Détection des croisements de fibre en IRM de diffusion par décomposition de tenseur : Approche analytique

National audienceL'IRM de diffusion (IRMd) est l'unique modalité qui permet d'explorer les structures neuronales de la substance blanche in-vivo et de manière non-invasive. La diffusion a d'abord été modélisée par le modèle du tenseur de diffusion du second ordre (DTI). Toutefois, ce modèle trouve rapidement ses limites dans les zones, nombreuses, où les fibres de la matière blanche se croisent. Pour surmonter cette limite et reconstruire les croisements de fibres, différentes approches ont été proposées telles que: l'imagerie à résonance magnétique (IRM) à haute résolution angulaire (HARDI) et les tenseurs d'ordre supérieur (HOT) ; ces méthodes permettent de reconstruire des fonctions telle que la fonction de distribution d'orientation de fibre (FOD) dont les maxima s'alignent sur les orientations des fibres multiples. Dans ce travail, on se propose d'extraire les directions des fibres caractérisées par les maxima de la fonction FOD. Pour cela, une approche analytique de décomposition de tenseur symétrique a été implémentée et efficacement adaptée pour extraire les directions des fibres avec précision. Différents résultats obtenus sur des données synthétiques et réelles illustrent l'efficacité de la méthode

Evaluation des méthodes d'extraction des orientations locales des faisceaux de fibres par analyse quantitative de la connectivité

International audienceRecovering of the fiber orientations in each voxel constitutes an important step for the fiber tracking algorithms. In fact, the reliability of the resulted connectivity depends on how well the local fiber orientations were extracted. Based on the tractography results we evaluated and compared different methods of fiber orientations extraction. Thus, we analyzed quantitatively the resulted connectivity by using the Tractometer tool. This later allows by measuring a number of metrics to quantify the connections reliability and the tractography performance. All the methods of fiber orientations extraction were evaluated on two types of tractography algorithms, deterministic and probabilistic algorithms. Furthermore, all of these methods have been executed on two types of data, high angular resolution data acquired with 60 gradient directions and low angular resolution data, acquired with 30 gradient directions. These two types of data were corrupted with a Ricien noise of ratio SNR=20, 10. In this article, we present the results obtained by our validation and comparison work.La détection des orientations locales des faisceaux de fibres constitue une étape importante pour les algorithmes de suivi de fibres (tractographie).En effet, la fiabilité de la connectivité résultante dépend de la qualité de l'extraction de ces orientations locales de fibres. Sur la base des résultats produits au niveau de la tractographie, nous avons évalué et comparé un ensemble d'algorithmes d'extraction des orientations des faisceaux de fibres. Une analyse quantitative de la connectivité a été ainsi réalisée en utilisant un outil appelé le Tractometer. Cet outil permet grâce à un certain nombre de métriques de quantifier la fiabilité des connexions reconstruites et aussi la performance des algorithmes de suivi de fibre utilisés. Toutes les méthodes d'extraction des orientations de fibres mises en oeuvre ont été évaluées sur la base des résultats de deux types d'algorithmes de tractographie, déterministe et probabiliste. De plus, l'ensemble de ces méthodes ont été exécutées sur deux types de données de diffusion, des données à haute résolution angulaire de 60 directions de gradient et des données à basse résolution angulaire de 30 directions de gradient, ces deux jeux de données ont été corrompus par un bruit Ricien d'un rapport SNR de 20 puis de 10. Dans cet article, nous présentons les résultats obtenus par ce travail de validation et de comparaison

Use of Particle Swarm Optimization for ODF Maxima Extraction

International audienceFiber tracking is winning more and more interest in the neuroscience research field and clinical practice, for its ability in revealing the structural connectivity; the quality of the fiber tracking depends in great extent, on fiber directions extraction The PSO algorithm could give good approximation of these directions

Fiber Orientation Distribution from Non-Negative Sparse Recovery

International audienceThe Fiber Orientation Distribution (FOD) [3] is a high angular resolution diffusion imaging (HARDI) model forrobustly estimating crossing white-matter fiber bundles from q-ball acquisitions. However, its angular resolutiondepends on the spherical harmonic (SH) / tensor basis order, which implies a large number of acquisitions: 45, 66,91 for typically used orders such as 8, 10, 12. Further, it is still necessary to compute the fiber orientations from theFOD. In the literature two ways have been adopted for this purpose: maxima detection and tensor decomposition.To overcome this two step approach (FOD estimation + fiber detection), we have proposed a novel FOD modeland estimation method based on non-negative sparse recovery [1, 2]. The method has the following advantages:(i) it naturally estimates non-negative FODs, (ii) it computes both the FOD (tensor) and the fiber-orientationstogether – making tensor decomposition (which is NP-hard) or maxima detection unnecessary, (iii) it doesn’trequire the number of fiber-compartments to be predefined and (iv) it can estimate very high order FOD tensorsfrom a minimal number of acquisitions (20 or 30). We adopt this method for single shell data of this challenge

