Deconvolution‐based distortion correction of EPI using analytic single‐voxel point‐spread functions

Purpose To develop a postprocessing algorithm that corrects geometric distortions due to spatial variations of the static magnetic field amplitude, B0, and effects from relaxation during signal acquisition in EPI. Theory and Methods An analytic, complex point‐spread function is deduced for k‐space trajectories of EPI variants and applied to corresponding acquisitions in a resolution phantom and in human volunteers at 3 T. With the analytic point‐spread function and experimental maps of B0 (and, optionally, the effective transverse relaxation time, urn:x-wiley:07403194:media:mrm28591:mrm28591-math-0004) as input, a point‐spread function matrix operator is devised for distortion correction by a Thikonov‐regularized deconvolution in image space. The point‐spread function operator provides additional information for an appropriate correction of the signal intensity distribution. A previous image combination algorithm for acquisitions with opposite phase blip polarities is adapted to the proposed method to recover destructively interfering signal contributions. Results Applications of the proposed deconvolution‐based distortion correction (“DecoDisCo”) algorithm demonstrate excellent distortion corrections and superior performance regarding the recovery of an undistorted intensity distribution in comparison to a multifrequency reconstruction. Examples include full and partial Fourier standard EPI scans as well as double‐shot center‐out trajectories. Compared with other distortion‐correction approaches, DecoDisCo permits additional deblurring to obtain sharper images in cases of significant urn:x-wiley:07403194:media:mrm28591:mrm28591-math-0005 effects. Conclusion Robust distortion corrections in EPI acquisitions are feasible with high quality by regularized deconvolution with an analytic point‐spread function. The general algorithm, which is publicly released on GitHub, can be straightforwardly adapted for specific EPI variants or other acquisition schemes

Quantitative diffusion MRI with application to multiple sclerosis

Diffusion MRI (dMRI) is a uniquely non-invasive probe of biological tissue properties, increasingly able to provide access to ever more intricate structural and microstructural tissue information. Imaging biomarkers that reveal pathological alterations can help advance our knowledge of complex neurological disorders such as multiple sclerosis (MS), but depend on both high quality image data and robust post-processing pipelines. The overarching aim of this thesis was to develop methods to improve the characterisation of brain tissue structure and microstructure using dMRI. Two distinct avenues were explored. In the first approach, network science and graph theory were used to identify core human brain networks with improved sensitivity to subtle pathological damage. A novel consensus subnetwork was derived using graph partitioning techniques to select nodes based on independent measures of centrality, and was better able to explain cognitive impairment in relapsing-remitting MS patients than either full brain or default mode networks. The influence of edge weighting scheme on graph characteristics was explored in a separate study, which contributes to the connectomics field by demonstrating how study outcomes can be affected by an aspect of network design often overlooked. The second avenue investigated the influence of image artefacts and noise on the accuracy and precision of microstructural tissue parameters. Correction methods for the echo planar imaging (EPI) Nyquist ghost artefact were systematically evaluated for the first time in high b-value dMRI, and the outcomes were used to develop a new 2D phase-corrected reconstruction framework with simultaneous channel-wise noise reduction appropriate for dMRI. The technique was demonstrated to alleviate biases associated with Nyquist ghosting and image noise in dMRI biomarkers, but has broader applications in other imaging protocols that utilise the EPI readout. I truly hope the research in this thesis will influence and inspire future work in the wider MR community

Neural Representations of Visual Motion Processing in the Human Brain Using Laminar Imaging at 9.4 Tesla

During natural behavior, much of the motion signal falling into our eyes is due to our own movements. Therefore, in order to correctly perceive motion in our environment, it is important to parse visual motion signals into those caused by self-motion such as eye- or head-movements and those caused by external motion. Neural mechanisms underlying this task, which are also required to allow for a stable perception of the world during pursuit eye movements, are not fully understood. Both, perceptual stability as well as perception of real-world (i.e. objective) motion are the product of integration between motion signals on the retina and efference copies of eye movements. The central aim of this thesis is to examine whether different levels of cortical depth or distinct columnar structures of visual motion regions are differentially involved in disentangling signals related to self-motion, objective, or object motion. Based on previous studies reporting segregated populations of voxels in high level visual areas such as V3A, V6, and MST responding predominantly to either retinal or extra- retinal (‘real’) motion, we speculated such voxels to reside within laminar or columnar functional units. We used ultra-high field (9.4T) fMRI along with an experimental paradigm that independently manipulated retinal and extra-retinal motion signals (smooth pursuit) while controlling for effects of eye-movements, to investigate whether processing of real world motion in human V5/MT, putative MST (pMST), and V1 is associated to differential laminar signal intensities. We also examined motion integration across cortical depths in human motion areas V3A and V6 that have strong objective motion responses. We found a unique, condition specific laminar profile in human area V6, showing reduced mid-layer responses for retinal motion only, suggestive of an inhibitory retinal contribution to motion integration in mid layers or alternatively an excitatory contribution in deep and superficial layers. We also found evidence indicating that in V5/MT and pMST, processing related to retinal, objective, and pursuit motion are either integrated or colocalized at the scale of our resolution. In contrast, in V1, independent functional processes seem to be driving the response to retinal and objective motion on the one hand, and to pursuit signals on the other. The lack of differential signals across depth in these regions suggests either that a columnar rather than laminar segregation governs these functions in these areas, or that the methods used were unable to detect differential neural laminar processing. Furthermore, the thesis provides a thorough analysis of the relevant technical modalities used for data acquisition and data analysis at ultra-high field in the context of laminar fMRI. Relying on our technical implementations we were able to conduct two high-resolution fMRI experiments that helped us to further investigate the laminar organization of self-induced and externally induced motion cues in human high-level visual areas and to form speculations about the site and the mechanisms of their integration

