42 research outputs found
Towards Clinical Microscopic Fractional Anisotropy Imaging
Microscopic fractional anisotropy (µFA) is a diffusion-weighted magnetic resonance imaging (dMRI) metric that is sensitive to neuron microstructural features without being confounded by the orientation dispersion of axons and dendrites. µFA may potentially act as a surrogate biomarker for neurodegeneration, demyelination, and other pathological changes to neuron microstructure with greater specificity than other dMRI techniques that are sensitive to orientation dispersion, such as diffusion tensor imaging. As with many advanced imaging techniques, µFA is primarily used in research studies and has not seen use in clinical settings.
The primary goal of this Thesis was to assess the clinical viability of µFA by developing a rapid protocol for full brain µFA imaging and then applying it to the study of a neurological disease. Chapter 1 presents the motivation behind this Thesis and a detailed summary of general background information that supports the subsequent chapters. Chapter 2 focuses on the development and optimization of a µFA imaging protocol that involves the acquisition of dMRI data in two encoding schemes, linear tensor encoding and spherical tensor encoding, and then a joint fit of the data to the powder kurtosis signal representation. The technique was shown to have good repeat measurement reliability in white matter and measured values strongly correlated with another µFA computed using the gamma signal representation. In Chapter 3, a modified signal representation was investigated to estimate µFA and other indices while mitigating contaminating partial volume effects from free water, such as the cerebrospinal fluid in ventricles. The work described in Chapter 4 explores the sensitivity of µFA to hippocampal abnormalities in patients with unilateral temporal lobe epilepsy. Chapter 5 summarizes the contributions of this Thesis and provides suggestions for future studies
The role of the temporal pole in temporal lobe epilepsy: A diffusion kurtosis imaging study
This study aimed to evaluate the use of diffusion kurtosis imaging (DKI) to detect microstructural abnormalities within the temporal pole (TP) and its temporopolar cortex in temporal lobe epilepsy (TLE) patients. DKI quantitative maps were obtained from fourteen lesional TLE and ten non-lesional TLE patients, along with twenty-three healthy controls. Data collected included mean (MK); radial (RK) and axial kurtosis (AK); mean diffusivity (MD) and axonal water fraction (AWF). Automated fiber quantification (AFQ) was used to quantify DKI measurements along the inferior longitudinal (ILF) and uncinate fasciculus (Unc). ILF and Unc tract profiles were compared between groups and tested for correlation with disease duration. To characterize temporopolar cortex microstructure, DKI maps were sampled at varying depths from superficial white matter (WM) towards the pial surface. Patients were separated according to the temporal lobe ipsilateral to seizure onset and their AFQ results were used as input for statistical analyses. Significant differences were observed between lesional TLE and controls, towards the most temporopolar segment of ILF and Unc proximal to the TP within the ipsilateral temporal lobe in left TLE patients for MK, RK, AWF and MD. No significant changes were observed with DKI maps in the non-lesional TLE group. DKI measurements correlated with disease duration, mostly towards the temporopolar segments of the WM bundles. Stronger differences in MK, RK and AWF within the temporopolar cortex were observed in the lesional TLE and noticeable differences (except for MD) in non-lesional TLE groups compared to controls. This study demonstrates that DKI has potential to detect subtle microstructural alterations within the temporopolar segments of the ILF and Unc and the connected temporopolar cortex in TLE patients including non-lesional TLE subjects. This could aid our understanding of the extrahippocampal areas, more specifically the temporal pole role in seizure generation in TLE and might inform surgical planning, leading to better seizure outcomes
Automatisation du traitement d'images acquises par IRM de diffusion et techniques d'acquisition avancées avec application sur le primate
L'imagerie par résonance magnétique de diffusison (IRMd) est une technologie d'imagerie médicale non-invasive permettant de cartographier la structure axonale du cerveau et d'en extraire des mesures d'orientation et d'intégrité de la matière blanche. Malgré l'intérêt que connaît le domaine de la recherche en IRMd depuis presque 40 ans, un faible pourcentage des techniques modernes développées sont utilisées au niveau clinique et hospitalier. Cela vient en grande partie du fait que la communauté connaît un grand problème de variabilité et de validation, rendant la mise en application des technologies ardue et risquée. Pour valider l'exécution d'un algorithme ou la validité d'une théorie, comme aucune mesure étalon n'existe en IRMd, il est usuel de chercher à reproduire les résultats observés chez les humains dans le cerveau d'animaux similaires. Pour cela, les primates sont particulièrement intéressants, puisque la morphologie de leur cerveau est très proche de celle de l'humain. Cependant, peu d'outils de traitement automatisés dans le domaine de l'IRMd développés pour l'humain s'exécutent correctement sur les images de petit animal ou de primate. Les images sont acquises à des résolutions spatiales plus fines et angulaires plus riches et souffrent généralement d'artéfacts plus intenses, requérant plus d'itérations pour converger et une configuration fine des paramètres d'exécutions. Dans ce mémoire, nous présentons un nouvel outil d'automatisation de traitement des données d'IRMd, pouvant être utilisé pour produire des modèles et des mesures de diffusion. Nous exposons son implémentation modulaire permettant une maintenance simple des dépendances, modules et algorithmes et une configuration étendue des étapes de traitement. Nous démontrons la robustesse et la reproducibilité de son exécution sur des données d'IRMd haute résolution. Nous présentons aussi une étude de la variabilité des données de diffusion de primates contenues dans la base de données PRIME-DE
Quantification of Tissue Microstructure Using Tensor-Valued Diffusion Encoding: Brain and Body
Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique to probe tissue microstructure. Conventional Stejskal–Tanner diffusion encoding (i.e., encoding along a single axis), is unable to disentangle different microstructural features within a voxel; If a voxel contains microcompartments that vary in more than one attribute (e.g., size, shape, orientation), it can be difficult to quantify one of those attributes in isolation using Stejskal–Tanner diffusion encoding. Multidimensional diffusion encoding, in which the water diffusion is encoded along multiple directions in q-space (characterized by the so-called “b-tensor”) has been proposed previously to solve this problem. The shape of the b-tensor can be used as an additional encoding dimension and provides sensitivity to microscopic anisotropy. This has been applied in multiple organs, including brain, heart, breast, kidney and prostate. In this work, we discuss the advantages of using b-tensor encoding in different organs
Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms
Purpose: To introduce a method to create 3D-printed axon-mimetic phantoms with complex fibre orientations to characterise the performance of diffusion magnetic resonance imaging (MRI) models and representations in the presence of orientation dispersion. Methods: An extension to an open-source 3D printing package was created to produce a set of five 3D-printed axon-mimetic (3AM) phantoms with various combinations of bending and crossing fibre orientations. A two-shell diffusion MRI scan of the five phantoms in water was performed at 9.4T. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), the ball and stick model, neurite orientation density and dispersion imaging (NODDI), and Bingham-NODDI were all fit to the resulting diffusion MRI data. A ground truth map of that phantom’s crossing angles and/or arc radius was registered to the diffusion-weighted images. Metrics from each model and representation were compared to the ground-truth maps, and a quadratic regression model was fit to each combination of output metric and ground-truth metric. Results: The mean diffusivity (MD) metric defined by DTI was insensitive to crossing angle but increased with fibre curvature. Axial diffusivity (AD) decreased with increasing crossing angle. DKI’s diffusivity metrics replicated the trends seen in DTI, and its mean kurtosis (MK) metric decreased with fibre curvature, except in regions with high crossing angles. The estimated stick volume fraction in the ball and stick model decreased with increasing fibre curvature and crossing angle. NODDI’s intra-neurite volume fraction was insensitive to crossing angle, and its orientation dispersion index (ODI) was correlated to crossing angle. Bingham-NODDI’s intra-neurite volume fraction was also insensitive to crossing angle, while its primary ODI (ODIP) was also correlated to crossing angle and its secondary ODI (ODIS) was insensitive to crossing angle. For both NODDI models, the volume fractions of the extra-neurite and CSF compartments had low reliability with no clear relationship to crossing angle. Conclusion: Inexpensive 3D-printed axon-mimetic phantoms can be used to investigate the effect of fibre curvature and crossings on diffusion MRI representations and models of diffusion signal. The dependence of several representations and models on fibre dispersion/crossing was investigated. As expected, Bingham-NODDI was best able to characterise planar fibre dispersion in the phantoms
Diffusion-Weighted Imaging: Recent Advances and Applications
Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain “in vivo”, and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications
The sensitivity of diffusion MRI to microstructural properties and experimental factors
Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic
Validation of MRtrix tractography for clinical use
Tractography is a technique which uses non-invasive diffusion magnetic resonance imaging (dMRI) to model the structural connections of the brain in vivo. Since it is not known to what extent the exact positions of white matter tracts vary between individuals, and how scan parameters affect the reconstructed location of tracts, the performance of tractography algorithms cannot be assessed by comparing them against a standardised model of white matter in the human brain. Until technology has advanced to a stage where these issues can be resolved, if this is indeed possible, the quality of tractograms must be assessed via alternative means if they are to be used in a clinical setting. MRtrix is a software package which offers a suite of tools for tractography. To investigate the clinical viability of tractography, the effect of adding artificial noise and patient movement to the input dMRI scans was examined. The performance of the following three MRtrix tractography algorithms was considered: the second-order integration over fibre orientation distributions (iFOD2) algorithm, the spherical deconvolution streamlines tractography (SD_STREAM) algorithm, and the probabilistic tractography (Tensor_Prob) algorithm. The accuracy of each tractogram and its robustness to added noise and motion was assessed through both quantitative and qualitative means. Streamline length histograms, polar plots, a resemblance metric, the RMS difference between tractmaps and polar plots, and false positive and negative rates were used to quantitatively measure tractogram quality. Each of these techniques provide measures of quality relative to an initial unmodified scan. Potentially, these techniques could be extended to provide a stand-alone metric of tractogram quality to eliminate operator dependence in diagnosing, treating and managing conditions associated with the integrity of white matter pathways in the brain. The tractography algorithm found to be most suitable for clinical applications was iFOD2, which provided the highest level of detail compared with the SD_STREAM and Tensor_Prob algorithms. iFOD2 was capable of consistently resolving tracts with added noise of up to about an added white Gaussian noise (WGN) of power 20, or a SNR of about 8 for the majority of patients (calculated using the single image method). Additionally, the iFOD2 algorithm successfully reconstructed tractograms with added translations up to 5 mm, and rotations up to 10 degrees, despite the reconstructed tracts being spatially shifted.Thesis (MPhil.) -- University of Adelaide, School of Physical Sciences, 202
Analyse de l'architecture de la matière blanche et projection de mesures sur la surface corticale
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