Mechanisms of Cognitive Impairment in Cerebral Small Vessel Disease: Multimodal MRI Results from the St George's Cognition and Neuroimaging in Stroke (SCANS) Study.

Cerebral small vessel disease (SVD) is a common cause of vascular cognitive impairment. A number of disease features can be assessed on MRI including lacunar infarcts, T2 lesion volume, brain atrophy, and cerebral microbleeds. In addition, diffusion tensor imaging (DTI) is sensitive to disruption of white matter ultrastructure, and recently it has been suggested that additional information on the pattern of damage may be obtained from axial diffusivity, a proposed marker of axonal damage, and radial diffusivity, an indicator of demyelination. We determined the contribution of these whole brain MRI markers to cognitive impairment in SVD. Consecutive patients with lacunar stroke and confluent leukoaraiosis were recruited into the ongoing SCANS study of cognitive impairment in SVD (n = 115), and underwent neuropsychological assessment and multimodal MRI. SVD subjects displayed poor performance on tests of executive function and processing speed. In the SVD group brain volume was lower, white matter hyperintensity volume higher and all diffusion characteristics differed significantly from control subjects (n = 50). On multi-predictor analysis independent predictors of executive function in SVD were lacunar infarct count and diffusivity of normal appearing white matter on DTI. Independent predictors of processing speed were lacunar infarct count and brain atrophy. Radial diffusivity was a stronger DTI predictor than axial diffusivity, suggesting ischaemic demyelination, seen neuropathologically in SVD, may be an important predictor of cognitive impairment in SVD. Our study provides information on the mechanism of cognitive impairment in SVD

Improvement of data quality for Diffusion Kurtosis Imaging and application to clinical neurological research

Understanding human brain function and dysfunction is one of the major challenges of our century. One of the most popular methods to achieve this goal is in vivo magnetic resonance imaging. In particular, diffusion-weighted (DW) imaging has become a standard tool to non-invasively study white matter structure in vivo. The main contributions of this work can be divided in two parts. The first part deals with the development of pre-processing methods to improve image quality and the accuracy of diffusion tensor and diffusion kurtosis-derived parameters. First, we describe and evaluate a novel method to correct data misalignment due to subject motion. Using an iterative model-based approach, individual diffusion images are realigned to their own theoretical pair, rather than to the unweighted image. A recently developed advanced measure of tensor distance was used as a stopping criterion. The accuracy of the method is tested via a simulated diffusion tensor imaging data set. We have shown here that our procedure is a reliable and efficient way to correct subject motion during DW acquisitions, and that with a proper acquisition setup, it performs better than standard coregistration procedures. Next, we studied the influence of noise in diffusion kurtosis imaging. Two noise correction approaches are proposed and applied to a pool of 25 subjects to evaluate inter-subject variability and the impact of noise correction. Additionally, data were acquired on a single subject with different head positions within the coil and different acquisition scheme to evaluate the impact of noise correction on within-subject variability. Results show a strong impact of noise correction on the estimated mean kurtosis, while the estimation of fractional anisotropy and mean diffusivity were less affected. Both within- and between-subject signal-to-noise (SNR) related variability of the mean kurtosis estimate is considerably reduced after correction for the noise bias, leading to more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves the accuracy of diffusion kurtosis metrics. Diffusion kurtosis imaging acquisitions at higher spatial resolution are made possible, which increases the chances to make valuable inferences in group analysis.The second part of this thesis deals with a clinical application of these methods. A large group of patients with early-stage Parkinson’s disease was investigated with diffusion kurtosis imaging and compared with a group of age- and sex-matched healthy volunteers using voxel-based analysis. Kurtosis metrics show more sensitivity to white matter changes than standard diffusion metrics. Significant differences were found in posterior cerebral areas as well as subcortical regions like the putamen, and are therefore promising results.Comprendre le fonctionnement et le dysfonctionnement du cerveau humain est l’un des grands défis de ce siècle. Pour atteindre ce but, l’imagerie par résonance magnétique (IRM) in vivo est devenue une technique incontournable. En particulier, l’IRM de diffusion est aujourd’hui un outil standard et non invasif pour étudier la structure de la matière blanche in vivo. Les principales constributions de ce travail de thèse se divisent en deux parties. Dans une première partie, deux nouvelles méthodes pour le prétraitement des images sont développées afin d’améliorer la qualité de celles-ci. Ces méthodes permettront également d’augmenter la reproductibilité et la précision des paramètres dérivés des tenseurs de diffusion et de kurtosis. Tout d’abord, nous présentons et évaluons une nouvelle méthode pour recaler les images, initialement décalées à cause des mouvements du sujet. Via une approche itérative et qui repose sur un modèle, chaque image de diffusion est recalée individuellement sur sa propre paire théorique plutôt que sur l’image non pondérée en diffusion. Comme critère d’arrêt, nous avons utilisé une measure de distance entre deux tenseurs. Un set de données de tenseurs de diffusion a été simulé pour tester la performance de cette méthode. Nous avons démontré que notre procédure est une technique fiable et efficace pour corriger les effets dus aux mouvements du sujet pendant l’acquisition des images de diffusion. Nous avons également mis en évidence que cette méthode, utilisée avec des paramètres d’acquisition adéquats, permet d’obtenir de meilleurs résultats par rapport aux méthodes standard de recalage. Ensuite, toujours pour améliorer la qualité des images, nous avons étudié l’influence du bruit dans le cadre de l’imagerie du tenseur de kurtosis. Deux méthodes de correction du bruit sont proposées et appliquées sur les données acquises sur 25 sujets afin d’évaluer la variabilité inter-sujets et l’impact de la correction du bruit sur cette variabilité. De plus, plusieurs sets de données ont été acquis sur un mˆeme sujet, en faisant varier d’une part la position de la tˆete à l’intérieur de l’antenne et d’autre part les paramètres d’acquisition, afin d’étudier l’impact de la correction du bruit sur la variabilité intra-sujet. Les résultats montrent un effet très important du bruit sur l’estimation du coefficient de kurtosis moyen. Par contre cet effet est relativement plus faible sur l’estimation de l’anisotropie fractionnelle et de la diffusivité moyenne. Après correction du bruit, la dépendance du coefficient moyen de kurtosis avec le rapport signal sur bruit, ainsi que de la variabilité intra- et inter-sujets, sont considérablement réduites, conduisant à des mesures plus justes et reproductibles. Nous avons donc proposé ici une méthode simple qui améliore la justesse et la précision des métriques dérivées des tenseurs de kurtosis, indépendemment du niveau de bruit. Il est donc possible d’augmenter la résolution spatiale et ainsi d’augmenter les chances de trouver des différences en comparant deux groupes de sujets. Dans une deuxième partie, nous avons appliqués les méthodes développées dans la première partie à une étude de recherche clinique. Un groupe de patients diagnostiqués à un stade pécoce de la maladie de Parkinson a suivi un protocole d’acquisition d’imagerie du tenseur de kurtosis et les données ont été comparées voxel par voxel avec celles acquises dans un groupe de sujets sains, avec une répartition semblable de l’ˆage et du sexe. Les paramètres dérivés du tenseur de kurtosis sont plus sensibles aux changements de la structure de la matière blanche que les paramètres standard dérivés du tenseur de diffusion. Des différences significatives ont été trouvées dans les régions cérébrales postérieures ainsi que dans les régions sous-corticales comme le putamen. Les résultats sont donc prometteurs.Methods in Neuroimagin

