Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom.

International audienceAs it provides the only method for mapping white matter fibers in vivo, diffusion MRI tractography is gaining importance in clinical and neuroscience research. However, despite the increasing availability of different diffusion models and tractography algorithms, it remains unclear how to select the optimal fiber reconstruction method, given certain imaging parameters. Consequently, it is of utmost importance to have a quantitative comparison of these models and algorithms and a deeper understanding of the corresponding strengths and weaknesses. In this work, we use a common dataset with known ground truth and a reproducible methodology to quantitatively evaluate the performance of various diffusion models and tractography algorithms. To examine a wide range of methods, the dataset, but not the ground truth, was released to the public for evaluation in a contest, the "Fiber Cup". 10 fiber reconstruction methods were evaluated. The results provide evidence that: 1. For high SNR datasets, diffusion models such as (fiber) orientation distribution functions correctly model the underlying fiber distribution and can be used in conjunction with streamline tractography, and 2. For medium or low SNR datasets, a prior on the spatial smoothness of either the diffusion model or the fibers is recommended for correct modelling of the fiber distribution and proper tractography results. The phantom dataset, the ground truth fibers, the evaluation methodology and the results obtained so far will remain publicly available on: http://www.lnao.fr/spip.php?rubrique79 to serve as a comparison basis for existing or new tractography methods. New results can be submitted to [email protected] and updates will be published on the webpage

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018

Processing of diffusion MR images of the brain: from crossing fibres to distributed tractography

Diffusion-weighted (DW) magnetic resonance imaging allows the quantification of water diffusion within tissue. Due to the hindrance of water molecules by the various tissue compartments, probing for the diffusive properties of a region can provide information on the underlying structure. This is particularly useful for the human brain, whose anatomy is complex. Diffusion imaging provides currently the only tool to study the brain connectivity and organization non-invasively and in-vivo, through a group of methods, commonly referred to as tractography methods. This thesis is concerned with brain anatomical connectivity and tractography. The goal is to elucidate problems with existing approaches used to process DW images and propose solutions and methods through new frameworks. These concern data from two popular DW imaging protocols, diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI), or Q-ball imaging in particular. One of the problems tackled is resolving crossing fibre configurations, a major concern in DW imaging, using data that can be routinely acquired in a clinical setting. The physical constraint of spatial continuity of the diffusion environment is imposed throughout the brain volume, using a multi-tensor model and a regularization method. The new approach is shown to improve tractography results through crossing regions. Quantitative tractography algorithms are also proposed that, apart from reconstructing the white matter tracts, assign relative indices of anatomical connectivity to all regions. A fuzzy algorithm is presented for assessing orientational coherence of neuronal tracts, reflecting the fuzzy nature of medical images. As shown for different tracts, where a-priori anatomical knowledge exists, regions that are coherently connected and possibly belong to the same tract can be differentiated from the background. In a different framework, elements of graph theory are used to develop a new tractography algorithm that can utilize information from multiple image modalities to assess brain connectivity. Both algorithms inherently consider crossing fibre information and are shown to solve problems that affect existing methods

Tractographie par IRM de diffusion : algorithmes, validation, reproductibilité et applications

La tractographie gagne de plus en plus en importance dans les études cliniques car elle est l'unique modalité d'imagerie en mesure de caractériser in vivo l'architecture et l'intégrité des fibres de la substance blanche. Toutefois, la disponibilité croissante de modèles de diffusion et d'algorithmes de tractographie rend le choix d'une méthode de reconstruction de fibres difficile. Plus important encore, les performances et la reproductibilité de chaque méthode peuvent varier. Cette dernière considération souligne la difficulté de validation des méthodes de tractographie étant donné qu'aucune réalité terrain n'est disponible. Dans ce travail de thèse, nous avons dans un premier temps implémenté et intégré quatre différents algorithmes de tractographie par Imagerie de Tenseur de Diffusion à un logiciel de neuroimagerie. Trois déterministes et un autre probabiliste. Ensuite, nous avons étudié la validation de ces algorithmes sur des données fantôme qui simule une réalité terrain, offrant différentes configurations complexes de fibres. La reproductibilité des algorithmes implémentés a été étudiée sur des données réelles, chez 12 sujets sains en variant la résolution angulaire et en prenant comme faisceau test, le faisceau corticospinal. Les résultats obtenus ont montré une meilleure reproductibilité de l'algorithme probabiliste en conjonction avec une haute résolution angulaire. Enfin, sachant que dans certaines maladies, l'asymétrie entre les faisceaux concernés devrait être différente de celle des sujets sains, nous avons utilisé l'algorithme le plus reproductible pour examiner chez des sujets sains les degrés d'asymétries macro et microstructurale du faisceau corticospinal.Tractography is gaining increasing importance in clinical studies because it is the only imaging modality able to characterize in vivo the architecture and integrity of white matter fibers. However, the increasing availability of diffusion models and tractography algorithms makes the choice of a fiber reconstruction method difficult. More important, the performance and reproducibility of each method can vary. This last observation underscores the difficulty of validating tractography methods since no ground truth is available. In this work, we initially implemented and integrated four different Diffusion Tensor Imaging tractography algorithms in neuroimaging software. Three deterministic and one probabilistic. Next, we studied the validation of these algorithms on phantom data which simulates a given ground truth, offering various complex configurations of fibers. The reproducibility of the implemented algorithms has been studied on real data, in 12 healthy subjects by varying the angular resolution and taking as tractus test, the corticospinal tract. The results showed a better reproducibility of the probabilistic algorithm in conjunction with high angular resolution. Finally, in some diseases, the asymmetry between the tractus involved should be different from that of healthy subjects, we used the most reproducible algorithm to investigate in healthy subjects the levels of macro and microstructural asymmetries in the corticospinal tract

