367 research outputs found
The Connectome Viewer Toolkit: an open source framework to manage, analyze and visualize connectomes
No full textAbstract Advanced neuroinformatics tools are required for methods of connectome mapping, analysis and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration and sharing. We have designed and implemented the Connectome Viewer Toolkit --- a set of free and extensible open-source neuroimaging tools written in Python. The key components of the toolkit are as follows: 1. The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. 2. The Connectome File Format Library enables management and sharing of connectome files. 3. The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org/
Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology
No full textPurpose To exploit the improved comparability and hardware independency of quantitative MRI, databases of MR physical parameters in healthy tissue are required, to which tissue properties of patients can be compared. In this work, normative values for longitudinal and transverse relaxation times in the brain were established and tested in single-subject comparisons for detection of abnormal relaxation times. Methods Relaxometry maps of the brain were acquired from 52 healthy volunteers. After spatially normalizing the volumes into a common space, T-1 and T-2 inter-subject variability within the healthy cohort was modeled voxel-wise. A method for a single-subject comparison against the atlases was developed by computing z-scores with respect to the established healthy norms. The comparison was applied to two multiple sclerosis and one clinically isolated syndrome cases for a proof of concept. Results The established atlases exhibit a low variation in white matter structures (median RMSE of models equal to 32 ms for T-1 and 4 ms for T-2), indicating that relaxation times are in a narrow range for normal tissues. The proposed method for single-subject comparison detected relaxation time deviations from healthy norms in the example patient data sets. Relaxation times were found to be increased in brain lesions (mean z-scores >5). Moreover, subtle and confluent differences (z-scores similar to 2-4) were observed in clinically plausible regions (between lesions, corpus callosum). Conclusions Brain T-1 and T-2 quantitative norms were derived voxel-wise with low variability in healthy tissue. Example patient deviation maps demonstrated good sensitivity of the atlases for detecting relaxation time alterations.LTS
L'échographie Doppler couleur dans l'investigation des rejets de greffes rénales : confrontation radio-anatomopathologique
No full textVisuo-vestibular mechanisms of bodily self-consciousness
No full textBodily self-consciousness is linked to multisensory integration and is particularly dependent on vestibular perception providing the brain with the main sensory cues about body motion and location in space. Vestibular and visual inputs are permanently balanced and integrated to encode the most optimal representation of the external world and of the observer in the central nervous system. Vection, an illusory self-motion experience induced only by visual stimuli, illustrates the fact that the visual and the vestibular systems share common neural underpinnings and a similar phenomenology. Optokinetic stimulation inducing vection and direct vestibular stimulation induce whole-body motion sensations that can be used to explore multisensory interactions. A failure in visuo-vestibular integration, artificially induced by the methods of cognitive psychology or in pathological conditions, has also been reported to altered own body perception and bodily self-consciousness. The respective contributions of the vestibular and visual systems to bodily self-consciousness amongst other polymodal sensory mechanisms, and the neural correlates of visuo-vestibular convergence, should be better understood. We first performed a neuroimaging study of brain regions where optokinetic and vestibular stimuli converge, using 7T functional magnetic resonance imaging in individual subjects. We identified three main regions of convergence: (1) the depth of supramarginal gyrus or retroinsular cortex, (2) the surface of supramarginal gyrus at the temporo-parietal junction, (3) and the posterior part of middle temporal gyrus and superior temporal sulcus. Then, we aimed to induce the embodiment of an external fake rubber hand through visuo-tactile conflict - the so-called rubber hand illusion paradigm, and studied how this integration is modulated by vection. Subjects experiencing vection in the direction of the rubber hand mislocalised the position of their real hand towards the rubber hand indicating that visuo-vestibular stimuli can enhance visuo-tactile integration. We also investigated if visuo-proprioceptive and tactile integration in peripersonal space could be dynamically updated based on the congruency of visual and proprioceptive feedback. A pair of rubber hands or feet provided visual feedback. Fake and real limbs were crossed or uncrossed. We showed that sensory cues were integrated in peripersonal space, dynamically reshaped but only for hands. Finally, we investigated a rare case of an illusory own body perception in an epileptic patient suffering from multiple daily disembodiments during seizures. Seizures were associated to a focal cortical microdysplasia juxtaposed to a developmental venous anomaly in the left angular gyrus, a brain region known to be important for visuo-vestibular integration and bodily self-consciousness. Our results characterize the inferior parietal lobule as a crucial structure in merging visual, vestibular, tactile and proprioceptive inputs, allowing the emergence of the global and unified experience of being â I.â Multisensory body representation can be reshaped transiently using visual and vestibular signals or in relation to a medical condition affecting the temporo-parietal junction. The integration of visual and vestibular signals, aims to adapt dynamically our internal representations to constant changes occurring in our environment.LNC
Segmentation automatique de la matière grise et de la matière blanche par un algorithme génétique : une étude de validation
No full textRendement diagnostic des ponctions-biopsies disco-vertébrales et impact dans le traitement du patient suspect de spondylodiscite. (Spondylodiscite, biopsies)
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The Connectome Mapper: an open-source processing pipeline to map connectomes with MRI
No full textResearchers working in the field of global connectivity analysis using diffusion magnetic resonance imaging (MRI) can count on a wide selection of software packages for processing their data, with methods ranging from the reconstruction of the local intra-voxel axonal structure to the estimation of the trajectories of the underlying fibre tracts. However, each package is generally task-specific and uses its own conventions and file formats. In this article we present the Connectome Mapper, a software pipeline aimed at helping researchers through the tedious process of organising, processing and analysing diffusion MRI data to perform global brain connectivity analyses. Our pipeline is written in Python and is freely available as open-source at www.cmtk.org
Novel Image Processing Methods for Improved Fetal Brain MRI
No full textFetal magnetic resonance imaging (MRI) has been increasingly used as a powerful complement imaging modality to ultrasound imaging (US) for the clinical evaluation of prenatal abnormalities. Specifically, clinical application of fetal MRI has been significantly improved in the nineties by hardware and software advances with the development of ultrafast multi-slice T2-weighted (T2w) acquisition sequences able to freeze the unpredictable fetal motion and provide excellent soft-tissue contrast. Fetal motion is indeed the major challenge in fetal MRI and slice acquisition time should be kept as short as possible. As a result, typical fetal MRI examination involves the acquisition of a set of orthogonally planned scans of thick two-dimensional slices, largely free of intra-slice motion artifacts. The poor resolution in the slice-select dimension as well as possible motion occurring between slices limits further quantitative data analysis, which is the key for a better understanding of the developing brain but also the key for the determination of operator-independent biomarkers that might significantly facilitate fetal diagnosis and prognosis.
To this end, several research groups have developed in the past ten years advanced image processing methods, often denoted by motion-robust super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) scans, a high-resolution (HR) motion-free volume. SR problem is usually modeled as a linear inverse problem describing the imaging degradation due to acquisition and fetal motion. Typically, such approaches consist in iterating between slice motion estimation that estimates the motion parameters and SR that recovers the HR image given the estimated degradation model. This thesis focuses on the development of novel advanced image processing methods, which have enabled the design of a completely automated reconstruction pipeline for fetal MRI. The proposed techniques help in improving state-of-the-art fetal MRI reconstruction in terms of efficiency, robustness and minimized user-interactions, with the ultimate goal of being translated to the clinical environment.
The first part focuses on the development of a more efficient Total Variation (TV)-regularized optimization algorithm for the SR problem. The algorithm uses recent advances in convex optimization with a novel adaptive regularization strategy to offer simultaneously fast, accurate and robust solutions to the fetal image recovery problem. Extensive validations on both simulated fetal and real clinical data show the proposed algorithm is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.
The second part focuses on the development of a novel automatic brain localization and extraction approach based on template-to-slice block matching and deformable slice-totemplate registration. Asmost fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction, the fetal brain needs to be localized and extracted as a first step. These tasks generally necessitate user interaction, manually or semi-automatically done. Our methods have enabled the design of completely automated reconstruction pipeline that involves intensity normalization, inter-slice motion estimation, and super-resolution.
Quantitative evaluation on clinical MRI scans shows that our approach produces brain masks that are very close to manually drawn brain masks, and ratings performed by two expert observers show that the proposed pipeline achieves similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction without any further effort. The third part investigates the possibility of automatic cortical folding quantification, one of the best biomarkers of brain maturation, by combining our automatic reconstruction pipeline with a state-of-the-art fetal brain tissue segmentation method and existing automated tools provided for adult brain’s cortical folding quantification. Results indicate that our reconstruction pipeline can provide HR MR images with sufficient quality that enable the use of surface tessellation and active surface algorithms similar to those developed for adults to extract meaningful information about fetal brain maturation.
Finally, the last part presents new methodological improvements of the reconstruction pipeline aiming at improving the quality of the image for quantitative data analysis, whose accuracy is highly dependent on the quality and resolution of the reconstructed image. In particular, it presents a more consistent and global magnetic bias field correction method which takes advantage of the super-resolution framework to provide a final reconstructed image quasi free of the smooth bias field. Then, it presents a new TV SR algorithm that uses the Huber norm in the data fidelity term to be more robust to non-Gaussian outliers. It also presents the design of a novel joint reconstruction-segmentation framework and the development of a novel TV SR algorithm driven by segmentation to produce images with enhanced edge information that could ultimately improve their segmentation. Finally, it preliminary investigates the capability of increasing the resolution in the in-plane dimensions using SR to ultimately reduce the partial volume effect
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