Analysis and Strategies to Enhance Intensity-Base Image Registration.

The availability of numerous complementary imaging modalities allows us to obtain a detailed picture of the body and its functioning. To aid diagnostics and surgical planning, all available information can be presented by visually aligning images from different modalities using image registration. This dissertation investigates strategies to improve the performance of image registration algorithms that use intensity-based similarity metrics. Nonrigid warp estimation using intensity-based registration can be very time consuming. We develop a novel framework based on importance sampling and stochastic approximation techniques to accelerate nonrigid registration methods while preserving their accuracy. Registration results for simulated brain MRI data and human lung CT data demonstrate the efficacy of the proposed framework. Functional MRI (fMRI) is used to non-invasively detect brain-activation by acquiring a series of brain images, called a time-series, while the subject performs tasks designed to stimulate parts of the brain. Consequently, these studies are plagued by subject head motion. Mutual information (MI) based slice-to-volume (SV) registration algorithms used to estimate time-series motion are less accurate for end-slices (i.e., slices near the top of the head scans), where a loss in image complexity yields noisy MI estimates. We present a strategy, dubbed SV-JP, to improve SV registration accuracy for time-series end-slices by using joint pdf priors derived from successfully registered high complexity slices near the middle of the head scans to bolster noisy MI estimates. Although fMRI time-series registration can estimate head motion, this motion also spawns extraneous intensity fluctuations called spin saturation artifacts. These artifacts hamper brain-activation detection. We describe spin saturation using mathematical expressions and develop a weighted-average spin saturation (WASS) correction scheme. An algorithm to identify time-series voxels affected by spin saturation and to implement WASS correction is outlined. The performance of registration methods is dependant on the tuning parameters used to implement their similarity metrics. To facilitate finding optimal tuning parameters, we develop a computationally efficient linear approximation of the (co)variance of MI-based registration estimates. However, empirically, our approximation was satisfactory only for a simple mono-modality registration example and broke down for realistic multi-modality registration where the MI metric becomes strongly nonlinear.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61552/1/rbhagali_1.pd

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

In this PhD Thesis proposal, the principles of diffusion MRI (dMRI) in its application to the human brain mapping of connectivity are reviewed. The background section covers the fundamentals of dMRI, with special focus on those related to the distortions caused by susceptibility inhomogeneity across tissues. Also, a deep survey of available correction methodologies for this common artifact of dMRI is presented. Two methodological approaches to improved correction are introduced. Finally, the PhD proposal describes its objectives, the research plan, and the necessary resources

Magnetic resonance imaging of brain tissue abnormalities: transverse relaxation time in autism and Tourette syndrome and development of a novel whole-brain myelin mapping technique

The transverse relaxation time (T2) is a fundamental parameter of magnetic resonance imaging sensitive to tissue microstructure and water content, thus offering a non-invasive approach to evaluate abnormalities of brain tissue in-vivo. Prevailing hypotheses of two childhood psychiatric disorders were tested using quantitative T2 imaging and automated region of interest (ROI) analyses. In autism, the under-connectivity theory, which proposes aberrant connectivity within white matter (WM) was assessed, finding T2 to be eleveted in the frontal and parietal lobes, while dividing whole brain data into neurodevelopmentally relevant WM ROIs found increased T2 in bridging and radiate WM. In Tourette syndrome, tissue abnormalities of deep gray matter structures implicated in the symptomology of this disorder were evaluated and increased T2 of the caudate was found. Despite the sensitivity of quantitative T2 measurements to underlying pathophysiology, interpretation remain difficult. However, in WM, the compartmentalization of distinct water environments may lead to the detection of multi-exponential T2 decay. The metric of interest is principally the myelin water fraction (MWF), which is the proportion of the MRI signal arising from water trapped within layers of the myelin sheath. As a proof of concept study, the ability to measure the MWF based on T2* decay was evaluated and compared to a MWF measurements obtained from T2 decay. Data were analysed using both non-negative least squares and a two-pool model. Signal losses near sources of magnetic field inhomogeneity, such as the sinuses, rendered T2* components inseparable, invalidating this approach for whole brain MWF measurements. However, this study demonstrated the suitability of a two-pool model to calculate the MWF in WM. A novel approach, based on the multi-component gradient echo sampling of spin echoes (mcGESSE) and a two-pool model of WM, is proposed and its feasibility demonstrated using simulations. The in-vivo implementation of mcGESSE followed, with reproducibility of MWF measurements being assessed and the potential of an accelerated protocol using parallel imaging being investigated. While further work is needed to assess data quality, this approach shows great potential to obtain whole brain MWF data within a clinically relevant scan time

