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

Hardware considerations for preclinical magnetic resonance of the kidney

Magnetic resonance imaging (MRI) is a noninvasive imaging technology that offers unparalleled anatomical and functional detail, along with diagnostic sensitivity. MRI is suitable for longitudinal studies due to the lack of exposure to ionizing radiation. Before undertaking preclinical MRI investigations of the kidney, the appropriate MRI hardware should be carefully chosen to balance the competing demands of image quality, spatial resolution, and imaging speed, tailored to the specific scientific objectives of the investigation. Here we describe the equipment needed to perform renal MRI in rodents, with the aim to guide the appropriate hardware selection to meet the needs of renal MRI applications.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This chapter on hardware considerations for renal MRI in small animals is complemented by two separate publications describing the experimental procedure and data analysis

Delta Relaxation Enhanced Magnetic Resonance - Development and Application of a Field-Cycling Contrast Mechanism

Delta relaxation enhanced magnetic resonance (dreMR) is a novel imaging method capable of producing contrast proportional only to the concentration of the bound form of the targetable contrast agent using a dynamic field-cycling technique. The characteristic high relaxivity magnetic field dependence of bound paramagnetic contrast agents enables suppression of tissue contrast from unbound agents and unenhanced tissue, thereby increasing probe specificity. The dreMR technique requires an auxiliary actively shielded field-shifting insert electromagnet to modulate the strength of the main clinical magnetic field as a function of time during the relaxation and evolution periods of a pulse sequence. Ablavar (approved for human use) binds specifically to serum albumin and has a strong magnetic field dependence for serum albumin around 1.5T. Native biological tissues and unbound Ablavar demonstrate very little magnetic field dependence. Using a custom dreMR preparatory pulse, where the strength of the B0 field is modulated for different times, signal intensity from only the bound form of Ablavar can be produced. This work entails further advancement of the dreMR method, where we investigate developing the dreMR contrast using phantoms loaded with and without Rabbit Serum Albumin and in vivo imaging using mouse subjects with prostate and breast cancer

Functional Magnetic Resonance Imaging of Breast Cancer

This thesis examines the use of magnetic resonance imaging (MRI) techniques in the detection of breast cancer and the prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NACT). This thesis compares the diagnostic performance of diffusion-weighted imaging (DWI) models in the breast using a systematic review and meta-analysis. Advanced diffusion models have been proposed that may improve the performance of standard DWI using the apparent diffusion coefficient (ADC) to discriminate between malignant and benign breast lesions. Pooling the results from 73 studies, comparable diagnostic accuracy is shown using the ADC and parameters from the intra-voxel incoherent motion (IVIM) and diffusion tensor imaging (DTI) models. This work highlights a lack of standardisation in DWI protocols and methodology. Conventional acquisition techniques used in DWI often suffer from image artefacts and low spatial resolution. A multi-shot DWI technique, multiplexed sensitivity encoding (MUSE), can improve the image quality of DWI. A MUSE protocol has been optimised through a series of phantom experiments and validated in 20 patients. Comparing MUSE to conventional DWI, statistically significant improvements are shown in distortion and blurring metrics and qualitative image quality metrics such as lesion conspicuity and diagnostic confidence, increasing the clinical utility of DWI. This thesis investigates the use of dynamic contrast-enhanced MRI (DCE-MRI) in the detection of breast cancer and the prediction of pCR. Abbreviated MRI (ABB-MRI) protocols have gained increasing attention for the detection of breast cancer, acquiring a shortened version of a full diagnostic protocol (FDP-MRI) in a fraction of the time, reducing the cost of the examination. The diagnostic performance of abbreviated and full diagnostic protocols is systematically compared using a meta-analysis. Pooling 13 studies, equivalent diagnostic accuracy is shown for ABB-MRI in cohorts enriched with cancers, and lower but not significantly different diagnostic performance is shown in screening cohorts. Higher order imaging features derived from pre-treatment DCE-MRI could be used to predict pCR and inform decisions regarding targeted treatment, avoiding unnecessary toxicity. Using data from 152 patients undergoing NACT, radiomics features are extracted from baseline DCE-MRI and machine learning models trained to predict pCR with moderate accuracy. The stability of feature selection using logistic regression classification is demonstrated and a comparison of models trained using features from different time points in the dynamic series demonstrates that a full dynamic series enables the most accurate prediction of pCR.GE Healthcare funded PhD Studentshi

Quantitative Magnetic Resonance Imaging Techniques for the Measurement of Organ Fat and Body Composition - Validation and Initial Clinical Utility