Registering Histological and MR Images of Prostate for Image-based Cancer Detection

Rationale and Objectives Needle biopsy is currently the only way to confirm prostate cancer. To increase prostate cancer diagnostic rate, needles are expected to be deployed at suspicious cancer locations. High contrast MR imaging provides a powerful tool for detecting suspicious cancerous tissues. To do this, MR appearances of cancerous tissue should be characterized and learned from a sufficient number of prostate MR images with known cancer information. However, ground-truth cancer information is only available in histological images. Therefore, it is necessary to warp ground-truth cancerous regions in histological images to MR images by a registration procedure. The objective of this paper is to develop a registration technique for aligning histological and MR images of the same prostate. Material and Methods Five pairs of histological and T2-weighted MR images of radical prostatectomy specimens are collected. For each pair, registration is guided by two sets of correspondences that can be reliably established on prostate boundaries and internal salient blob-like structures of histological and MR images. Results Our developed registration method can accurately register histological and MR images. It yields results comparable to manual registration, in terms of landmark distance and volume overlap. It also outperforms both affine registration and boundary-guided registration methods. Conclusions We have developed a novel method for deformable registration of histological and MR images of the same prostate. Besides the collection of ground-truth cancer information in MR images, the method has other potential applications. An automatic, accurate registration of histological and MR images actually builds a bridge between in vivo anatomical information and ex vivo pathological information, which is valuable for various clinical studies

Information-Theoretic Registration with Explicit Reorientation of Diffusion-Weighted Images

We present an information-theoretic approach to the registration of images with directional information, and especially for diffusion-Weighted Images (DWI), with explicit optimization over the directional scale. We call it Locally Orderless Registration with Directions (LORD). We focus on normalized mutual information as a robust information-theoretic similarity measure for DWI. The framework is an extension of the LOR-DWI density-based hierarchical scale-space model that varies and optimizes the integration, spatial, directional, and intensity scales. As affine transformations are insufficient for inter-subject registration, we extend the model to non-rigid deformations. We illustrate that the proposed model deforms orientation distribution functions (ODFs) correctly and is capable of handling the classic complex challenges in DWI-registrations, such as the registration of fiber-crossings along with kissing, fanning, and interleaving fibers. Our experimental results clearly illustrate a novel promising regularizing effect, that comes from the nonlinear orientation-based cost function. We show the properties of the different image scales and, we show that including orientational information in our model makes the model better at retrieving deformations in contrast to standard scalar-based registration.Comment: 16 pages, 19 figure