Etude en IRM des modifications des connectivités cérébrales anatomique et fonctionnelle en fonction de l'âge chez le sujet sain

Notre travail a consisté à analyser les modifications au cours de l'âge des connectivités cérébrales anatomique et fonctionnelle. Pour chaque participant, 47 hommes sains (20-65 ans), plusieurs séquences IRM ont été acquises (IRM Philips 3T) : une image anatomique T1 (calcul de l'épaisseur corticale et évaluation de l'atrophie cérébrale), une image de diffusion 32 directions (extraction de la fraction d'anisotropie et la diffusivité moyenne et réalisation de la tractographie) et trois imageries fonctionnelles, au repos, durant une tâche motrice et une attentionnelle, (permettent l'analyse de la connectivité fonctionnelle grâce à des méthodes d'Analyse en Composantes Indépendantes et des méthodes basées sur l'étude statistique des réseaux : Network Based Statistics). Nous avons utilisé les logiciels SPM8 (Statistical Parametric Mapping), MATLAB (The MathWorks, Inc), FSL (FMRIB Software Library), et Statistica (Statsoft). L'imagerie fonctionnelle a permis de mettre en évidence le rôle joué par le Gyrus Angulaire (dont de nombreuses connexions se modifient), et des modifications intervenant dans le réseau du Default-Mode et celui de la Mémoire de Travail (diminutions dans le lobe frontal). Nous avons également observé une diminution de l'orientation des fibres dans la partie antérieure du Corps Calleux et dans le cervelet. L'étude de la connectivité anatomique a montré un ensemble de sous-réseaux d'aires structurellement liées qui résistent au cours de l'âge. Les retombées de ce projet se trouvent dans les potentialités d'application à diverses maladies neurodégénératives puisqu'il permet une meilleure caractérisation du vieillissement physiologique.Our study was focused on the changes of anatomical and functional brain connectivity during aging. We acquired for each participant (47 male subjects, healthy, aged from 20 to 65) several MRI imaging (Philips 3T MRI): an anatomical sequence (T1 weighted image), a sequence of diffusion imaging in 32 directions and three sequences of functional imaging (at rest, during a motor task and an attentional). The anatomical image allows us to assess the brain atrophy and calculate the cortical thickness. With the diffusion tensor imaging (DTI) we have extracted fractional anisotropy and mean diffusivity and we have realized tractography. We used different software as SPM8 (Statistical Parametric Mapping), MATLAB (The MathWorks, Inc.) and Statistica (Statsoft). We have analyzed the functional connectivity with the 3 sequences of fMRI using methods of Independent Component Analysis, and methods based on statistical analysis of networks (Network Based Statistics). Functional imaging has showed the role played by the Angular Gyrus (including many modifications on connections), and changes occurring in the Default-Mode Network and the Working Memory (decreases in the frontal lobe). Furthermore, we have demonstrated a decrease in fiber orientation in the anterior part of the Corpus Callosum, and in the cerebellum. Due to the study of anatomical connectivity, we have defined a set of sub-networks that resist structurally with age. Our contribution will allow a better characterization of the effect of normal aging on brain connectivity. Besides, benefits of this study may be useful for the comprehension of neurodegenerative diseases such as Alzheimer and Parkinson

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered