Measuring Brain Serine With Proton Magnetic Resonance Spectroscopy At 3.0 Tesla

Proton magnetic resonance spectroscopy (MRS) non-invasively measures regional human brain chemistry in vivo, providing concentration estimates for several metabolites in a pre-selected region of interest. MRS has been applied to investigations of disease-related metabolic and neurochemical alterations in schizophrenia since the early 90’s. The objective of this research is to implement a metabolite-selective MRS method to quantify endogenous concentrations of human brain serine. Serine is a naturally-occurring amino acid and an important co-modulator of the N-Methyl D-aspartic Acid (NMDA) glutamate receptor. Glutamate abnormalities have been implicated in the pathophysiology of schizophrenia, especially its so-called negative and cognitive symptoms, which can be relieved by D-serine supplements. Measurements of serine have been impossible using standard MRS due to its low concentration and strongly coupled spins. In this thesis, we implement and test an advanced MRS pulse sequence, called DANTE-PRESS, using a narrow band radiofrequency (RF) pulse to isolate the serine signals from the human brain spectra for the first time on a 3.0 Tesla clinical scanner. Test-retest reliability of in vitro serine measurements in brain-mimicking samples was verified using ten repeated acquisitions from two serine samples with concentrations of 0.732 mM (similar to “in vivo”) and 1.464 mM (“double in vivo”) at baseline and one week later. Within- and between-session reproducibility was measured with the coefficient of variation (CV) and one-way ANOVA, respectively. Average serine “in vivo” concentration at baseline, one week later, “double in vivo” at baseline, and one week later were 1.13 ± 0.09 (CV = 8.3%), 1.06 ± 0.10 (CV = 9.9%), 2.18 ± 0.13 (CV = 5.7%) and 2.23 ± 0.14 (CV = 6.5%), respectively. The thesis also presents a 3.0 Tesla application of DANTE-PRESS in a human brain region relevant to schizophrenia as proof-of-concept. Future studies can extend the work to implementation of DANTE-PRESS at 7.0 Tesla and in vivo test-retest

Quantification of NAD+ in human brain with 1 H MR spectroscopy at 3 T: Comparison of three localization techniques with different handling of water magnetization.

PURPOSE The detection of nicotinamide-adenine-dinucleotide (NAD+ ) is challenging using standard 1 H MR spectroscopy, because it is of low concentration and affected by polarization-exchange with water. Therefore, this study compares three techniques to access NAD+ quantification at 3 T-one with and two without water presaturation. METHODS A large brain volume in 10 healthy subjects was investigated with three techniques: semi-LASER with water-saturation (WS) (TE = 35 ms), semi-LASER with metabolite-cycling (MC) (TE = 35 ms), and the non-water-excitation (nWE) technique 2D ISIS-localization with chemical-shift-selective excitation (2D I-CSE) (TE = 10.2 ms). Spectra were quantified with optimized modeling in FiTAID. RESULTS NAD+ could be well quantified in cohort-average spectra with all techniques. Obtained apparent NAD+ tissue contents are all lower than expected from literature confirming restricted visibility by 1 H MRS. The estimated value from WS-MRS (58 μM) was considerably lower than those obtained with non-WS techniques (146 μM for MC-semi-LASER and 125 μM for 2D I-CSE). The nWE technique with shortest TE gave largest NAD+ signals but suffered from overlap with large amide signals. MC-semi-LASER yielded best estimation precision as reflected in relative Cramer-Rao bounds (14%, 21 μM/146 μM) and also best robustness as judged by the coefficient-of-variance over the cohort (11%, 10 μM/146 μM). The MR-visibility turned out as 16% with WS and 41% with MC. CONCLUSION Three methods to assess NAD+ in human brain at 3 T have been compared. NAD+ could be detected with a visibility of ∼41% for the MC method. This may open a new window for the observation of pathological changes in the clinical research setting

Implementation of Hadamard Encoding and Reconstruction of MEGA-Edited Spectroscopy (HERMES) for quantification of ɣ-aminobutyric acid (GABA) and glutathione (GSH)