Crossing Fibers Detection with an Analytical High Order Tensor Decomposition

International audienceDiffusion magnetic resonance imaging (dMRI) is the only technique to probe in vivo and noninvasively the fiber structure of human brain white matter. Detecting the crossing of neuronal fibers remains an exciting challenge with an important impact in tractography. In this work, we tackle this challenging problem and propose an original and efficient technique to extract all crossing fibers from diffusion signals. To this end, we start by estimating, from the dMRI signal, the so-called Cartesian tensor fiber orientation distribution (CT-FOD) function, whose maxima correspond exactly to the orientations of the fibers. The fourth order symmetric positive definite tensor that represents the CT-FOD is then analytically decomposed via the application of a new theoretical approach and this decomposition is used to accurately extract all the fibers orientations. Our proposed high order tensor decomposition based approach is minimal and allows recovering the whole crossing fibers without any a priori information on the total number of fibers. Various experiments performed on noisy synthetic data, on phantom diffusion, data and on human brain data validate our approach and clearly demonstrate that it is efficient, robust to noise and performs favorably in terms of angular resolution and accuracy when compared to some classical and state-of-the-art approaches

ODF’s maxima extraction using Particle Swarm Optimization. Biomedical

International audiencePrevious works show that brain Diffusion MRI (dMRI) allow to give with more or less great precision the whitematter fiber structure and its possible intersection fibers. In this paper, we show that a Particle Swarm Optimization (PSO) based approach developed for brain dMRI allows to accurately recover the orientations of complex crossing fibers characterized as Orientation Distribution Function (ODF) maxima. Diffusion wasfirst modeled using the Diffusion Tensor Imaging (DTI), but this model has problems detecting crossing fibers and this has led to develop many other approaches to extract crossing fibers. Models have been proposed such as the High Order Tensor techniques and High Angular Resolution Diffusion Imaging (HARDI) from which we can reconstruct functions like ODF, whose maxima do correspond to the orientations of the multiple fibers. In this paper, we parametrize PSO to extract all the crossing fibers characterized as the maxima of the Orientation Distribution Function (PSO-ODF). Promising experimental results obtained with synthetic data illustrate the potential of the technique

Fiber orientation distribution function from non-negative sparse recovery with quantitative analysis of local fiber orientations and tractography using DW-MRI datasets

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Odf Maxima Computation Using Hill Climbing Algorithm

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Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?

International audienceDiffusion magnetic resonance imaging (dMRI) is the modality of choice for investigating in-vivo white matter connectivity and neural tissue architecture of the brain. The diffusion-weighted signal in dMRI reflects the diffusivity of water molecules in brain tissue and can be utilized to produce image-based biomarkers for clinical research. Due to the constraints on scanning time, a limited number of measurements can be acquired within a clinically feasible scan time. In order to reconstruct the dMRI signal from a discrete set of measurements, a large number of algorithms have been proposed in recent years in conjunction with varying sampling schemes, i.e., with varying b-values and gradient directions. Thus, it is imperative to compare the performance of these reconstruction methods on a single data set to provide appropriate guidelines to neuroscientists on making an informed decision while designing their acquisition protocols. For this purpose, the SPArse Reconstruction Challenge (SPARC) was held along with the workshop on Computational Diffusion MRI (at MICCAI 2014) to validate the performance of multiple reconstruction methods using data acquired from a physical phantom. A total of 16 reconstruction algorithms (9 teams) participated in this community challenge. The goal was to reconstruct single b-value and/or multiple b-value data from a sparse set of measurements. In particular, the aim was to determine an appropriate acquisition protocol (in terms of the number of measurements, b-values) and the analysis method to use for a neuroimaging study. The challenge did not delve on the accuracy of these methods in estimating model specific measures such as fractional anisotropy (FA) or mean diffusivity, but on the accuracy of these methods to fit the data. This paper presents several quantitative results pertaining to each reconstruction algorithm. The conclusions in this paper provide a valuable guideline for choosing a suitable algorithm and the corresponding data-sampling scheme for clinical neuroscience applications