14 research outputs found

    Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution

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    We propose two strategies to improve the quality of tractography results computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both methods are based on the same PDE framework, defined in the coupled space of positions and orientations, associated with a stochastic process describing the enhancement of elongated structures while preserving crossing structures. In the first method we use the enhancement PDE for contextual regularization of a fiber orientation distribution (FOD) that is obtained on individual voxels from high angular resolution diffusion imaging (HARDI) data via constrained spherical deconvolution (CSD). Thereby we improve the FOD as input for subsequent tractography. Secondly, we introduce the fiber to bundle coherence (FBC), a measure for quantification of fiber alignment. The FBC is computed from a tractography result using the same PDE framework and provides a criterion for removing the spurious fibers. We validate the proposed combination of CSD and enhancement on phantom data and on human data, acquired with different scanning protocols. On the phantom data we find that PDE enhancements improve both local metrics and global metrics of tractography results, compared to CSD without enhancements. On the human data we show that the enhancements allow for a better reconstruction of crossing fiber bundles and they reduce the variability of the tractography output with respect to the acquisition parameters. Finally, we show that both the enhancement of the FODs and the use of the FBC measure on the tractography improve the stability with respect to different stochastic realizations of probabilistic tractography. This is shown in a clinical application: the reconstruction of the optic radiation for epilepsy surgery planning

    Fibre tract segmentation for intraoperative diffusion MRI in neurosurgical patients using tract-specific orientation atlas and tumour deformation modelling

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    Purpose:: Intraoperative diffusion MRI could provide a means of visualising brain fibre tracts near a neurosurgical target after preoperative images have been invalidated by brain shift. We propose an atlas-based intraoperative tract segmentation method, as the standard preoperative method, streamline tractography, is unsuitable for intraoperative implementation. Methods:: A tract-specific voxel-wise fibre orientation atlas is constructed from healthy training data. After registration with a target image, a radial tumour deformation model is applied to the orientation atlas to account for displacement caused by lesions. The final tract map is obtained from the inner product of the atlas and target image fibre orientation data derived from intraoperative diffusion MRI. Results:: The simple tumour model takes only seconds to effectively deform the atlas into alignment with the target image. With minimal processing time and operator effort, maps of surgically relevant tracts can be achieved that are visually and qualitatively comparable with results obtained from streamline tractography. Conclusion:: Preliminary results demonstrate feasibility of intraoperative streamline-free tract segmentation in challenging neurosurgical cases. Demonstrated results in a small number of representative sample subjects are realistic despite the simplicity of the tumour deformation model employed. Following this proof of concept, future studies will focus on achieving robustness in a wide range of tumour types and clinical scenarios, as well as quantitative validation of segmentations

    Analyse et reconstruction de faisceaux de la matière blanche

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    L'imagerie par résonance magnétique de diffusion (IRMd) est une modalité d'acquisition permettant de sonder les tissus biologiques et d'en extraire une variété d'informations sur le mouvement microscopique des molécules d'eau. Plus spécifiquement à l'imagerie médicale, l'IRMd permet l'investigation des structures fibreuses de nombreux organes et facilite la compréhension des processus cognitifs ou au diagnostic. Dans le domaine des neurosciences, l'IRMd est cruciale à l'exploration de la connectivité structurelle de la matière blanche. Cette thèse s'intéresse plus particulièrement à la reconstruction de faisceaux de la matière blanche ainsi qu'à leur analyse. Toute la complexité du traitement des données commençant au scanneur jusqu'à la création d'un tractogramme est extrêmement importante. Par contre, l'application spécifique de reconstruction des faisceaux anatomiques plausibles est ultimement le véritable défi de l'IRMd. L'optimisation des paramètres de la tractographie, le processus de segmentation manuelle ou automatique ainsi que l'interprétation des résultats liée à ces faisceaux sont toutes des étapes du processus avec leurs lots de difficultés

    The Superoanterior Fasciculus (SAF): A Novel White Matter Pathway in the Human Brain?

