Double volumetric navigators for real-time simultaneous shim and motion measurement and correction in Glycogen Chemical Exchange Saturation Transfer (GlycoCEST) MRI

Glycogen is the primary glucose storage mechanism in in living systems and plays a central role in systemic glucose homeostasis. The study of muscle glycogen concentrations in vivo still largely relies on tissue sampling methods via needle biopsy. However, muscle biopsies are invasive and limit the frequency of measurements and the number of sites that can be assessed. Non-invasive methods for quantifying glycogen in vivo are therefore desirable in order to understand the pathophysiology of common diseases with dysregulated glycogen metabolism such as obesity, insulin resistance, and diabetes, as well as glycogen metabolism in sports physiology. Chemical Exchange Saturation Transfer (CEST) MRI has emerged as a non-invasive contrast enhancement technique that enables detection of molecules, like glycogen, whose concentrations are too low to impact the contrast of standard MR imaging. CEST imaging is performed by selectively saturating hydrogen nuclei of the metabolites that are in chemical exchange with those of water molecules and detecting a reduction in MRI signal in the water pool resulting from continuous chemical exchange. However, CEST signal can easily be compromised by artifacts. Since CEST is based on chemical shift, it is very sensitive to field inhomogeneity which may arise from poor initial shimming, subject respiration, heating of shim iron, mechanical vibrations or subject motion. This is a particular problem for molecules that resonate close to water, such as - OH protons in glycogen, where small variations in chemical shift cause misinterpretation of CEST data. The purpose of this thesis was to optimize the CEST MRI sequence for glycogen detection and implement a real-time simultaneous motion and shim correction and measurement method. First, analytical solution of the Bloch-McConnell equations was used to find optimal continuous wave RF pulse parameters for glycogen detection, and results were validated on a phantom with varying glycogen concentrations and in vivo on human calf muscle. Next, the CEST sequence was modified with double volumetric navigators (DvNavs) to measure pose changes and update field of view and zero- and first-order shim parameters. Finally, the impact of B0 field fluctuations on the scan-rescan reproducibility of CEST was evaluated in vivo in 9 volunteers across 10 different scans. Simulation results showed an optimal RF saturation power of 1.5µT and duration of 1s for glycoCEST. These parameters were validated experimentally in vivo and the ability to detect varying glycogen concentrations was demonstrated in a phantom. Phantom data showed that the DvNav-CEST sequence accurately estimates system frequency and linear shim gradient changes due to motion and corrects resulting image distortions. In addition, DvNav-CEST was shown to yield improved CEST quantification in vivo in the presence of motion and motion-induced field inhomogeneity. B0 field fluctuations were found to lower the reproducibility of CEST measures: the mean coefficient of variation (CoV) for repeated scans was 83.70 ± 70.79 % without shim correction. However, the DvNav-CEST sequence was able to measure and correct B0 variations, reducing the CoV to 2.6 ± 1.37 %. The study confirms the possibility of detecting glycogen using CEST MRI at 3 T and shows the potential of the real-time shim and motion navigated CEST sequence for producing repeatable results in vivo by reducing the effect of B0 field fluctuations

Real-time motion and magnetic field correction for GABA editing using EPI volumetric navigated MEGA-SPECIAL sequence: Reproducibility and Gender effects