Ectopic fat is defined by excess deposition of triglycerides in non-adipose tissues that normally contain only small amounts of fat. Measuring the distribution of ectopic fat is important for understanding the pathogenesis of diseases such as obesity and type 2 diabetes mellitus (T2DM) and understanding variation in treatment response amongst patients. Body composition (the proportion of fat and lean mass in the body) is thought to influence both the development of T2DM and outcomes for treatments such as weight-loss surgery. It can also affect clinical outcomes in chronic diseases and malignancy. Quantitative magnetic resonance imaging (qMRI) enables objective measurements of tissue characteristics to be made directly from acquired data. In this thesis, a qMRI protocol based on chemical shift-encoded (CSE)-MRI, specifically the derived proton density fat fraction (PDFF) measurements, was validated against phantoms, and in volunteers and patients with obesity. A new, semi-automated tool for measurement of body composition from CSE-MRI images was developed and validated. CSE-MRI was used to quantify ectopic organ fat depots and body composition in diseases including obesity, T2DM and cancer. Specifically, differences in organ fat between patients with and without remission of T2DM after bariatric surgery was explored. Body composition was investigated in T2DM remission and it was also compared between patients with colorectal and lung cancer undergoing whole body MRI staging. Data from the pilot phase of a study investigating a new duodenal surfacing procedural treatment for T2DM (Revita-2) is presented, demonstrating the utility of hepatic fat content measured using PDFF as an endpoint in an international, multi-centre clinical trial. Finally, I describe the development of a novel technique for quantification of bone mineral density (BMD) using CSE-MRI techniques. The methodology and tools described in this thesis could be used to measure ectopic fat and body composition in future studies and have the potential for integration into clinical care pathways

Joint Analysis of PET/MR Data for Improved PET Quantification

Quantitative pharmacokinetic analysis of Positron Emission Tomography (PET) data typically requires a dynamic scan of at least one hour, which poses a challenge for both clinical and research studies. Instead, in standard practice, a static 10 minute scan is used to calculate the standardised uptake value ratio (SUVR). SUVR approximates tracer binding but is biased by blood flow changes, rendering it unsuitable for longitudinal studies. In this thesis, the availability of magnetic resonance imaging (MRI) data, simultaneously acquired from a PET-MR scanner is exploited to reduce the time required for accurate PET quantification. The main body of this work comprises the development of a framework to incorporate blood flow information from arterial spin labelled (ASL) MRI data into the existing simplified reference tissue model (SRTM) to replace the early phase of the PET data, reducing the acquisition time. This reduced acquisition time (RT-) SRTM was evaluated on [18F]-florbetapir data for the estimation of both regional average and voxelwise amyloid burden (BPND), and was validated against the gold standard BPND using a 60 minute scan. The first step of the RT-SRTM requires the PET tracer delivery parameter, R1, to be estimated from the ASL cerebral blood flow (CBF) maps. Several methods were evaluated: linear regression using region as a covariate, multi-atlas propagation with image fusion, and deep learning based regression using a convolutional neural network. The RT-SRTM was shown to facilitate accurate regional voxelwise quantification in half the acquisition time (30 minutes). Additionally, deep learning based regression was used to learn the model which maps ASL-CBF and dynamic PET data to BPND in a single step (SSDL). The SS-DL model exploits all available information, and avoids noise sensitive voxelwise fitting. This allows the acquisition time to be cut to 15 minutes, and facilitates accurate voxelwise BPND quantification on a timescale manageable for almost all patients and studies

Development and Optimization of 19F-MRI for Tracking Cellular Therapeutics

Introduction: This thesis aims to advance magnetic resonance imaging (MRI) for imaging cellular therapeutics. Traditional, proton-based, MRI provides detailed anatomical images, particularly of soft tissue. However, in order to obtain information at a cellular level specialized imaging agents are required to detect the cells of interest. Perfluorocarbons containing non-radioactive fluorine-19 (19F) are both biologically safe and MR sensitive. Methods: Pre-clinical 19F-MRI was implemented on a Varian 9.4T MRI scanner, using a dual 19F/1H-tuned birdcage volume coil. Mesenchymal stromal cells (MSC) were pre-labeled with a commercial, FDA approved 19F-perfluorocarbon emulsion, then implanted intramuscularly into the mouse hindlimb. To track the inflammation resulting from transplantation, a dual-agent cellular MRI technique was developed. This technique utilizes 19F to track MSC and superparamagnetic iron oxide nanoparticles (SPIO) to image macrophages, through the presence of signal quenching. A clinical imaging protocol was developed to translate 19F-MRI on a 3T GE MR750 scanner with a dual 19F/1H-tuned surface coil. Peripheral blood mononuclear cells (PBMC) were labeled with a FDA-approved 19F-agent and injected into a ham shank phantom for protocol optimization. Results: The balanced steady-state free precession pulse sequence was chosen for all studies due to the high signal-to-noise per unit time. Image acquisition was optimized for 19F detection sensitivity, accuracy of quantification, and compatibility with isoflurane. In vivo quantification of MSC on the day of implantation was in strong agreement with the expected number of cells. The change in 19F-signal was quantified over time and compared between two murine transplantation models. When iron oxide was administered i.v., the migration of immune cells could be tracked to the injection site. The presence of SPIO decreased both the 1H and 19F signal, indicating that transplant rejection was occurring. On a clinical system, as few as 4x106 PBMC could be imaged following both surface and subcutaneous injection. The minimum number of detectable cells was strongly influenced by intracellular 19F uptake. Conclusions: 19F-MRI is a promising tool for imaging cellular therapeutics. By pre-labeling cells of interest, they can be localized and the change in signal can be quantified over time. The technique shows promise for both pre-clinical and clinical applications