A diffusion MRI-based spatiotemporal continuum of the embryonic mouse brain for probing gene-neuroanatomy connections

The embryonic mouse brain undergoes drastic changes in establishing basic anatomical compartments and laying out major axonal connections of the developing brain. Correlating anatomical changes with gene-expression patterns is an essential step toward understanding the mechanisms regulating brain development. Traditionally, this is done in a cross-sectional manner, but the dynamic nature of development calls for probing gene-neuroanatomy interactions in a combined spatiotemporal domain. Here, we present a four-dimensional (4D) spatiotemporal continuum of the embryonic mouse brain from E10.5 to E15.5 reconstructed from diffusion magnetic resonance microscopy (dMRM) data. This study achieved unprecedented high-definition dMRM at 30- to 35-µm isotropic resolution, and together with computational neuroanatomy techniques, we revealed both morphological and microscopic changes in the developing brain. We transformed selected gene-expression data to this continuum and correlated them with the dMRM-based neuroanatomical changes in embryonic brains. Within the continuum, we identified distinct developmental modes comprising regional clusters that shared developmental trajectories and similar gene-expression profiles. Our results demonstrate how this 4D continuum can be used to examine spatiotemporal gene-neuroanatomical interactions by connecting upstream genetic events with anatomical changes that emerge later in development. This approach would be useful for large-scale analysis of the cooperative roles of key genes in shaping the developing brain

Methods for Data Management in Multi-Centre MRI Studies and Applications to Traumatic Brain Injury

Neuroimaging studies are becoming increasingly bigger, and multi-centre collaborations to collect data under similar protocols, but different scanning sites, are now commonplace.However, with increasing sample size the complexity of databases and the entailed data management as well as computational burden are growing. This thesis aims to highlight and address challenges faced by large multi-centre magnetic resonance imaging(MRI) studies. The methods implemented are then applied to traumatic brain injury (TBI) data.Firstly, a pre-processing pipeline for both anatomical and diffusion MRI was proposed, that allows for a high throughput of MRI scans. After describing the choices for processing tools,the performance of the integrated quality assurance was assessed based on the results from a large multi-centre dataset for TBI. Secondly, the applicability of the pipelines for processing mild TBI (mTBI) data from three sites was shown in a case study. For this, volumetric and diffusion metrics in the acute phase are analysed for their prognostic potential. Further-more, the cohort was examined for longitudinal changes. Thirdly, independent scan-rescan datasets are examined to gain a better understanding of the degree of reproducibility which can be achieved in imaging studies. This involves analysing the robustness of brain parcellations based on structural or diffusion imaging. The effect of using different MRI scanners or imaging protocols was also assessed and discussed. Fourthly, sources of diffusion MRI variability and different approaches to cope with these are reviewed. Using this foundation,state-of-the art methods for diffusion MRI harmonisation were compared against each other using both a benchmark dataset and mTBI cohort. Lastly, a solution to localise brain lesions was proposed. Its implications for lesion analysis, are assessed in the light of an application to a more severe TBI patient cohort, imaged on two different scanners. Furthermore, a lesion matching algorithm was introduced to automatically examine lesion evolution with time post-injury. In summary, this thesis explored different options for MRI data analysis in the context of large multi-centre studies. Different approaches are studied and compared using a number of different MRI datasets, including scan-rescan data across different MRI scanners and imaging protocols. The potential of the optimised solutions was illustrated through applications to TBI data.CENTER-TB

Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI

Age-related microstructural differences have been detected using diffusion tensor imaging (DTI). Although DTI is sensitive to the effects of aging, it is not specific to any underlying biological mechanism, including demyelination. Combining multiexponential T2 relaxation (MET2) and multishell diffusion MRI (dMRI) techniques may elucidate such processes. Multishell dMRI and MET2 data were acquired from 59 healthy participants aged 17-70 years. Whole-brain and regional age-associated correlations of measures related to multiple dMRI models (DTI, diffusion kurtosis imaging [DKI], neurite orientation dispersion and density imaging [NODDI]) and myelin-sensitive MET2 metrics were assessed. DTI and NODDI revealed widespread increases in isotropic diffusivity with increasing age. In frontal white matter, fractional anisotropy linearly decreased with age, paralleled by increased "neurite" dispersion and no difference in myelin water fraction. DKI measures and neurite density correlated well with myelin water fraction and intracellular and extracellular water fraction. DTI estimates remain among the most sensitive markers for age-related alterations in white matter. NODDI, DKI, and MET2 indicate that the initial decrease in frontal fractional anisotropy may be due to increased axonal dispersion rather than demyelination