γ-aminobutyric acid (GABA) is the primary inhibitory neurotransmitter and is of great interest to the magnetic resonance spectroscopy (MRS) community due to its role in several neurological diseases and disorders. Since GABA acquisition without macromolecule contamination requires long scan times and strongly depends on magnetic field (B0) stability, it is highly susceptible to motion and B0 inhomogeneity. In this work, a pair of three-dimensional (3D) echo planar imaging (EPI) volumetric navigators (vNav) with different echo times, were inserted in MEGA-SPECIAL to perform prospective correction for changes in the subject's head position and orientation, as well as changes in B0. The navigators do not increase acquisition time and have negligible effect on the GABA signal. The motion estimates are obtained by registering the first of the pairs of successive vNav volume images to the first volume image. The 3D field maps are calculated through complex division of the pair of vNav contrasts and are used for estimating zero- and first-order shim changes in the volume of interest (VOI). The efficacy of the vNav MEGA-SPECIAL sequence was demonstrated in-vitro and in vivo. Without motion and shim correction, spectral distortions and increases in spectral fitting error, linewidth and GABA concentration relative to creatine were observed in the presence of motion. The navigated sequence yielded high spectral quality despite significant subject motion. Using the volumetric navigated MEGA-SPECIAL sequence, the reproducibility of GABA measurements over a 40 minute period was investigated in two regions, the anterior cingulate (ACC) and medial parietal (PAR) cortices, and compared for different analysis packages, namely LCModel, jMRUI and GANNET. LCModel analysis yielded the most reproducible results, followed by jMRUI and GANNET. GABA levels in ACC were unchanged over time, while GABA levels in PAR were significantly lower for the second measurement. In ACC, GABA levels did not differ between males and females. In contrast, males had higher GABA levels in PAR. This gender difference was, however, only present in the first acquisition. Only in males did GABA levels in PAR decrease over time. These results demonstrate that gender differences are regional, and that GABA levels may fluctuate differently in different regions and sexes

Prediction of motion induced magnetic fields for human brain MRI at 3T

Objective Maps of B0 field inhomogeneities are often used to improve MRI image quality, even in a retrospective fashion. These field inhomogeneities depend on the exact head position within the static field but acquiring field maps (FM) at every position is time consuming. Here we explore different ways to obtain B0 predictions at different head positions. Methods FM were predicted from iterative simulations with four field factors: 1) sample induced B0 field, 2) system's spherical harmonic shim field, 3) perturbing field originating outside the field of view, 4) sequence phase errors. The simulation was improved by including local susceptibility sources estimated from UTE scans and position-specific masks. The estimation performance of the simulated FMs and a transformed FM, obtained from the measured reference FM, were compared with the actual FM at different head positions. Results The transformed FM provided inconsistent results for large head movements (>5 degree rotation), while the simulation strategy had a superior prediction accuracy for all positions. The simulated FM was used to optimize B0 shims with up to 22.2% improvement with respect to the transformed FM approach. Conclusion The proposed simulation strategy is able to predict movement induced B0 field inhomogeneities yielding more precise estimates of the ground truth field homogeneity than the transformed FM

Retrospective Motion Correction in Magnetic Resonance Imaging of the Brain

Magnetic Resonance Imaging (MRI) is a tremendously useful diagnostic imaging modality that provides outstanding soft tissue contrast. However, subject motion is a significant unsolved problem; motion during image acquisition can cause blurring and distortions in the image, limiting its diagnostic utility. Current techniques for addressing head motion include optical tracking which can be impractical in clinical settings due to challenges associated with camera cross-calibration and marker fixation. Another category of techniques is MRI navigators, which use specially acquired MRI data to track the motion of the head. This thesis presents two techniques for motion correction in MRI: the first is spherical navigator echoes (SNAVs), which are rapidly acquired k-space navigators. The second is a deep convolutional neural network trained to predict an artefact-free image from motion-corrupted data. Prior to this thesis, SNAVs had been demonstrated for motion measurement but not motion correction, and they required the acquisition of a 26s baseline scan during which the subject could not move. In this work, a novel baseline approach is developed where the acquisition is reduced to 2.6s. Spherical navigators were interleaved into a spoiled gradient echo sequence (SPGR) on a stand-alone MRI system and a turbo-FLASH sequence (tfl) on a hybrid PET/MRI system to enable motion measurement throughout image acquisition. The SNAV motion measurements were then used to retrospectively correct the image data. While MRI navigator methods, particularly SNAVs that can be acquired very rapidly, are useful for motion correction, they do require pulse sequence modifications. A deep learning technique may be a more general solution. In this thesis, a conditional generative adversarial network (cGAN) is trained to perform motion correction on image data with simulated motion artefacts. We simulate motion in previously acquired brain images and use the image pairs (corrupted + original) to train the cGAN. MR image data was qualitatively and quantitatively improved following correction using the SNAV motion estimates. This was also true for the simultaneously acquired MR and PET data on the hybrid system. Motion corrected images were more similar than the uncorrected to the no-motion reference images. The deep learning approach was also successful for motion correction. The trained cGAN was evaluated on 5 subjects; and artefact suppression was observed in all images