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    Fiber tractography (FT) using diffusion magnetic resonance imaging (dMRI) is widely used for investigating microstructural properties of white matter (WM) fiber-bundles and for mapping structural connections of the human brain. While studying the architectural configuration of the brain’s circuitry with FT is not without controversy, recent progress in acquisition, processing, modeling, analysis, and visualization of dMRI data pushes forward the reliability in reconstructing WM pathways. Despite being aware of the well-known pitfalls in analyzing dMRI data and several other limitations of FT discussed in recent literature, we present the superoanterior fasciculus (SAF), a novel bilateral fiber tract in the frontal region of the human brain that—to the best of our knowledge—has not been documented. The SAF has a similar shape to the anterior part of the cingulum bundle, but it is located more frontally. To minimize the possibility that these FT findings are based on acquisition or processing artifacts, different dMRI data sets and processing pipelines have been used to describe the SAF. Furthermore, we evaluated the configuration of the SAF with complementary methods, such as polarized light imaging (PLI) and human brain dissections. The FT results of the SAF demonstrate a long pathway, consistent across individuals, while the human dissections indicate fiber pathways connecting the postero-dorsal with the antero-dorsal cortices of the frontal lobe

    New tractography methods based on parametric models of white matter fibre dispersion

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    Diffusion weighted magnetic resonance imaging (DW-MRI) is a powerful imaging technique that can probe the complex structure of the body, revealing structural trends which exist at scales far below the voxel resolution. Tractography utilises the information derived from DW-MRI to examine the structure of white matter. Using information derived from DW-MRI, tractography can estimate connectivity between distinct, functional cortical and sub-cortical regions of grey matter. Understanding how seperate functional regions of the brain are connected as part of a network is key to understanding how the brain works. Tractography has been used to deliniate many known white matter structures and has also revealed structures not fully understood from anatomy due to limitations of histological examination. However, there still remain many shortcomings of tractography, many anatomical features for which tractography algorithms are known to fail, which leads to discrepancies between known anatomy and tractography results. With the aim of approaching a complete picture of the human connectome via tractography, we seek to address the shortcomings in current tractography techniques by exploiting new advances in modelling techniques used in DW-MRI, which provide more accurate representation of underlying white matter anatomy. This thesis introduces a methodology for fully utilising new tissue models in DWMRI to improve tractography. It is known from histology that there are regions of white matter where fibres disperse or curve rapidly at length scales below the DW-MRI voxel resolution. One area where dispersion is particularly prominent is the corona radiata. New DW-MRI models capture dispersion utilising specialised parametric probability distributions. We present novel tractography algorithms utilising these parametric models of dispersion in tractography to improve connectivity estimation in areas of dispersing fibres. We first present an algorithm utilising the the new parametric models of dispersion for tractography in a simple Bayesian framework. We then present an extension to this algorithm which introduces a framework to pool neighbourhood information from multiple voxels in the neighbournhood surrounding the tract in order to better estimate connectivity, introducing the new concept of the neighbourhood-informed orientation distribution function (NI-ODF). Specifically, using neighbourhood exploration we address the ambiguity arising in ’fanning polarity’. In regions of dispersing fibres, the antipodal symmetry inherent in DW-MRI makes it impossible to resolve the polarity of a dispersing fibre configuration from a local voxel-wise model in isolation, by pooling information from neighbouring voxels, we show that this issue can be addressed. We evaluate the newly proposed tractography methods using synthetic phantoms simulating canonical fibre configurations and validate the ability to effectively navigate regions of dispersing fibres and resolve fanning polarity. We then validate that the algorithms perform effectively in real in vivo data, using DW-MRI data from 5 healthy subjects. We show that by utilising models of dispersion, we recover a wider range of connectivity compared to other standard algorithms when tracking through an area of the brain known to have significant white fibre dispersion - the corona radiata. We then examine the impact of the new algorithm on global connectivity estimates in the brain. We find that whole brain connectivity networks derived using the new tractography method feature strong connectivity between frontal lobe regions. This is in contrast to networks derived using competing tractography methods which do not account for sub-voxel fibre dispersion. We also compare thalamo-cortical connectivity estimated using the newly proposed tractography method and compare with a compteing tractography method, finding that the recovered connectivity profiles are largely similar, with some differences in thalamo-cortical connections to regions of the frontal lobe. The results suggest that fibre dispersion is an important structural feature to model in the basis of a tractography algorithm, as it has a strong effect on connectivity estimation

    Analytical and fast Fiber Orientation Distribution reconstruction in 3D-Polarized Light Imaging

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    International audienceThree dimensional Polarized Light Imaging (3D-PLI) is an optical technique which allows mapping the spatial fiber architecture of fibrous postmortem tissues, at sub-millimeter resolutions. Here, we propose an analytical and fast approach to compute the fiber orientation distribution (FOD) from high-resolution vector data provided by 3D-PLI. The FOD is modeled as a sum of K orientations/Diracs on the unit sphere, described on a spherical harmonics basis and analytically computed using the spherical Fourier transform. Experiments are performed on rich synthetic data which simulate the geometry of the neuronal fibers and on human brain data. Results indicate the analytical FOD is computationally efficient and very fast, and has high angular precision and angular resolution. Furthermore, investigations on the right occipital lobe illustrate that our strategy of FOD computation enables the bridging of spatial scales from microscopic 3D-PLI information to macro-or mesoscopic dimensions of diffusion Magnetic Resonance Imaging (MRI), while being a means to evaluate prospective resolution limits for diffusion MRI to reconstruct regionspecific white matter tracts. These results demonstrate the interest and great potential of our analytical approach