The present study aimed to accelerate and improve accuracy of ɣ-aminobutyric acid (GABA) and glutathione (GSH) quantification. These metabolites, present at low concentrations in the brain, are challenging to detect using MR spectroscopy due to the fact that their resonance frequencies overlap with those of other more abundant metabolites. The advanced spectral editing techniques involving J-difference editing that are required to resolve the overlapping signals of these low concentration metabolites are not routinely available on clinical MRI scanners. In this work we implemented on a 3T Siemens Skyra MRI a novel MRS technique called Hadamard Encoding and Reconstruction of MEGA-Edited Spectroscopy (HERMES) to simultaneously detect GABA and GSH, developed a novel postprocessing technique that simultaneously models the sum and various difference spectra, and evaluated the performance of the sequence and processing method both in phantoms and in vivo. HERMES was implemented by modifying the Siemens GABA-edited MEGA-PRESS WIP sequence to include two additional sub-experiments – one editing GSH with a single lobe pulse and one editing both GABA and GSH using a dual lobe pulse, and replacing the Siemens pulses with ‘universal' pulses similar to those used in a previous implementation of HERMES on a Philips platform. Performance was assessed in a phantom and 22 healthy adults, 15 of whom provided usable data (7 male, mean age 25.6 ± 2.7 yr). Three of the subjects were scanned 3 times to assess reproducibility. Data were processed and compared using a set of custom scripts in MATLAB. Following frequency and phase correction of individual averages with GANNET, we applied our custom simultaneous linear combination model that iteratively fit the concatenated sum and difference spectra using a least squares routine. SPM was used for tissue segmentation of structural images and FID-A to simulate high-resolution basis sets. The simultaneous modelling technique provided absolute quantification results for 15 metabolite moieties using internal unsuppressed water as a reference. The performance of the simultaneous fitting approach was compared to multiple independent fittings for HERCULES (Hadamard Editing Resolves Chemicals Using Linear-combination Estimation of Spectra) data that had been previously acquired on a 3T Philips Achieva MRI. Although the HERMES sequence implemented on the Siemens platform as part of this project was able to successfully edit both GABA and GSH, and generate line shapes consistent with the work by Saleh et al. (2016), quantification accuracy was disappointing. In the phantom data, GSH and GABA concentrations were both roughly 50% of known levels. Since the actual concentrations in vivo were not known, we were not able to establish accuracy, but quantification agreement between the MEGA-PRESS and HERMES sequences was poor for most metabolites. Specifically, GABA levels were two to three times higher than expected values using both HERMES and GABA-edited MEGA-PRESS. Despite poor absolute agreement, concentrations from HERMES and MEGA-PRESS data were moderately correlated, and HERMES data showed lower coefficients of variation across subjects, suggesting that it may be more reliable. HERMES was also more reproducible across scanning sessions and participants for more metabolites than GABA- or GSH-edited MEGA-PRESS. Our findings also showed that simultaneous fitting using the sum and difference spectra produces lower coefficients of variation for most metabolites than fittings to sum and difference spectra separately

Multimodal neuroimaging signatures of early cART-treated paediatric HIV - Distinguishing perinatally HIV-infected 7-year-old children from uninfected controls

