On the Reliability of Diffusion Neuroimaging

Over the last years, diffusion imaging techniques like DTI, DSI or Q-Ball received increasin

Improving the clinico-radiological association in neurological diseases

Despite the key role of magnetic resonance imaging (MRI) in the diagnosis and monitoring of multiple sclerosis (MS) and cerebral small vessel disease (SVD), the association between clinical and radiological disease manifestations is often only moderate, limiting the use of MRI-derived markers in the clinical routine or as endpoints in clinical trials. In the projects conducted as part of this thesis, we addressed this clinico-radiological gap using two different approaches. Lesion-symptom association: In two voxel-based lesion-symptom mapping studies, we aimed at strengthening lesion-symptom associations by identifying strategic lesion locations. Lesion mapping was performed in two large cohorts: a dataset of 2348 relapsing-remitting MS patients, and a population-based cohort of 1017 elderly subjects. T2-weighted lesion masks were anatomically aligned and a voxel-based statistical approach to relate lesion location to different clinical rating scales was implemented. In the MS lesion mapping, significant associations between white matter (WM) lesion location and several clinical scores were found in periventricular areas. Such lesion clusters appear to be associated with impairment of different physical and cognitive abilities, probably because they affect commissural and long projection fibers. In the SVD lesion mapping, the same WM fibers and the caudate nucleus were identified to significantly relate to the subjects’ cerebrovascular risk profiles, while no other locations were found to be associated with cognitive impairment. Atrophy-symptom association: With the construction of an anatomical physical phantom, we aimed at addressing reliability and robustness of atrophy-symptom associations through the provision of a “ground truth” for atrophy quantification. The built phantom prototype is composed of agar gels doped with MRI and computed tomography (CT) contrast agents, which realistically mimic T1 relaxation times of WM and grey matter (GM) and showing distinguishable attenuation coefficients using CT. Moreover, due to the design of anatomically simulated molds, both WM and GM are characterized by shapes comparable to the human counterpart. In a proof-of-principle study, the designed phantom was used to validate automatic brain tissue quantification by two popular software tools, where “ground truth” volumes were derived from high-resolution CT scans. In general, results from the same software yielded reliable and robust results across scans, while results across software were highly variable reaching volume differences of up to 8%

Magnetic Resonance Imaging of the Brain in Moving Subjects. Application of Fetal, Neonatal and Adult Brain Studies

Imaging in the presence of subject motion has been an ongoing challenge for magnetic resonance imaging (MRI). Motion makes MRI data inconsistent, causing artifacts in conventional anatomical imaging as well as invalidating diffusion tensor imaging (DTI) reconstruction. In this thesis some of the important issues regarding the acquisition and reconstruction of anatomical and DTI imaging of moving subjects are addressed; methods to achieve high resolution and high signalto- noise ratio (SNR) volume data are proposed. An approach has been developed that uses multiple overlapped dynamic single shot slice by slice imaging combined with retrospective alignment and data fusion to produce self consistent 3D volume images under subject motion. We term this method as snapshot MRI with volume reconstruction or SVR. The SVR method has been performed successfully for brain studies on subjects that cannot stay still, and in some cases were moving substantially during scanning. For example, awake neonates, deliberately moved adults and, especially, on fetuses, for which no conventional high resolution 3D method is currently available. Fine structure of the in-utero fetal brain is clearly revealed for the first time with substantially improved SNR. The SVR method has been extended to correct motion artifacts from conventional multi-slice sequences when the subject drifts in position during data acquisition. Besides anatomical imaging, the SVR method has also been further extended to DTI reconstruction when there is subject motion. This has been validated successfully from an adult who was deliberately moving and then applied to inutero fetal brain imaging, which no conventional high resolution 3D method is currently available. Excellent fetal brain 3D apparent diffusion coefficient (ADC) maps in high resolution have been achieved for the first time as well as promising fractional Anisotropy (FA) maps. Pilot clinical studies using SVR reconstructed data to study fetal brain development in-utero have been performed. Growth curves for the normally developing fetal brain have been devised by the quantification of cerebral and cerebellar volumes as well as some one dimensional measurements. A Verhulst model is proposed to describe these growth curves, and this approach has achieved a correlation over 0.99 between the fitted model and actual data

Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach

Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of many adults. It a ects almost 1:5 - 5% of the general population. Sub- Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates of morbidity and mortality. Therefore, radiologists aim to detect it and diagnose it at an early stage, by analyzing the medical images, to prevent or reduce its damages. The analysis process is traditionally done manually. However, with the emerging of the technology, Computer-Aided Diagnosis (CAD) algorithms are adopted in the clinics to overcome the traditional process disadvantages, as the dependency of the radiologist's experience, the inter and intra observation variability, the increase in the probability of error which increases consequently with the growing number of medical images to be analyzed, and the artifacts added by the medical images' acquisition methods (i.e., MRA, CTA, PET, RA, etc.) which impedes the radiologist' s work. Due to the aforementioned reasons, many research works propose di erent segmentation approaches to automate the analysis process of detecting a CA using complementary segmentation techniques; but due to the challenging task of developing a robust reproducible reliable algorithm to detect CA regardless of its shape, size, and location from a variety of the acquisition methods, a diversity of proposed and developed approaches exist which still su er from some limitations. This thesis aims to contribute in this research area by adopting two promising techniques based on the multiresolution and statistical approaches in the Two-Dimensional (2D) domain. The rst technique is the Contourlet Transform (CT), which empowers the segmentation by extracting features not apparent in the normal image scale. While the second technique is the Hidden Markov Random Field model with Expectation Maximization (HMRF-EM), which segments the image based on the relationship of the neighboring pixels in the contourlet domain. The developed algorithm reveals promising results on the four tested Three- Dimensional Rotational Angiography (3D RA) datasets, where an objective and a subjective evaluation are carried out. For the objective evaluation, six performance metrics are adopted which are: accuracy, Dice Similarity Index (DSI), False Positive Ratio (FPR), False Negative Ratio (FNR), speci city, and sensitivity. As for the subjective evaluation, one expert and four observers with some medical background are involved to assess the segmentation visually. Both evaluations compare the segmented volumes against the ground truth data

Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits.

Spontaneous and sensory-evoked activity propagates across varying spatial scales in the mammalian cortex, but technical challenges have limited conceptual links between the function of local neuronal circuits and brain-wide network dynamics. We present a method for simultaneous cellular-resolution two-photon calcium imaging of a local microcircuit and mesoscopic widefield calcium imaging of the entire cortical mantle in awake mice. Our multi-scale approach involves a microscope with an orthogonal axis design where the mesoscopic objective is oriented above the brain and the two-photon objective is oriented horizontally, with imaging performed through a microprism. We also introduce a viral transduction method for robust and widespread gene delivery in the mouse brain. These approaches allow us to identify the behavioral state-dependent functional connectivity of pyramidal neurons and vasoactive intestinal peptide-expressing interneurons with long-range cortical networks. Our imaging system provides a powerful strategy for investigating cortical architecture across a wide range of spatial scales

Quantitative MRI correlates of hippocampal and neocortical pathology in intractable temporal lobe epilepsy

Intractable or drug-resistant epilepsy occurs in over 30% of epilepsy patients, with many of these patients undergoing surgical excision of the affected brain region to achieve seizure control. Advances in MRI have the potential to improve surgical treatment of epilepsy through improved identification and delineation of lesions. However, validation is currently needed to investigate histopathological correlates of these new imaging techniques. The purpose of this work is to investigate histopathological correlates of quantitative relaxometry and DTI from hippocampal and neocortical specimens of intractable TLE patients. To achieve this goal I developed and evaluated a pipeline for histology to in-vivo MRI image registration, which finds dense spatial correspondence between both modalities. This protocol was divided in two steps whereby sparsely sectioned histology from temporal lobe specimens was first registered to the intermediate ex-vivo MRI which is then registered to the in-vivo MRI, completing a pipeline for histology to in-vivo MRI registration. When correlating relaxometry and DTI with neuronal density and morphology in the temporal lobe neocortex, I found T1 to be a predictor of neuronal density in the neocortical GM and demonstrated that employing multi-parametric MRI (combining T1 and FA together) provided a significantly better fit than each parameter alone in predicting density of neurons. This work was the first to relate in-vivo T1 and FA values to the proportion of neurons in GM. When investigating these quantitative multimodal parameters with histological features within the hippocampal subfields, I demonstrated that MD correlates with neuronal density and size, and can act as a marker for neuron integrity within the hippocampus. More importantly, this work was the first to highlight the potential of subfield relaxometry and diffusion parameters (mainly T2 and MD) as well as volumetry in predicting the extent of cell loss per subfield pre-operatively, with a precision so far unachievable. These results suggest that high-resolution quantitative MRI sequences could impact clinical practice for pre-operative evaluation and prediction of surgical outcomes of intractable epilepsy