    Tractographie par apprentissage par renforcement

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    L'Imagerie par Résonance Magnétique de diffusion (IRMd) est présentement la seule technique non-invasive permettant d'étudier la structure de la matière blanche dans le cerveau humain. L'IRMd permet une reconstruction indirecte de la matière blanche grâce à la modélisation du mouvement de l'eau et la tractographie. La tractographie a été décrite comme un problème mal-posé; malgré les nombreux algorithmes développés, il demeure très difficile d'évaluer la connectivité globale du cerveau selon des actions basées sur des informations locales. Motivées par l'explosion des performances de l'apprentissage profond supervisé, des tentatives ont été faites afin d'utiliser cet outil pour concevoir des algorithmes de tractographie exempts des problèmes affligeant la tractographie classique. Cependant ces méthodes, apprenant de données provenant des algorithmes classiques, sont à ce jour vouées à reproduire les même erreurs. Parallèlement, l'apprentissage profond par renforcement a récemment connu des avancées extraordinaires menant à des percées telles que AlphaGo. L'apprentissage profond par renforcement, par opposition à l'apprentissage profond supervisé, permet à l'algorithme d'apprendre par exploration, ne requérant qu'un signal récompensant les actions adéquates de l'agent apprenant. Dans ce mémoire, nous aborderons la possibilité d'apprendre à un algorithme d'apprentissage profond par renforcement à reconstruire les chemins de la matière blanche sans avoir recourt à des données biaisées par les algorithmes classiques. Nous poserons le problème de la tractographie dans le contexte de l'apprentissage par renforcement, décrirons les pièges à éviter lors de la conception d'un tel algorithme, puis proposerons une méthode permettant d'obtenir des résultats compétitifs aux algorithmes de tractographie existants

    Analyse de l'architecture de la matière blanche et projection de mesures sur la surface corticale

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    L'étude de l'architecture et de la connectivité structurelle du cerveau est possible grâce à l'imagerie par résonance magnétique de diffusion (IRMd). Ce type d’image, similaire à un champ vectoriel tridimensionnel, combiné à un algorithme nommé tractographie, permet d’inférer la distribution des fibres de matière blanche et ainsi de reconstruire la structure locale du tissu. Or, cette méthode demeure limitée par une basse résolution et un faible rapport signal sur bruit. Afin de contourner ces limitations, des modèles géométriques construits à partir d’aprioris anatomiques sont utilisés. Cette thèse montre que des règles et des contraintes basées sur la modélisation corticale peuvent être intégrées à la tractographie par le biais d’équations de géométrie différentielle. En effet, la structure axonale sous-jacente à la matière grise peut être approximée avec l'utilisation de la surface et d'un flot de courbure moyenne. Pondéré par l’information de densité, ce flot permet d’obtenir une meilleure représentation des projections des fibres de matière blanche sous le cortex. D'ailleurs, le fait d’incorporer la surface corticale, obtenue d’une image anatomique haute résolution, à l’IRMd permet d'augmenter la précision de la tractographie. Puisque l'acquisition d'une image anatomique (pondération T1) est toujours faite lors d'une IRMd, la combinaison des deux est une façon simple et peu coûteuse d'améliorer cette technique de reconstruction. Par ailleurs, discrétiser les surfaces corticales à l'aide de maillages, plutôt qu’avec des masques voxeliques, permet non seulement d'augmenter la précision de l'interface, mais d'intégrer facilement de nouveaux aprioris et de mieux choisir la répartition des positions initiales. L'ajout d'aprioris et de modèles géométriques permet de mieux modéliser près du cortex et ainsi connecter jusqu'aux surfaces corticales. Cette connexion rend possible la projection de mesures de la matière blanche le long du cortex, un domaine également utilisé pour plusieurs analyses anatomiques (ex. épaisseur corticale), magnéto-/électro-encéphalographie (MEG/EEG) et IRM fonctionnelle (IRMf). L'intégration de ces surfaces corticales à la tractographie a un impact important pour les recherches multimodales sur la connectivité cérébrale
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