Robust Motion and Distortion Correction of Diffusion-Weighted MR Images

Effective image-based correction of motion and other acquisition artifacts became an essential step in diffusion-weighted Magnetic Resonance Imaging (MRI) analysis as the micro-structural tissue analysis advances towards higher-order models. These come with increasing demands on the number of acquired images and the diffusion strength (b-value) yielding lower signal-to-noise ratios (SNR) and a higher susceptibility to artifacts. These conditions, however, render the current image-based correction schemes, which act retrospectively on the acquired images through pairwise registration, more and more ineffective. Following the hypothesis, that a more consequent exploitation of the different intensity relationships between the volumes would reduce registration outliers, a novel correction scheme based on memetic search is proposed. This scheme allows for incorporating all single image metrics into a multi-objective optimization approach. To allow a quantitative evaluation of registration precision, realistic synthetic data are constructed by extending a diffusion MRI simulation framework by motion and eddy-currents-caused artifacts. The increased robustness and efficacy of the multi-objective registration method is demonstrated on the synthetic as well as in-vivo datasets at different levels of motion and other acquisition artifacts. In contrast to the state-of-the-art methods, the average target registration error (TRE) remained below the single voxel size also at high b-values (3000 s.mm-2) and low signal-to-noise ratio in the moderately artifacted datasets. In the more severely artifacted data, the multi-objective method was able to eliminate most of the registration outliers of the state-of-the-art methods, yielding an average TRE below the double voxel size. In the in-vivo data, the increased precision manifested itself in the scalar measures as well as the fiber orientation derived from the higher-order Neurite Orientation Dispersion and Density Imaging (NODDI) model. For the neuronal fiber tracts reconstructed on the data after correction, the proposed method most closely resembled the ground-truth. The proposed multi-objective method has not only impact on the evaluation of higher-order diffusion models as well as fiber tractography and connectomics, but could also find application to challenging image registration problems in general

Machine learning techniques implementation in power optimization, data processing, and bio-medical applications

The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for demand side management of electric water heaters using Q-learning and action-dependent heuristic dynamic programming. The implemented approaches provide an efficient load management mechanism that reduces the overall power cost and smooths grid load profile. The second paper implements an ensemble statistical and subspace-clustering model for analyzing the heterogeneous data of the autism spectrum disorder. The paper implements a novel k-dimensional algorithm that shows efficiency in handling heterogeneous dataset. The third paper provides a unified learning model for clustering neuroimaging data to identify the potential risk factors for suboptimal brain aging. In the last paper, clustering and clustering validation indices are utilized to identify the groups of compounds that are responsible for plant uptake and contaminant transportation from roots to plants edible parts --Abstract, page iv

Mapping connections in the neonatal brain with magnetic resonance imaging

The neonatal brain undergoes rapid development after birth, including the growth and maturation of the white matter fibre bundles that connect brain regions. Diffusion MRI (dMRI) is a unique tool for mapping these bundles in vivo, providing insight into factors that impact the development of white matter and how its maturation influences other developmental processes. However, most studies of neonatal white matter do not use specialised analysis tools, instead using tools that have been developed for the adult brain. However, the neonatal brain is not simply a small adult brain, as differences in geometry and tissue decomposition cause considerable differences in dMRI contrast. In this thesis, methods are developed to map white matter connections during this early stage of neurodevelopment. First, two contrasting approaches are explored: ROI-constrained protocols for mapping individual tracts, and the generation of whole-brain connectomes that capture the developing brain's full connectivity profile. The impact of the gyral bias, a methodological confound of tractography, is quantified and compared with the equivalent measurements for adult data. These connectomes form the basis for a novel, data-driven framework, in which they are decomposed into white matter bundles and their corresponding grey matter terminations. Independent component analysis and non-negative matrix factorisation are compared for the decomposition, and are evaluated against in-silico simulations. Data-driven components of dMRI tractography data are compared with manual tractography, and networks obtained from resting-state functional MRI. The framework is further developed to provide corresponding components between groups and individuals. The data-driven components are used to generate cortical parcellations, which are stable across subjects. Finally, some future applications are outlined that extend the use of these methods beyond the context of neonatal imaging, in order to bridge the gap between functional and structural analysis paradigms, and to chart the development of white matter throughout the lifespan and across species