Diffusion and perfusion MRI and applications in cerebral ischaemia

Two MRI techniques, namely diffusion and perfusion imaging, are becoming increasingly used for evaluation of the pathophysiology of stroke. This work describes the use of these techniques, together with more conventional MRI modalities (such as T1, and T2 imaging) in the investigation of cerebral ischaemia. The work was performed both in a paediatric population in a whole-body clinical MR system (1.5 T) and in an animal model of focal ischaemia at high magnetic field strength (8.5 T). For the paediatric studies, a single shot echo planar imaging (EPI) sequence was developed to enable the on-line calculation of maps of the trace of the diffusion tensor. In the process of this development, it was necessary to address two different imaging artefacts in these maps: eddy current induced image shifts, and residual Nyquist ghost artefacts. Perfusion imaging was implemented using an EPI sequence to follow the passage through the brain of a bolus of a paramagnetic contrast agent. Computer simulations were performed to evaluate the limitations of this technique in the quantification of cerebral blood flow when delay in the arrival and dispersion of the bolus of contrast agent are not accounted for. These MRI techniques were applied to paediatric patients to identify acute ischaemic events, as well as to differentiate between multiple acute events, or between acute and chronic events. Furthermore, the diffusion and perfusion findings were shown to contribute significantly to the management of patients with high risk of stroke, and in the evaluation of treatment outcome. In the animal experiments, permanent middle cerebral artery occlusion was performed in rats to investigate longitudinally the acute MRI changes (first 4-6 hours) following an ischaemic event. This longitudinal analysis contributed to the understanding of the evolution of the ischaemic lesion. Furthermore, the findings allowed the acute identification of tissue 'at risk' of infarction

Using high angular resolution diffusion imaging data to discriminate cortical regions

Brodmann's 100-year-old summary map has been widely used for cortical localization in neuroscience. There is a pressing need to update this map using non-invasive, high-resolution and reproducible data, in a way that captures individual variability. We demonstrate here that standard HARDI data has sufficiently diverse directional variation among grey matter regions to inform parcellation into distinct functional regions, and that this variation is reproducible across scans. This characterization of the signal variation as non-random and reproducible is the critical condition for successful cortical parcellation using HARDI data. This paper is a first step towards an individual cortex-wide map of grey matter microstructure, The gray/white matter and pial boundaries were identified on the high-resolution structural MRI images. Two HARDI data sets were collected from each individual and aligned with the corresponding structural image. At each vertex point on the surface tessellation, the diffusion-weighted signal was extracted from each image in the HARDI data set at a point, half way between gray/white matter and pial boundaries. We then derived several features of the HARDI profile with respect to the local cortical normal direction, as well as several fully orientationally invariant features. These features were taken as a fingerprint of the underlying grey matter tissue, and used to distinguish separate cortical areas. A support-vector machine classifier, trained on three distinct areas in repeat 1 achieved 80-82% correct classification of the same three areas in the unseen data from repeat 2 in three volunteers. Though gray matter anisotropy has been mostly overlooked hitherto, this approach may eventually form the foundation of a new cortical parcellation method in living humans. Our approach allows for further studies on the consistency of HARDI based parcellation across subjects and comparison with independent microstructural measures such as ex-vivo histology