Introduction: HIV-related brain alterations can be identified using neuroimaging modalities such as proton magnetic resonance spectroscopy (1H-MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI). However, few studies have combined multiple MRI measures/features to identify a multivariate neuroimaging signature that typifies HIV infection. Elastic net (EN) regularisation uses penalised regression to perform variable selection, shrinking the weighting of unimportant variables to zero. We chose to use the embedded feature selection of EN logistic regression to identify a set of neuroimaging features characteristic of paediatric HIV infection. We aimed to determine 1) the most useful features across MRI modalities to separate HIV+ children from HIV- controls and 2) whether better classification performance is obtained by combining multimodal MRI features rather than using features from a single modality. Methods: The study sample comprised 72 HIV+ 7-year-old children from the Children with HIV Early Antiretroviral Therapy (CHER) trial in Cape Town, who initiated combination antiretroviral therapy (cART) in infancy and had their viral loads suppressed from a young age, and 55 HIV- control children. Neuroimaging features were extracted to generate 7 MRI-derived sets. For sMRI, 42 regional brain volumes (1st set), mean cortical thickness and gyrification in 68 brain regions (2nd and 3rd set) were used. For DTI data: radial (RD), axial (AD), mean (MD) diffusivities, and fractional anisotropy (FA) in each of 20 atlas regions were extracted for a total of 80 DTI features (4th set). For 1H-MRS, concentrations of 14 metabolites and their ratios to creatine in the basal ganglia, peritrigonal white matter, and midfrontal gray matter voxels (5th, 6th and 7th set) were considered. A logistic EN regression model with repeated 10-fold cross validation (CV) was implemented in R, initially on each feature set separately. Sex, age and total intracranial volume (TIV) were included as confounders with no shrinkage penalty. For each model, the classification performance for HIV+ vs HIV- was assessed by computing accuracy, specificity, sensitivity, and mean area under the receiver operator characteristic curve (AUC) across 10 CV folds and 100 iterations. To combine feature sets, the best performing set was concatenated with each of the other sets and further EN regressions were run. The combination giving the largest AUC was combined with each of the remaining sets until there was no further increase in AUC. Two concatenation techniques were explored: nested and non-nested modelling. All models were assessed for their goodness of fit using χ 2 likelihood ratio tests for non-nested models and Akaike information criterion (AIC) for nested models. To identify features most useful in distinguishing HIV infection, the EN model was retrained on all the data, to find features with non-zero weights. Finally, multivariate imputation using chained equations (MICE) was explored to investigate the effect of increased sample size on classification and feature selection. Results: The best performing modality in the single modality analysis was sMRI volume

IMAGING SPECIFIC ABSORPTION RATE WITH MR THERMOMETRY USING PARAMAGNETIC LANTHANIDE COMPLEXES AND IN VIVO GABA MR SPECTROSCOPY IN MOVEMENT DISORDERS

Magnetic Resonance Imaging (MRI) is a popular imaging modality due to its ability to provide excellent soft tissue contrast without exposure to ionizing radiation. It can be used for temperature monitoring (thermometry) as well as for assessing the biochemistry in vivo (MRS). This dissertation focuses separately on the development, application and quantitation issues of these two aspects of MRI

New techniques for quantification of biomarkers and metabolites by magnetic resonance imaging and spectroscopy

Since its early beginnings, almost five decades ago, MRI has revolutionised medical imaging, sustaining an active field of research into new applications, and improved understanding of the underlying mechanisms. Its complexity and flexibility, as a non-invasive imaging modality is simultaneously, an asset and a challenge. Quantitative imaging provides a particular challenge due to an increased sensitivity to experimental variations. The development of accurate and robust methods for quantitative magnetic resonance requires protocols to be carefully calibrated to produce consistent results. This necessitates the use of test objects with known, stable, configurable characteristics. This thesis is aimed at the development of these test objects, and their use within quantitative imaging, spectroscopy, and the development of new techniques.First, a set of magnetic resonance test objects were created, and their relaxation properties assessed. T1 and T2 are calculated using spin, and multi-spin echo sequences respectively. Several contrast and gelling agents were assessed, and the relaxivity estimated in each case. The protocol dependence of T1 estimation methods is examined using a phantom and in-vivo study. Saturation and inversion recovery estimations are compared to variable flip angle methods, and the statistical distributions of T1 maps quantified. A series of calibrated phantom studies are conducted, assessing the analysis methods used for in-vivo magnetic resonance spectroscopy. The concentration of brain metabolites is varied within liquid and gel phantoms, and the ratio of GABA to NAA is calculated using a number of analysis tools, and in-house software.Finally, a magnetic resonance spectroscopy Hamiltonian simulator is implemented in Matlab. The simulator is utilised by collaborators in developing a quantum control framework. Optimal control is used to generate chemically selective RF pulses, and initial experimental implementations explored.The quantitative methods were found to exhibit both acquisition and analysis method dependencies. However, results were largely consistent within methodology, highlighting the need for consistency across sites to ensure valid comparison. The the-oretical development of novel RF pulses has been successful, but much work remains to approach experimental implementation

