Optic radiation tractography and vision in anterior temporal lobe resection.

Anterior temporal lobe resection (ATLR) is an effective treatment for refractory temporal lobe epilepsy but may result in a contralateral superior visual field deficit (VFD) that precludes driving in the seizure-free patient. Diffusion tensor imaging (DTI) tractography can delineate the optic radiation preoperatively and stratify risk. It would be advantageous to incorporate display of tracts into interventional magnetic resonance imaging (MRI) to guide surgery

Parametric spherical deconvolution: Inferring anatomical connectivity using diffusion MR imaging

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Linking climate history and ice crystalline fabric evolution in polar ice sheets

Thesis (Ph.D.) University of Alaska Fairbanks, 2015An ice sheet consists of an unfathomable number of ice crystallites (grains) that typically have a preferred orientation of the crystalline lattices, termed fabric. At the surface of ice sheets, the microstructural processes that control the grain structure and fabric evolution are influenced by climate variables. Layers of firn, in different climate regimes, may have an observable variation in fabric which can persist deep into the ice sheet; fabric may have 'memory' of these past climate regimes. To model the evolution of a subtle variation in fabric below the firn-ice transition, we have developed and released an open-source Fabric Evolution with Recrystallization (FEvoR) model. FEvoR is an anisotropic stress model that distributes stresses through explicit nearest-neighbor interaction. The model includes parameterizations of grain growth, rotation recrystallization and migration recrystallization which account for the major recrystallization processes that affect the macroscopic grain structure and fabric evolution. Using this model, we explore the evolution of a subtle variation in near-surface fabric using both constant applied stress and a stress-temperature history based on data from Taylor Dome, East Antarctica. Our results show that a subtle fabric variation will be preserved for ~200ka in compressive stress regimes with temperatures typical of polar ice-sheets. The addition of shear to compressive stress regimes preserves fabric variations longer than in compression-only regimes because shear drives a positive feedback between crystal rotation and deformation. We find that temperature affects how long the fabric variation is preserved, but does not affect the strain-integrated fabric evolution profile except when crossing the thermal-activation-energy threshold (~-10°C). Even at high temperatures, migration recrystallization does not rid the fabric of its memory under most conditions. High levels of nearest-neighbor interactions between grains will rid the fabric of its memory more quickly than low levels of nearest-neighbor interactions. Because FEvoR does not compute flow, an integrated fabric-flow model is needed to investigate the flow-fabric feedbacks that arise in ice sheets. Using the open-source Parallel Ice Sheet Model (PISM) and FEvoR, we develop a combined flow-fabric model (PISM-FEvoR). We provide the first integrated flow-fabric model that explicitly computes the fabric evolution and includes all three major recrystallization processes. We show that PISM-FEvoR is able to capture the flow enhancement due to fabric by modeling a slab-on-slope glacier, initialized with a variety of fabric profiles. We also show that the entire integrated fabric-flow history affects the final simulated flow. This provides a further, independent validation of using an integrated fabric-flow model over a constant enhancement factor in ice-sheet models

New tractography methods based on parametric models of white matter fibre dispersion

Diffusion weighted magnetic resonance imaging (DW-MRI) is a powerful imaging technique that can probe the complex structure of the body, revealing structural trends which exist at scales far below the voxel resolution. Tractography utilises the information derived from DW-MRI to examine the structure of white matter. Using information derived from DW-MRI, tractography can estimate connectivity between distinct, functional cortical and sub-cortical regions of grey matter. Understanding how seperate functional regions of the brain are connected as part of a network is key to understanding how the brain works. Tractography has been used to deliniate many known white matter structures and has also revealed structures not fully understood from anatomy due to limitations of histological examination. However, there still remain many shortcomings of tractography, many anatomical features for which tractography algorithms are known to fail, which leads to discrepancies between known anatomy and tractography results. With the aim of approaching a complete picture of the human connectome via tractography, we seek to address the shortcomings in current tractography techniques by exploiting new advances in modelling techniques used in DW-MRI, which provide more accurate representation of underlying white matter anatomy. This thesis introduces a methodology for fully utilising new tissue models in DWMRI to improve tractography. It is known from histology that there are regions of white matter where fibres disperse or curve rapidly at length scales below the DW-MRI voxel resolution. One area where dispersion is particularly prominent is the corona radiata. New DW-MRI models capture dispersion utilising specialised parametric probability distributions. We present novel tractography algorithms utilising these parametric models of dispersion in tractography to improve connectivity estimation in areas of dispersing fibres. We first present an algorithm utilising the the new parametric models of dispersion for tractography in a simple Bayesian framework. We then present an extension to this algorithm which introduces a framework to pool neighbourhood information from multiple voxels in the neighbournhood surrounding the tract in order to better estimate connectivity, introducing the new concept of the neighbourhood-informed orientation distribution function (NI-ODF). Specifically, using neighbourhood exploration we address the ambiguity arising in ’fanning polarity’. In regions of dispersing fibres, the antipodal symmetry inherent in DW-MRI makes it impossible to resolve the polarity of a dispersing fibre configuration from a local voxel-wise model in isolation, by pooling information from neighbouring voxels, we show that this issue can be addressed. We evaluate the newly proposed tractography methods using synthetic phantoms simulating canonical fibre configurations and validate the ability to effectively navigate regions of dispersing fibres and resolve fanning polarity. We then validate that the algorithms perform effectively in real in vivo data, using DW-MRI data from 5 healthy subjects. We show that by utilising models of dispersion, we recover a wider range of connectivity compared to other standard algorithms when tracking through an area of the brain known to have significant white fibre dispersion - the corona radiata. We then examine the impact of the new algorithm on global connectivity estimates in the brain. We find that whole brain connectivity networks derived using the new tractography method feature strong connectivity between frontal lobe regions. This is in contrast to networks derived using competing tractography methods which do not account for sub-voxel fibre dispersion. We also compare thalamo-cortical connectivity estimated using the newly proposed tractography method and compare with a compteing tractography method, finding that the recovered connectivity profiles are largely similar, with some differences in thalamo-cortical connections to regions of the frontal lobe. The results suggest that fibre dispersion is an important structural feature to model in the basis of a tractography algorithm, as it has a strong effect on connectivity estimation