Advances in image acquisition and filtering for MRI neuroimaging at 7 tesla

Performing magnetic resonance imaging at high magnetic field strength promises many improvements over low fields that are of direct benefit in functional neuroimaging. This includes the possibility of improved signal-to-noise levels, and increased BOLD functional contrast and spatial specificity. However, human MRI at 7T and above suffers from unique engineering challenges that limit the achievable gains. In this thesis, three technological developments are introduced, all of which address separate issues associated with functional magnetic resonance neuroimaging at very high magnetic field strengths. First, the image homogeneity problem is addressed by investigating methods of RF shimming — modifying the excitation portion of the MRI experiment for use with multi-channel RF coils. It is demonstrated that in 2D MRI experiments, shimming on a slice-by slice basis allows utilization of an extra degree of freedom available from the slice dimension, resulting in significant gains in image homogeneity and reduced RF power requirements. After acceptable images are available, we move to address complications of high field imaging that manifest in the fMRI time series. In the second paper, the increased physiological noise present in BOLD time series at high field is addressed with a unique data-driven noise regressor scheme based upon information in the phase component of the MRI signal. It is demonstrated that this method identifies and removes a significant portion of physiological signals, and performs as good or better than other popular data driven methods that use only the magnitude signal information. Lastly, the BOLD phase signal is again leveraged to address the confounding role of veins in resting state BOLD fMRI experiments. The phase regressor technique (previously developed by Dr. Menon) is modified and applied to resting state fMRI to remove macro vascular contributions in the datasets, leading to changes in spatial extent and connectivity of common resting state networks on single subjects and at the group level

Quantitative MRI and machine learning for the diagnosis and prognosis of Multiple Sclerosis

Multiple sclerosis (MS) is an immune-mediated, inflammatory, neurological disease affecting myelin in the central nervous system, whose driving mechanisms are not yet fully understood. Conventional magnetic resonance imaging (MRI) is largely used in the MS diagnostic process, but because of its lack of specificity, it cannot reliably detect microscopic damage. Quantitative MRI provides instead feature maps that can be exploited to improve prognosis and treatment monitoring, at the cost of prolonged acquisition times and specialised MR-protocols. In this study, two converging approaches were followed to investigate how to best use the available MRI data for the diagnosis and prognosis of MS. On one hand, qualitative data commonly used in clinical research for lesion and anatomical purposes were shown to carry quantitative information that could be used to conduct myelin and relaxometry analyses on cohorts devoid of dedicated quantitative acquisitions. In this study arm, named bottom-up, qualitative information was up-converted to quantitative surrogate: traditional model-fitting and deep-learning frameworks were proposed and tested on MS patients to extract relaxometry and indirect-myelin quantitative data from qualitative scans. On the other hand, when using multi-modal MRI data to classify MS patients with different clinical status, different MR-features contribute to specific classification tasks. The top-down study arm consisted in using machine learning to reduce the multi-modal dataset dimensionality only to those MR-features that are more likely to be biophysically meaningful with respect to each MS phenotype pathophysiology. Results show that there is much more potential to qualitative data than lesion and tissue segmentation, and that specific MRI modalities might be better suited for investigating individual MS phenotypes. Efficient multi-modal acquisitions informed by biophysical findings, whilst being able to extract quantitative information from qualitative data, would provide huge statistical power through the use of large, historical datasets, as well as constitute a significant step forward in the direction of sustainable research

Quantitative MRI for measuring myelin content in human spinal cord

The aim of this thesis is to progress the state-of-the art of quantitative Magnetic Resonance Imaging (MRI) in the human spinal cord, with particular focus on methods sensitive to myelin content. Myelin is a fundamental structure of the central nervous system, ensuring the correct transmission of action potentials along neuronal axons, affected in a number of neurological disorders, first and foremost Multiple Sclerosis (MS). MRI methods to assess myelin in the spinal cord have found limited development, despite the primary involvement of the spinal cord in demyelinating diseases, such as MS where the characterization of spinal cord pathology is key for a better diagnosis, understanding of pathological processes, and evaluation of neuroprotective and reparative treatments. In this thesis, we develop novel methods for the spinal cord to measure parameters that are known to correlate with myelin content, namely the longitudinal relaxation time (T₁) and quantitative Magnetization Transfer (qMT) parameters, and we compare them with a large set of myelin sensitive MRI indices in the post mortem MS spinal cord. The thesis is structured as follows: chapter 1 states the problem this thesis attempts to address and provides background information regarding the involvement of the spinal cord in MS; chapter 2 reviews the basic principles of MRI and introduces the theory behind the measurement of surrogate indices of myelin content with MRI; chapter 3 reviews an existing imaging sequence for the spinal cord, extends its use for measuring myelin sensitive parameters and discusses potential improvements for in vivo applications; chapter 4 and chapter 5 propose novel efficient methods to measure T₁ and qMT parameters in vivo in the spinal cord; and chapter 6 evaluates the performance of the methods developed in the previous chapter, together with other prospective myelin mapping methods, in the healthy and MS post mortem human spinal cord