Mapping connections in the neonatal brain with magnetic resonance imaging

The neonatal brain undergoes rapid development after birth, including the growth and maturation of the white matter fibre bundles that connect brain regions. Diffusion MRI (dMRI) is a unique tool for mapping these bundles in vivo, providing insight into factors that impact the development of white matter and how its maturation influences other developmental processes. However, most studies of neonatal white matter do not use specialised analysis tools, instead using tools that have been developed for the adult brain. However, the neonatal brain is not simply a small adult brain, as differences in geometry and tissue decomposition cause considerable differences in dMRI contrast. In this thesis, methods are developed to map white matter connections during this early stage of neurodevelopment. First, two contrasting approaches are explored: ROI-constrained protocols for mapping individual tracts, and the generation of whole-brain connectomes that capture the developing brain's full connectivity profile. The impact of the gyral bias, a methodological confound of tractography, is quantified and compared with the equivalent measurements for adult data. These connectomes form the basis for a novel, data-driven framework, in which they are decomposed into white matter bundles and their corresponding grey matter terminations. Independent component analysis and non-negative matrix factorisation are compared for the decomposition, and are evaluated against in-silico simulations. Data-driven components of dMRI tractography data are compared with manual tractography, and networks obtained from resting-state functional MRI. The framework is further developed to provide corresponding components between groups and individuals. The data-driven components are used to generate cortical parcellations, which are stable across subjects. Finally, some future applications are outlined that extend the use of these methods beyond the context of neonatal imaging, in order to bridge the gap between functional and structural analysis paradigms, and to chart the development of white matter throughout the lifespan and across species

Planning for steerable needles in neurosurgery

The increasing adoption of robotic-assisted surgery has opened up the possibility to control innovative dexterous tools to improve patient outcomes in a minimally invasive way. Steerable needles belong to this category, and their potential has been recognised in various surgical fields, including neurosurgery. However, planning for steerable catheters' insertions might appear counterintuitive even for expert clinicians. Strategies and tools to aid the surgeon in selecting a feasible trajectory to follow and methods to assist them intra-operatively during the insertion process are currently of great interest as they could accelerate steerable needles' translation from research to practical use. However, existing computer-assisted planning (CAP) algorithms are often limited in their ability to meet both operational and kinematic constraints in the context of precise neurosurgery, due to its demanding surgical conditions and highly complex environment. The research contributions in this thesis relate to understanding the existing gap in planning curved insertions for steerable needles and implementing intelligent CAP techniques to use in the context of neurosurgery. Among this thesis contributions showcase (i) the development of a pre-operative CAP for precise neurosurgery applications able to generate optimised paths at a safe distance from brain sensitive structures while meeting steerable needles kinematic constraints; (ii) the development of an intra-operative CAP able to adjust the current insertion path with high stability while compensating for online tissue deformation; (iii) the integration of both methods into a commercial user front-end interface (NeuroInspire, Renishaw plc.) tested during a series of user-controlled needle steering animal trials, demonstrating successful targeting performances. (iv) investigating the use of steerable needles in the context of laser interstitial thermal therapy (LiTT) for maesial temporal lobe epilepsy patients and proposing the first LiTT CAP for steerable needles within this context. The thesis concludes with a discussion of these contributions and suggestions for future work.Open Acces

Contributions to the study of Austism Spectrum Brain conectivity

164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines

Journal of Telecommunications and Information Technology

kwartalni

Combined EEG and MEG source analysis of epileptiform activity using calibrated realistic finite element head models