Improved Quantification of Connectivity in Human Brain Mapping

Diffusion magnetic resonance imaging (dMRI) is an advanced MRI methodology that can be used to probe the microstructure of biological tissue. dMRI can provide orientation information by modeling the process of water diffusion in white matter. This thesis presents contributions in three areas of diffusion imaging technology: diffusion reconstruction, quantification, and validation of derived metrics. It presents a novel reconstruction method by combining generalized q-sampling imaging, spherical harmonic basis functions and constrained spherical deconvolution methods to estimate the fiber orientation distribution function (ODF). This method provides improved spatial localization of brain nuclei and fiber tract separation. A novel diffusion anisotropy metric is presented that provides anatomically interpretable measurements of tracts that are robust in crossing areas of the brain. The metric, directional Axonal Volume (dAV) provides an estimate of directional water content of the tract based on the (ODF) and proton density map. dAV is a directionally sensitive metric and can separate anisotropic water content for each fiber population, providing a quantification in milliliters of water. A method is provided to map voxel-based dAV onto tracts that is not confounded by crossing areas and follows the tract morphology. This work introduces a novel textile based hollow fiber anisotropic phantom (TABIP) for validation of reconstruction and quantification methods. This provides a ground truth reference for axonal scale water tubular structures arranged in various anatomical configurations, crossing and mixing patterns. Analysis shows that: 1) the textile tracts are identifiable with scans used in human imaging and produced tracts and voxel metrics in the range of human tissue; 2) the current methods could resolve crossing at 90o and 45o but not 30o; 3) dAV/NODDI model closely matches (r=0.95) the number of fibers whereas conventional metrics poorly match (i.e., FA r=0.32). This work represents a new accurate quantification of axonal water content through diffusion imaging. dAV shows promise as a new anatomically interpretable metric of axonal connectivity that is not confounded by factors such as axon dispersion, crossing and local isotropic water content. This will provide better anatomical mapping of white matter and potentially improve the detection of axonal tract pathology

Modelling uncertainty in brain fibre orientation from diffusion-weighted magnetic resonance imaging.

Diffusion-weighted magnetic resonance imaging (DW-MRI) permits in-vivo measurements of water diffusion, from which we can infer the orientation of white matter fibres in the brain. We show that by ordering the measurements, we can improve the reproducibility of the fibre-orientation estimate from partially-completed DW-MRI scans, without altering the complete data set. Tractography methods reconstruct entire fibre pathways from the local fibre-orientation estimates. Because the local fibre-orientation measurements are subject to uncertainty, the reconstructed fibre pathways are best described with a probabilistic algorithm. One way to estimate the connection probabilities is by defining a probability density function (PDF) in each voxel, and sampling from the PDF in a Monte-Carlo fashion. We propose new models of the PDF based on standard spherical statistical methods. The models improve previous work by closely modelling the dispersion of repeated noisy estimates of the fibre orientation. We compare a simple PDF (the Watson PDF) that models circular cluster of axes to a more general PDF (the Bingham PDF) that models circular or elliptical clusters of axes. We also propose models of the PDF in regions of crossing fibres, where there are two distinct fibre populations in the voxel. We validate the PDFs by comparing them to the uncertainty in fibre orientation calculated from bootstrap resampling of a repeated brain MR acquisition. We find mat the Bingham PDF produces connection probabilities that are closer to the bootstrap results man the Watson PDF. We use the new PDF models to perform a connectivity-based segmentation of the corpus callosum in eight different subjects. The results are similar to those of previous studies on corpus callosum connectivity, despite the use of finer cortical labelling, suggesting that the dominant connections from the corpus callosum project to the superior frontal gyrus, the superior parietal gyrus and the occipital gyrus