Quantification of bone using a 3.0 tesla clinical magnetic resonance scanner

The work in this thesis examines the potential of using magnetic resonance imaging and spectroscopy (MRI & MRS) as a quantitative tool for diagnosing bone abnormalities at multiple skeletal sites, which could be used in conjunction with routine clinical imaging.MRI and MRS are routinely used in the clinical setting for the diagnosis of various types of diseases and abnormalities due to its advantages of providing excellent soft tissue contrast and also providing physiological and metabolic information. The use of MRI and MRS as a direct diagnostic tool for bone abnormalities is very limited at the moment due to issues of costs and standardisation. The aim in this thesis was to use the clinical 3.0 T MR scanner to acquire data from bone and bone marrow for identification of structural and chemical properties and to use those features to identify differences in bone strength and condition. The volunteers in this thesis were part of the high bone mass (HBM) study and they had additional acquisitions from dual-energy X-ray absorptiometry (DEXA) and peripheral quantitative computed tomography (pQCT).MR acquisition protocols have been successfully optimised for each type of bone region and in-house software has also been created to process the acquired data and quantify various types of structural and chemical properties.The MR data from distal radius and tibia demonstrated good correlation with pQCT data (e.g. Figure 8-2 & Figure 8-3) and were also able to differentiate between HBM-affected and control populations (e.g. Figure 8-26). The MR data from lumbar vertebrae also demonstrated good correlation with DEXA data and some of the measurements were also able to differentiate between the HBM-affected and control populations.The combined results from this thesis demonstrate that both MRI and MRS are sensitive techniques for measurement of bone quantity and quality, and they are ready to be applied for clinical investigation as part of routine clinical imaging to identify bone strength in relation to abnormalities and treatments

Glutamate Imaging of Mouse Models of Neurodegeneration

Malfunctions in the glutamatergic system of the central nervous system have been implicated in neurodegenerative diseases such as Alzheimer’s disease (AD), tauopathies, and Parkinson’s disease (PD). A non-invasive measurement of glutamate would enhance our understanding of neurodegenerative processes and potentially facilitate early diagnosis. The current method for measuring glutamate in vivo is proton magnetic resonance spectroscopy (1HMRS) although it has poor spatial resolution and weak sensitivity to glutamate changes. The primary objective of this thesis was to measure pathology induced changes in glutamate levels in mouse models of neurodegeneration using a novel magnetic resonance imaging technique, glutamate chemical exchange saturation transfer (GluCEST) imaging. Several studies were performed in three mouse models of neurodegeneration: the APP-PS1 transgenic model of amyloid-beta pathology of AD, the PS19 transgenic model of tau pathology, and the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) neurotoxin model of PD. Glutamate levels derived from GluCEST imaging were correlated with results from 1HMRS and immunohistochemistry (IHC). The primary IHC antibodies that were investigated include markers of phosphorylated tau protein, synapse density, neuron density, glial cell reactivity, a glutamate transporter, and an NMDA receptor. GluCEST contrast correlated with 1HMRS-derived glutamate levels in the striatum of APP-PS1 mice (R2=0.91) and the thalamus of PS19 mice (R2=0.64). However, GluCEST detected deficits in PS19 mice four months earlier than 1HMRS, highlighting the method’s enhanced sensitivity to glutamate. Demonstrating the advantage of high spatial resolution, GluCEST imaging measured sub-hippocampal dynamics in glutamate levels in the aging PS19 mouse. A gradient in glutamate levels along the mouse hippocampus was also measured in vivo using GluCEST. While hippocampal glutamate levels were significantly decreased in early stages of PS19 tauopathy, glutamate levels in the dentate gyrus (DG) and cornu ammonis (CA1) increased at 9-13 months. Decreased GluCEST was concurrent with synapse loss and occurred before structural volume loss. Elevated GluCEST was associated with glial fibrillary acidic protein (GFAP) immunostaining in late stages of the PS19 tauopathy model and in the striatum of the MPTP PD model. Results of this work demonstrate the use of GluCEST imaging to study regional and temporal variations in glutamate in different pathologies associated with neurodegeneration

Integration of magnetic resonance spectroscopic imaging into the radiotherapy treatment planning