In dieser Arbeit wird eine neue Pipeline, welche die komplementären Informationen der Elektroenzephalographie (EEG) und Magnetoenzephalographie (MEG) berücksichtigen kann, vorgestellt und experimentell sowie methodisch analysiert. Um das Vorwärtsproblem zu lösen, wird ein hochrealistisches Finite-Elemente-Kopfmodell aus individuell gemessenen T1-gewichteten, T2-gewichteten und Diffusion-Tensor (DT)-MRIs generiert. Dafür werden die Kompartments Kopfhaut, spongioser Schädel, kompakter Schädel, Liquor Cerebrospinalis (CSF), graue Substanz und weiße Substanz segmentiert und ein individuelles Kopfmodell erstellt. Um eine sehr akkurate Quellenanalyse zu garantieren werden die individuelle Kopfform, die Anisotropie der weißen Substanz und die individuell kalibrierte Schädelleitfähigkeiten berücksichtigt. Die Anisotropie der weißen Substanz wird anhand der gemessenen DT-MRI Daten berechnet und in das segmentierte Kopfmodell integriert. Da sich die Leitfähigkeit des schwach-leitenden Schädels für verschiedene Probanden sehr stark unterscheidet und diese die Ergebnisse der EEG Quellenanalyse stark beeinflusst, wird ein Fokus auf die Untersuchung der Schädelleitfähigkeit gelegt. Um die individuelle Schädelleitfähigkeit möglichst genau zu bestimmen werden simultan gemessene somatosensorische Potentiale und Felder der Probanden verwendet und ein Verfahren zur Kalibrierung der Schädelleitfähigkeit durchgeführt. Wie in dieser Studie gezeigt, können individuell generierte Kopfmodelle dazu verwendet werden um, in einem nicht-invasivem Verfahren, interiktale Aktivität für Patienten, welche an medikamentenresistenter Epilepsie leiden, mit einer sehr hohen Genauigkeit zu detektieren. Außerdem werden diese akkuraten Kopfmodelle dazu verwendet um die unterschiedlichen Sensitivitäten von EEG, MEG und einer kombinierten EEG und MEG (EMEG) Quellenanalyse in Bezug auf verschiedene Gewebeleitfähigkeiten zu untersuchen. Wie in dieser Studie gezeigt wird liefert eine kombinierte EMEG Quellenanalyse zuverlässigere und robustere Ergebnisse für die Lokalisierung epileptischer Aktivität als eine einfache EEG oder MEG Quellenanalyse. Zuletzt werden die Auswirkungen einer Spikemittelung sowie die Effekte verschiedener Signal-Rausch-Verhältnisse (SNRs) anhand verschiedener Teilmittelungen untersucht. Wie in dieser Arbeit gezeigt wird sind realistische Kopfmodelle mit anisotroper weißer Substanz und kalibrierter Schädelleitfähigkeit nicht nur für die EEG Quellenanalyse, sondern auch für die MEG und EMEG Quellenanalyse vorteilhaft. Durch die Anwendung dieser akkuraten Kopfmodelle konnte gezeigt werden, dass EMEG Quellenanalyse sehr gute Quellenrekonstruktionen auch schon zu Beginn des epileptischen Spikes liefert, wo nur eine sehr geringe SNR vorhanden ist. Da zu diesem Zeitpunkt noch keine Ausbreitung der epileptischen Aktivität eingesetzt hat ist die Lokalisation von frühen Quellen von besonderer Bedeutung. Während die EMEG Quellenanalyse auch Ausbreitungseffekte für spätere Zeitpunkte genau darstellen kann, können einfache EEG oder MEG Quellenanalysen diese nicht oder nur teilweise darstellen. Die Validierung der Ausbreitung wird anhand eines invasiv gemessenen Stereo-EEG durchgeführt. Durch die durchgeführten Spikemittelungen und die SNR Analyse wird verdeutlicht, dass durch eine Teilmittelung wichtige und exakte Informationen über den Mittelpunkt sowie die Größe des epileptischen Gewebes gewonnen werden können, welche weder durch eine einfachen noch einer "Grand-average" Lokalisation des Spikes erreichbar sind. Eine weitere Anwendung einer genauen EMEG Quellenanalyse ist die Bestimmung einer "region of interest" anhand von standardisierten MRT Messungen. Diese kleinen Gebiete werden dann später mit einer optimalen und höher aufgelösten MRT-Sequenz gemessen. Dank dieses optimierte Verfahren können auch sehr kleine FCDs entdeckt werden, welche auf dem standardisierten gemessenen MRT-Sequenzen nicht erkennbar sind. Die Pipeline, welche in