Estimating uncertainty in multiple fibre reconstructions

Diffusion magnetic resonance imaging (MRI) is a technique that allows us to probe the microstructure of materials. The standard technique in diffusion MRI is diffusion tensor imaging (DTI). However, DTI can only model a single fibre orientation and fails in regions of complex microstructure. Multiple-fibre algorithms aim to overcome this limitation of DTI, but there remain many questions about which multiple-fibre algorithms are most promising and how best to exploit them in tractography. This work focuses on exploring the potential of multiple-fibre reconstructions and preparing them for transfer to the clinical arena. We provide a standardised framework for comparing multiple-fibre algorithms and use it for a robust comparison of standard algorithms, such as persistent angular structure (PAS) MRI, spherical deconvolution (SD), maximum entropy SD (MESD), constrained SD (CSD) and QBall. An output of this framework is the parameter settings of the algorithms that maximise the consistency of reconstructions. We show that non-linear algorithms, and CSD in particular, provide the most consistent reconstructions. Next, we investigate features of the reconstructions that can be exploited to improve tractography. We show that the peak shapes of multiple-fibre reconstructions can be used to predict anisotropy in the uncertainty of fibre-orientation estimates. We design an experiment that exploits this information in the probabilistic index of connectivity (PICo) tractography algorithm. We then compare PICo tractography results using information about peak shape and sharpness to estimate uncertainty with PICo results using only the peak sharpness to estimate uncertainty and show structured differences. The final contribution of this work is a robust algorithm for calibrating PICo that overcomes some of the limitations of the original algorithm. We finish with some early exploratory work that aims to estimate the distribution of fibre-orientations in a voxel using features of the reconstruction

Processing of diffusion MR images of the brain: from crossing fibres to distributed tractography

Diffusion-weighted (DW) magnetic resonance imaging allows the quantification of water diffusion within tissue. Due to the hindrance of water molecules by the various tissue compartments, probing for the diffusive properties of a region can provide information on the underlying structure. This is particularly useful for the human brain, whose anatomy is complex. Diffusion imaging provides currently the only tool to study the brain connectivity and organization non-invasively and in-vivo, through a group of methods, commonly referred to as tractography methods. This thesis is concerned with brain anatomical connectivity and tractography. The goal is to elucidate problems with existing approaches used to process DW images and propose solutions and methods through new frameworks. These concern data from two popular DW imaging protocols, diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI), or Q-ball imaging in particular. One of the problems tackled is resolving crossing fibre configurations, a major concern in DW imaging, using data that can be routinely acquired in a clinical setting. The physical constraint of spatial continuity of the diffusion environment is imposed throughout the brain volume, using a multi-tensor model and a regularization method. The new approach is shown to improve tractography results through crossing regions. Quantitative tractography algorithms are also proposed that, apart from reconstructing the white matter tracts, assign relative indices of anatomical connectivity to all regions. A fuzzy algorithm is presented for assessing orientational coherence of neuronal tracts, reflecting the fuzzy nature of medical images. As shown for different tracts, where a-priori anatomical knowledge exists, regions that are coherently connected and possibly belong to the same tract can be differentiated from the background. In a different framework, elements of graph theory are used to develop a new tractography algorithm that can utilize information from multiple image modalities to assess brain connectivity. Both algorithms inherently consider crossing fibre information and are shown to solve problems that affect existing methods

Translation of Novel Imaging Techniques into Clinical Use for Patients with Epilepsy

Temporal lobe epilepsy is the most common focal epilepsy. Up to 40% of patients are refractory to medication. Anterior temporal lobe resection (ATLR) is an effective treatment but damage to the optic radiation can result in a visual field deficit (VFD) that precludes driving, a key goal of surgery. Diffusion tensor imaging tractography allows the in vivo delineation of white matter tracts such as the optic radiation. This thesis addresses the role of optic radiation tractography in planning and subsequently improving the safety of epilepsy surgery. I show how tractography assists risk stratification and surgical planning in patients with lesions near the optic radiation and assess the utility of different tractography methods for surgical planning. To derive the greatest benefit, tractography information should be available during surgery which requires correction for intraoperative brain shift and other sources of image distortion. I apply software developed at UCL in a clinical population underlying ATLR to show that postoperative imaging can predict the VFD and then use this software in real time during surgery in an intraoperative MRI suite. Updated anatomical scans can be acquired during surgery and tractography data accurately mapped on to these and displayed on the operating microscope display. I demonstrate that this image guidance allows the neurosurgeon to avoid significant VFD without affecting the seizure outcome. Diffusion imaging can also probe tissue microstructure. I explore how structural changes within the frontoparietal working memory network and temporal lobes are related to working memory impairment in TLE. I describe the structural changes that occur following ATLR showing both Wallerian degeneration and structural plasticity. Finally, I show how a novel diffusion model (NODDI) could aid the clinical assessment of patients with focal cortical dysplasia. The emphasis throughout this thesis is how diffusion imaging can be clinically useful and address clinically relevant outcomes