Towards efficient neurosurgery: Image analysis for interventional MRI

Interventional magnetic resonance imaging (iMRI) is being increasingly used for performing imageguided neurosurgical procedures. Intermittent imaging through iMRI can help a neurosurgeon visualise the target and eloquent brain areas during neurosurgery and lead to better patient outcome. MRI plays an important role in planning and performing neurosurgical procedures because it can provide highresolution anatomical images that can be used to discriminate between healthy and diseased tissue, as well as identify location and extent of functional areas. This is of significant clinical utility as it helps the surgeons maximise target resection and avoid damage to functionally important brain areas. There is clinical interest in propagating the pre-operative surgical information to the intra-operative image space as this allows the surgeons to utilise the pre-operatively generated surgical plans during surgery. The current state of the art neuronavigation systems achieve this by performing rigid registration of pre-operative and intra-operative images. As the brain undergoes non-linear deformations after craniotomy (brain shift), the rigidly registered pre-operative images do not accurately align anymore with the intra-operative images acquired during surgery. This limits the accuracy of these neuronavigation systems and hampers the surgeon’s ability to perform more aggressive interventions. In addition, intra-operative images are typically of lower quality with susceptibility artefacts inducing severe geometric and intensity distortions around areas of resection in echo planar MRI images, significantly reducing their utility in the intraoperative setting. This thesis focuses on development of novel methods for an image processing workflow that aims to maximise the utility of iMRI in neurosurgery. I present a fast, non-rigid registration algorithm that can leverage information from both structural and diffusion weighted MRI images to localise target lesions and a critical white matter tract, the optic radiation, during surgical management of temporal lobe epilepsy. A novel method for correcting susceptibility artefacts in echo planar MRI images is also developed, which combines fieldmap and image registration based correction techniques. The work developed in this thesis has been validated and successfully integrated into the surgical workflow at the National Hospital for Neurology and Neurosurgery in London and is being clinically used to inform surgical decisions

Exploring the developing preterm brain with diffusion tensor magnetic resonance imaging

Imperial Users onl

Improving Accuracy of Information Extraction from Quantitative Magnetic Resonance Imaging

Quantitative MRI offers the possibility to produce objective measurements of tissue physiology at different scales. Such measurements are highly valuable in applications such as drug development, treatment monitoring or early diagnosis of cancer. From microstructural information in diffusion weighted imaging (DWI) or local perfusion and permeability in dynamic contrast (DCE-) MRI to more macroscopic observations of the local intestinal contraction, a number of aspects of quantitative MRI are considered in this thesis. The main objective of the presented work is to provide pre-processing techniques and model modification in order to improve the reliability of image analysis in quantitative MRI. Firstly, the challenge of clinical DWI signal modelling is investigated to overcome the biasing effect due to noise in the data. Several methods with increasing level of complexity are applied to simulations and a series of clinical datasets. Secondly, a novel Robust Data Decomposition Registration technique is introduced to tackle the problem of image registration in DCE-MRI. The technique allows the separation of tissue enhancement from motion effects so that the latter can be corrected independently. It is successfully applied to DCE-MRI datasets of different organs. This application is extended to the correction of respiratory motion in small bowel motility quantification in dynamic MRI data acquired during free breathing. Finally, a new local model for the arterial input function (AIF) is proposed. The estimation of the arterial blood contrast agent concentration in DCE-MRI is augmented using prior knowledge on local tissue structure from DWI. This work explores several types of imaging using MRI. It contributes to clinical quantitative MRI analysis providing practical solutions aimed at improving the accuracy and consistency of the parameters derived from image data