dieser Arbeit entwickelt wird, kann auch für gesunde Probanden angewendet werden. In einer ersten Studie wird eine Quellenanalyse der somatosensorischen und auditorisch-induzierten Reize durchgeführt. Die gewonnen Daten werden mit anderen Studien vergleichen und mögliche Gemeinsamkeiten diskutiert. Eine weitere Anwendung der realistischen Kopfmodelle ist die Untersuchung von Volumenleitungseffekten in nicht-invasiven Hirnstimulationsmethoden wie transkranielle Gleichstromstimulation und transkranielle Magnetstromstimulation.In this thesis, a new experimental and methodological analysis pipeline for combining the complementary information contained in electroencephalography (EEG) and magnetoencephalography (MEG) is introduced. The forward problem is solved using high resolution finite element head models that are constructed from individual T1 weighted, T2 weighted and diffusion tensor (DT-) MRIs. For this purpose, scalp, skull spongiosa, skull compacta, cerebrospinal fluid, white matter (WM) and gray matter (GM) are segmented and included into the head models. In order to obtain highly accurate source reconstructions, the realistic geometry, tissue conductivity anisotropy (i.e., WM tracts) and individually estimated conductivity values are taken into account. To achieve this goal, the brain anisotropy is modeled using the information obtained from DT-MRI. A main focus is placed on the skull conductivity due to its high inter-individual variance and different sensitivities of EEG and MEG source reconstructions to it. In order to estimate individual skull conductivity values that fit best to the constructed head models, simultaneously acquired somatosensory evoked potential and field data measured for the same individuals are analyzed. As shown in this work, the constructed head models could be used to non-invasively localize interictal spike activity in patients suffering from pharmaco-resistant focal epilepsy with higher reliability. In addition, by using these advanced head models, tissue sensitivities of EEG, MEG and combined EEG/MEG (EMEG) are compared by means of altering the distinguished tissue types and their conductivities. Finally, the effects of spike averaging and signal-to-noise-ratios (SNRs) on source analysis are evaluated by localizing subaverages. The results obtained in this thesis demonstrate the importance of using anisotropic and skull conductivity calibrated realistic finite element models not only for EEG but also for MEG and EMEG source analysis. By employing such advanced finite element models, it is possible to demonstrate that EMEG achieves accurate source reconstructions at early instants in time (epileptic spike onset), i.e., time points with low SNR, which are not yet subject to propagation and thus supposed to be closer to the origin of the epileptic activity. It is also shown that EMEG is able to reveal the propagation pathway at later time points in agreement with invasive stereo-EEG, while EEG or MEG alone reconstruct only parts of it. Spike averaging and SNR analysis reveal that subaveraging provides important and accurate information about both the center of gravity and the extent of the epileptogenic tissue that neither single nor grand-averaged spike localizations could supply. Moreover, it is shown that accurate source reconstructions obtained with EMEG can be used to determine a region of interest, and new MRI sequences that acquire high resolution images in this restricted area can detect FCDs that were not detectable with other MRI sequences. The pipelines proposed in this work are also tested for source analysis of somatosensory and auditory evoked responses measured from healthy subjects and the results are compared with the literature. In addition, the finite element head models are also used to assess the volume conductor effects on simulations of non-invasive brain stimulation techniques such as transcranial direct current and transcranial magnetic stimulation