Anisotropic EEG/MEG volume conductor modeling based on Diffusion Tensor Imaging

Die vorliegende Arbeit befasst sich mit der Volumenleitermodellierung auf Basis der Finiten Elemente für EEG/MEG Untersuchungen unter Einbeziehung von Anistropieinformation, die mit Hilfe der Magnetresonanzdiffusionstensorbildgebung (MR-DTI) gewonnen wurde. Im ersten Teil der Arbeit wurde der Einfluss unvollständig bestimmter Wichtungsparamter (b-Matrix) auf die zu rekonstruierenden Diffusionstensoren untersucht. Die Unvollständigkeit bezieht sich dabei auf die Tatsache, dass im Allgemeinen nur die starken Diffusionsgradienten zur Berechnung der b-Matrix herangezogen werden. Es wurde gezeigt, dass besonders bei Aufnahmen mit hoher räumlicher Auflösung der Anteil der Bildgradienten an der b-Matrix nicht mehr vernachlässigbar ist. Weiterhin wurde gezeigt, wie man die b-Matrizen korrekt analytisch bestimmt und damit einen systematischen Fehler vermeidet. Für den Fall, dass nicht ausreichend Informationen zur Verfügung stehen um die analytische Bestimmung durchzuführen, wurde eine Lösung vorgeschlagen, die es mit Hilfe von Phantommessungen ermöglicht eine parametrisierte b-Matrix zu bestimmen. Der zweite Teil widmet sich der Erstellung hochaufgelöster realistischer Volumenleitermodelle detailliert beschrieben. Besonders die Transformation der Diffusionstensordaten in Leitfähigkeitstensoren. Zudem wurde eine Vorgehensweise beschrieben, die es erlaubt, einen T1-gewichteten MR-Datensatz vollautomatisch in fünf verschiedene Gewebesegmente (weiches Gewebe, graue und weiße Substanz, CSF und Schädelknochen) zu unterteilen. Der dritte Teil der Arbeit befasst sich mit dem Einfluss der anisotropen Leitfähigkeit in der weißen Hirnsubstanz auf EEG und MEG unter Verwendung eines Tier- sowie eines Humanmodells. Um den Einfluss der verschiedenen Methoden der Transformation von DTI Daten in Leitfähigkeitsdaten zu untersuchen, wurden verschiedenen Modelle sowohl mit gemessener als auch mit künstlicher Anisotropie erstellt. In der Tiermodellstudie wurden EEG und in der Humanmodellstudie EEG und MEG Simulationen sowohl mit den anisotropen Modellen als auch mit einem isotropen Modell durchgeführt und miteinander verglichen. Dabei wurde gefunden, dass sowohl der topographische Fehler (RDM) als auch der Magnitudenfehler stark durch das Einbeziehen von Anisotropieinformationen beeinflusst wird. Es wurde auch gezeigt, dass sowohl die Position als auch die Orientierung einer dipolaren Quelle in Bezug auf das anisotrope Segment einen großen Effekt auf die untersuchten Fehlermaße hat.In this work anisotropic electric tissue properties determined by means of diffusion tensor imaging were modeled into high resolution finite element volume conductors. In first part of the work the influence of not considering imaging gradient in the calculation of the b-matrices on the correct determination of diffusion tensor data is shown and it was found that especially with high resolution imaging protocols the contributions of the imaging gradients is not negligible. It was also shown how correct b-matrices considering all applied gradients can be calculated correctly. For the case that information about the sequence are missing an experimental approach of determining a parameterized b-matrix using phantom measurements is proposed. In the second part the procedure of generating anisotropic volume conductor models is regarded. The main focus of this part was to facilitate the derivation of anisotropy information from DTI measurements and the inclusion of this information into an anisotropic volume conductor. It was shown, that it is possible to generate a sophisticated high resolution anisotropic model without any manual steps into five different tissue layers. The third part studied the influence of anisotropic white matter employing an animal as well as a human model. To compare the different ways of converting the anisotropy information from DTI into conductivity information, different models were investigated, having artificial as well as measured anisotropy. In the animal study the EEG and in the human study the EEG and MEG forward solution was studies using the anisotropic models and compared to the solution derived using an isotropic model. It was found that both, the topography error (RDM) as well as the magnitude error (MAG), are significantly affected if anisotropy is considered in the volume conductor. It was also shown, that the position as well as the orientation of the dipole with respect to white matter has a large effect on the amount of the error quantities. Finally, it is claimed that if one uses high resolution volume conductor models for EEG/MEG studies, the anisotropy has to be considered, since the average error of neglecting anisotropy is larger than the accuracy which can be achieved using such models

Fuzzy Fibers: Uncertainty in dMRI Tractography

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research