Magnetic resonance imaging of muscle structure and function

Ziel dieser Arbeit ist die Implementierung und kombinierte Anwendung verschiedener MRT Techniken zur Untersuchung der Struktur und Funktion der humanen Skelettmuskulatur. Insbesondere steht deren Applikation an der Rückenmuskulatur im Vordergrund, um auf Basis dieser Untersuchungen einen Beitrag zur Ursachenforschung des unspezifischen - meist chronifizierten - Rückenschmerzes zu leisten. Vor diesem Hintergrund wurden in der vorliegenden Dissertation dezidierte MR-Pulssequenzen und Bildrekonstruktionsverfahren entwickelt, welche unter Verwendung der diffusionsgewichteten MR-Bildgebung (diffusion-weighted imaging, DWI) die 3D-Rekonstruktion der Muskelfaserarchitektur sowie die Quantifizierung der muskulären Vaskularität ermöglichen. Die Erfassung der Faserarchitektur basiert auf der Diffusionstensorbildgebung (diffusion tensor imaging, DTI) - einer Weiterentwicklung der DWI - und wurde am Tiermodell anhand sequentiell durchgeführter in vivo und post mortem Messungen validiert. Anschließend wurde diese Methode in einer Pilotstudie genutzt, um degenerative Veränderungen bei Patienten nach Wirbelsäulenoperation zu erfassen. Im zweiten Schritt dieser Arbeit, wurde ein Messprotokoll zur funktionellen MR-Untersuchung implementiert, welches Messungen vor, während und nach willkürlicher Muskelkontraktion beinhaltet. Dieses Protokoll sieht weiterhin die Applikation einer neuartigen perfusionsensitiven DWI-Sequenz sowie optimierten Sequenzen zur quantitativen T2-gewichteten MR-Bildgebung und ortaufgelösten 31P-MR-Spektroskopie vor, wobei die beiden letztgenannten Techniken es erlauben, komplexe funktionelle Vorgänge, wie beispielsweise die des Energiemetabolismus, unter Einfluss einer Belastungssituation zu untersuchen. Diese funktionellen MR-Messprinzipien werden in der Regel unter dem Begriff muscle functional MRI (mfMRI) subsumiert und ermöglichen die multi-parametrische Erfassung unterschiedlicher funktioneller und struktureller Eigenschaften der Muskelphysiologie. Dies wird in der vorliegenden Arbeit anhand einer gerontologischen Studie demonstriert, wobei die hierbei gewonnenen Ergebnisse Einblick in zahlreiche altersassoziierte Aspekte der Rückenphysiologie geben. Zusammenfassend werden in dieser Dissertation verschiedene Ansätze der MR-Bildgebung und MR-Spektroskopie vorgestellt, die einerseits für grundlagenwissenschaftliche Fragestellungen zum unspezifischen Rückenschmerz, andererseits aber auch in der klinischen Routine zur Untersuchung degenerativer Veränderungen der Skelettmuskulatur herangezogen werden können.The aim of this work is the implementation and combined application of different MRI-based methods, which facilitate the comprehensive assessment of the skeletal muscle structure and function. Especially, the application of these MRI techniques to human back muscles has been put into focus, which may provide deeper insights into the origin of non-specific - and in most cases chronic - back pain. To this end, dedicated MRI sequences and quantitative image reconstruction approaches were developed in this work, which are based on diffusion-weighted imaging (DWI) and enable the 3D reconstruction of the muscle fiber architecture as well as the assessment of a surrogate measure of the vascular capacity. The MRI-based reconstruction of the fiber architecture relies on diffusion tensor imaging (DTI) - an extension of DWI - and was validated by successive in vivo and post mortem measurements. Afterwards, this method was employed in a pre-clinical pilot study in order to assess surgeryrelated degenerative changes of the back muscles in patients after spinal surgery. In the second step of this work, functional measurements of the human skeletal muscles, which means quantitative measurements prior to, during and after muscular loading, were performed by using a novel perfusion-sensitive DWI sequence as well as optimized sequence protocols of quantitative T2-weighted MRI and spatially resolved 31P-MR spectroscopy. The latter two methods allow the quantification of complex physiological processes, such as of the high-energy metabolism, during the exercise of skeletal muscles, and are often subsumed under the term muscle functional MRI (mfMRI). Combined application of these mfMRI techniques provides multi-parametric evaluation of several structural and functional determinants and, thus, allows comprehensive characterization of the muscle physiology. In order to demonstrate the capabilities of the proposed multi-parametric mfMRI approach, a gerontological study is performed in this work, while the obtained results indicate several age-related aspects of the human back muscle physiology. Overall, this thesis introduces MR imaging and MR spectroscopy techniques, which may, on the one hand, contribute to research of low back pain and, on the other hand, serve as basis of clinical investigations in order to investigate degenerative processes of skeletal muscles