L'objectif de cette thèse est de proposer de nouveaux algorithmes pour surmonter les limitations actuelles et de relever les défis ouverts dans le traitement de l'imagerie spectroscopique par résonance magnétique (ISRM). L'ISRM est une modalité non invasive capable de fournir la distribution spatiale des composés biochimiques (métabolites) utilisés comme biomarqueurs de la maladie. Les informations fournies par l'ISRM peuvent être utilisées pour le diagnostic, le traitement et le suivi de plusieurs maladies telles que le cancer ou des troubles neurologiques. Cette modalité se montre utile en routine clinique notamment lorsqu'il est possible d'en extraire des informations précises et fiables. Malgré les nombreuses publications sur le sujet, l'interprétation des données d'ISRM est toujours un problème difficile en raison de différents facteurs tels que le faible rapport signal sur bruit des signaux, le chevauchement des raies spectrales ou la présence de signaux de nuisance. Cette thèse aborde le problème de l'interprétation des données d'ISRM et la caractérisation de la rechute des patients souffrant de tumeurs cérébrales. Ces objectifs sont abordés à travers une approche méthodologique intégrant des connaissances a priori sur les données d'ISRM avec une régularisation spatio-spectrale. Concernant le cadre applicatif, cette thèse contribue à l'intégration de l'ISRM dans le workflow de traitement en radiothérapie dans le cadre du projet européen SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) financé par la Commission européenne (FP7-PEOPLE-ITN).The aim of this thesis is to propose new algorithms to overcome the current limitations and to address the open challenges in the processing of magnetic resonance spectroscopic imaging (MRSI) data. MRSI is a non-invasive modality able to provide the spatial distribution of relevant biochemical compounds (metabolites) commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate and reliable information from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, the interpretation of MRSI data is still a challenging problem due to different factors such as the low signal-to-noise ratio (SNR) of the signals, the overlap of spectral lines or the presence of nuisance components. This thesis addresses the problem of interpreting MRSI data and characterizing recurrence in tumor brain patients. These objectives are addressed through a methodological approach based on novel processing methods that incorporate prior knowledge on the MRSI data using a spatio-spectral regularization. As an application, the thesis addresses the integration of MRSI into the radiotherapy treatment workflow within the context of the European project SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) founded by the European Commission (FP7-PEOPLE-ITN framework)

Comparison of Magnetic Resonance Spectroscopy (MRS) data in children with and without HIV at 11-12 years

Although HIV and antiretroviral drugs have been shown to cause damage in the brain, the long-term impacts of perinatal infection, early treatment and exposure in children at 11 years, remain unclear. The effects of HIV and antiretroviral therapy (ART), whilst indistinguishable, can be investigated at a chemical level through proton magnetic resonance spectroscopy (1H-MRS). Previous studies in children have largely focused on individual metabolite changes. However, several adult studies have now advanced beyond this to address patterns of metabolic activity that are altered with HIV infection. Using a 3T Skyra scanner, 136 children (76 HIV+, 30 HEU, 30 HU; 71 males) between the ages of 11.0- 12.5 years, and from a similar socioeconomic background, were scanned. In this study metabolite concentrations were quantified within the basal ganglia (BG), midfrontal gray matter (MFGM) and peritrigonal white matter (PWM). We utilised linear regression to investigate individual metabolite differences, comparing HIV-infected (HIV+) children from the Children with HIV Early Antiretroviral Therapy (CHER) trial, and HIV-exposed-uninfected (HEU) children, to HIV-unexposed (HU) children. Pearson's correlation analysis, factor analysis and logistic regression were then used to study alterations in metabolic patterns between HIV+ and HIV-uninfected (HIV-) children. Analysis of the data was carried out in R. We found elevated total choline in the BG (p = 0.03) and MFGM (p < 0.001) of HIV+ children, as well as reduced PWM total NAA (p = 0.03) and total creatine (p = 0.01). Altered metabolite concentrations were further observed in HEU children. Additionally, we identified a cross-regional coupling of choline which distinguishes HIV+ from HIV- children (p < 0.001). These findings indicate that multiregional inflammation and PWM axonal damage are occurring in HIV+ children at 11 years. Ultimately, the consequences of perinatal HIV acquisition, in spite of early treatment, continue to be seen at 11 years, as do the impacts of exposure