of the outlier influence and undesired tract edge effects.

Real-time MR diffusion tensor and Q-ball imaging using Kalman filtering

Magnetic resonance diffusion imaging (dMRI) has become an established research tool for the investigation of tissue structure and orientation. In this paper, we present a method for real time processing of diffusion tensor and Q-ball imaging. The basic idea is to use Kalman filtering framework to fit either the linear tensor or Q-ball model. Because the Kalman filter is designed to be an incremental algorithm, it naturally enables updating the model estimate after the acquisition of any new diffusion-weighted volume. Processing diffusion models and maps during ongoing scans provides a new useful tool for clinicians, especially when it is not possible to predict how long a subject may remain still in the magnet.

Improving the Realism of White Matter Numerical Phantoms: A Step toward a Better Understanding of the Influence of Structural Disorders in Diffusion MRI

White matter is composed of irregularly packed axons leading to a structural disorder in the extra-axonal space. Diffusion MRI experiments using oscillating gradient spin echo sequences have shown that the diffusivity transverse to axons in this extra-axonal space is dependent on the frequency of the employed sequence. In this study, we observe the same frequency-dependence using 3D simulations of the diffusion process in disordered media. We design a novel white matter numerical phantom generation algorithm which constructs biomimicking geometric configurations with few design parameters, and enables to control the level of disorder of the generated phantoms. The influence of various geometrical parameters present in white matter, such as global angular dispersion, tortuosity, presence of Ranvier nodes, beading, on the extra-cellular perpendicular diffusivity frequency dependence was investigated by simulating the diffusion process in numerical phantoms of increasing complexity and fitting the resulting simulated diffusion MR signal attenuation with an adequate analytical model designed for trapezoidal OGSE sequences. This work suggests that angular dispersion and especially beading have non-negligible effects on this extracellular diffusion metrics that may be measured using standard OGSE DW-MRI clinical protocols.