Diffusion tensor imaging and resting state functional connectivity as advanced imaging biomarkers of outcome in infants with hypoxic-ischaemic encephalopathy treated with hypothermia

Therapeutic hypothermia confers significant benefit in term neonates with hypoxic-ischaemic encephalopathy (HIE). However, despite the treatment nearly half of the infants develop an unfavourable outcome. Intensive bench-based and early phase clinical research is focused on identifying treatments that augment hypothermic neuroprotection. Qualified biomarkers are required to test these promising therapies efficiently. This thesis aims to assess advanced magnetic resonance imaging (MRI) techniques, including diffusion tensor imaging (DTI) and resting state functional MRI (fMRI) as imaging biomarkers of outcome in infants with HIE who underwent hypothermic neuroprotection. FA values in the white matter (WM), obtained in the neonatal period and assessed by tract-based spatial statistics (TBSS), correlated with subsequent developmental quotient (DQ). However, TBSS is not suitable to study grey matter (GM), which is the primary site of injury following an acute hypoxic-ischaemic event. Therefore, a neonatal atlas-based automated tissue labelling approach was applied to segment central and cortical grey and whole brain WM. Mean diffusivity (MD) in GM structures, obtained in the neonatal period correlated with subsequent DQ. Although the central GM is the primary site of injury on conventional MRI following HIE; FA within WM tissue labels also correlated to neurodevelopmental performance scores. As DTI does not provide information on functional consequences of brain injury functional sequel of HIE was studied with resting state fMRI. Diminished functional connectivity was demonstrated in infants who suffered HIE, which associated with an unfavourable outcome. The results of this thesis suggest that MD in GM tissue labels and FA either determined within WM tissue labels or analysed with TBSS correlate to subsequent neurodevelopmental performance scores in infants who suffered HIE treated with hypothermia and may be applied as imaging biomarkers of outcome in this population. Although functional connectivity was diminished in infants with HIE, resting state fMRI needs further study to assess its utility as an imaging biomarker following a hypoxic-ischaemic brain injury.Open Acces

Development of an optimised non-invasive MRI method to measure renal perfusion in patients with impaired renal function

Arterial Spin Labelling (ASL) is a unique Magnetic Resonance Imaging (MRI) approach for quantifying tissue perfusion non-invasively. More than two decades of technical developments established ASL as a valuable tool in neuroimaging, having more recently began its translation to the clinic. ASL holds great potential for the assessment of kidney disease given that it does not require contrast agents which are typically contraindicated for patients with impaired renal function. However, renal ASL applications remain limited and the technique has yet to be incorporated into clinical practice. The sensitivity of ASL to patient movement, which severely corrupts the renal perfusion estimates, is arguably one of the greatest factors hindering a wide adoption of this technique. This thesis begins with an overview of the main concepts addressed in this work (kidney physiology, MRI and ASL) and a thorough literature review of previous renal ASL work. The problem of patient movement is then addressed at all levels of the ASL framework by combining a motion-insensitive ASL acquisition scheme with a specifically tailored image processing pipeline. The feasibility of this technique to provide repeatable renal perfusion measurements is demonstrated in the first paediatric cohort with impaired renal function to undergo renal ASL. Finally, the critical findings of this thesis are summarised and prospective future research directions are outlined