Development Of Human Brain Network Architecture Underlying Executive Function

The transition from late childhood to adulthood is characterized by refinements in brain structure and function that support the dynamic control of attention and goal-directed behavior. One broad domain of cognition that undergoes particularly protracted development is executive function, which encompasses diverse cognitive processes including working memory, inhibitory control, and task switching. Delineating how white matter architecture develops to support specialized brain circuits underlying individual differences in executive function is critical for understanding sources of risk-taking behavior and mortality during adolescence. Moreover, neuropsychiatric disorders are increasingly understood as disorders of brain development, are marked by failures of executive function, and are linked to the disruption of evolving brain connectivity. Network theory provides a parsimonious framework for modeling how anatomical white matter pathways support synchronized fluctuations in neural activity. However, only sparse data exists regarding how the maturation of white matter architecture during human brain development supports coordinated fluctuations in neural activity underlying higher-order cognitive ability. To address this gap, we capitalize on multi-modal neuroimaging and cognitive phenotyping data collected as part of the Philadelphia Neurodevelopmental Cohort (PNC), a large community-based study of brain development. First, diffusion tractography methods were applied to characterize how the development of structural brain network topology supports domain-specific improvements in cognitive ability (n=882, ages 8-22 years old). Second, structural connectivity and task-based functional connectivity approaches were integrated to describe how the development of anatomical constraints on functional communication support individual differences in executive function (n=727, ages 8-23 years old). Finally, the systematic impact of head motion artifact on measures of structural connectivity were characterized (n=949, ages 8-22 years old), providing important guidelines for studying the development of structural brain network architecture. Together, this body of work expands our understanding of how developing white matter connectivity in youth supports the emergence of functionally specialized circuits underlying executive processing. As diverse types of psychopathology are increasingly linked to atypical brain maturation, these findings could collectively lead to earlier diagnosis and personalized interventions for individuals at risk for developing mental disorders

The development and application of a simulation system for diffusion-weighted MRI

Diffusion-weighted MRI (DW-MRI) is a powerful, non-invasive imaging technique that allows us to infer the structure of biological tissue. It is particularly well suited to the brain, and is used by clinicians and researchers studying its structure in health and disease. High quality data is required to accurately characterise tissue structure with DW-MRI. Obtaining such data requires the careful optimisation of the image acquisition and processing pipeline, in order to maximise image quality and minimise artefacts. This thesis extends an existing MRI simulator to create a simulation system capable of producing realistic DW-MR data, with artefacts, and applies it to improve the acquisition and processing of such data. The simulator is applied in three main ways. Firstly, a novel framework for evaluating post-processing techniques is proposed and applied to assess commonly used strategies for the correction of motion, eddy-current and susceptibility artefacts. Secondly, it is used to explore the often overlooked susceptibility-movement interaction. It is demonstrated that this adversely impacts analysis of DW-MRI data, and a simple modification to the acquisition scheme is suggested to mitigate its impact. Finally, the simulation is applied to develop a new tool to perform automatic quality control. Simulated data is used to train a classifier to detect movement artefacts in data, with performance approaching that of a classifier trained on real data whilst requiring much less manually-labelled training data. It is hoped that both the findings in this thesis and the simulation tool itself will benefit the DW-MRI community. To this end, the tool is made freely available online to aid the development and validation of methods for acquiring and processing DW-MRI data