Lung Imaging and Function Assessment using Non-Contrast-Enhanced Magnetic Resonance Imaging

Measurement of pulmonary ventilation and perfusion has significant clinical value for the diagnosis and monitoring of prevalent lung diseases. To this end, non-contrast-enhanced MRI techniques have emerged as a promising alternative to scintigraphical measurements, computed tomography, and contrast-enhanced MRI. Although these techniques allow the acquisition of both structural and functional information in the same scan session, they are prone to robustness issues related to imaging artifacts and post-processing techniques, limiting their clinical utilization. In this work, new acquisition and post-processing techniques were introduced for improving the robustness of non-contrast-enhanced MRI based functional lung imaging. Furthermore, pulmonary functional maps were acquired in 2-year-old congenital diaphragmatic hernia (CDH) patients to demonstrate the feasibility of non-contrast-enhanced MRI methods for functional lung imaging. In the first study, a multi-acquisition framework was developed to improve robustness against field inhomogeneity artifacts. This method was evaluated at 1.5T and 3T field strengths via acquisitions obtained from healthy volunteers. The results demonstrate that the proposed acquisition framework significantly improved ventilation map homogeneity p<0.05. In the second study, a post-processing method based on dynamic mode decomposition (DMD) was developed to accurately identify dominant spatiotemporal patterns in the acquisitions. This method was demonstrated on digital lung phantoms and in vivo acquisitions. The findings indicate that the proposed method led to a significant reduction in dispersion of estimated ventilation and perfusion map amplitudes across different number of measurements when compared with competing methods p<0.05. In the third study, the free-breathing non-contrast-enhanced dynamic acquisitions were obtained from 2-year-old patients after CDH repair, and then processed using the DMD to obtain pulmonary functional maps. Afterwards, functional differences between ipsilateral and contralateral lungs were assessed and compared with results obtained using contrast-enhanced MRI measurements. The results demonstrate that pulmonary ventilation and perfusion maps can be generated from dynamic acquisitions successfully without the need for ionizing radiation or contrast agents. Furthermore, lung perfusion parameters obtained with DMD MRI correlate very strongly with parameters obtained using dynamic contrast-enhanced MRI. In conclusion, the presented work improves the robustness and accuracy of non-contrast-enhanced functional lung imaging using MRI. Overall, the methods introduced in this work may serve as a valuable tool in the clinical adaptation of non-contrast-enhanced imaging methods and may be used for longitudinal assessments of pulmonary functional changes

Quantitative rotating frame relaxometry methods in MRI

Macromolecular degeneration and biochemical changes in tissue can be quantified using rotating frame relaxometry in MRI. It has been shown in several studies that the rotating frame longitudinal relaxation rate constant (R1ρ) and the rotating frame transverse relaxation rate constant (R2ρ) are sensitive biomarkers of phenomena at the cellular level. In this comprehensive review, existing MRI methods for probing the biophysical mechanisms that affect the rotating frame relaxation rates of the tissue (i.e. R1ρ and R2ρ) are presented. Long acquisition times and high radiofrequency (RF) energy deposition into tissue during the process of spin-locking in rotating frame relaxometry are the major barriers to the establishment of these relaxation contrasts at high magnetic fields. Therefore, clinical applications of R1ρ and R2ρ MRI using on- or off-resonance RF excitation methods remain challenging. Accordingly, this review describes the theoretical and experimental approaches to the design of hard RF pulse cluster- and adiabatic RF pulse-based excitation schemes for accurate and precise measurements of R1ρ and R2ρ. The merits and drawbacks of different MRI acquisition strategies for quantitative relaxation rate measurement in the rotating frame regime are reviewed. In addition, this review summarizes current clinical applications of rotating frame MRI sequences. © 2016 John Wiley & Sons, Ltd

Optimization of Design Procedures for Delta Relaxation Enhanced Magnetic Resonance

Delta relaxation enhanced magnetic resonance (dreMR) is a magnetic resonance imaging (MRI) method that produces contrast based on longitudinal relaxation dispersion. Through modulation of the magnetic field using an actively-shielded, field-cycling insert coil, this technique increases probe specificity and suppresses remaining signal. However, significant improvements are needed. This thesis addresses two advancements in dreMR with a focus on optimizing design procedures. A general procedure was developed to design split power solenoid magnets. The procedure was then applied to the design of a switched-field exposure system. A coil was constructed and the method was validated. This procedure can be used for to optimize dreMR coil primary windings. Next, a simulation tool was developed to model tissue magnetization as a function of time and magnetic field. Polarization sequences were discovered that maximize dispersion-based contrast. These optimized design procedures may add to future developments in dreMR technology

Multi-component MRI transverse-relaxation parameter estimation to detect and monitor neuromuscular disease

We aimed to optimise the estimation of skeletal muscle-water spin-spin relaxation time (T2m), and fat fraction estimated from multi-echo MRI, as potential biomarkers, by accounting for instrumental factors such as B1 errors, non-Gaussian noise and non-ideal echo train evolution. A multi-component slice-profile-compensated extended phase graph (sEPG) model for multi-echo Carr-Purcell-Meiboom-Gill (CPMG) spin-echo sequence signals was implemented, modelling the fat signal as two empirically calibrated sEPG components with fixed parameters, and the remaining unknown parameters (B1 field factor, T2m, fat fraction (ffa), global amplitude and Rician noise SD) determined by maximum likelihood estimation. After validation using a calibrated test object the algorithm was used to analyse clinical muscle study data from patient groups with amyotrophic lateral sclerosis (ALS), Kennedy’s disease (KD) and Duchenne muscular dystrophy (DMD) and matched healthy controls. Parameter maps were generated using quality control steps to reject pixels failing fit quality or physical meaningfulness criteria. Muscle fat-fraction was also determined independently by 3-point Dixon MRI (ffd). In ALS and KD median T2m were significantly elevated compared with healthy controls in varied patterns and time courses, whereas it was decreased in DMD; other T2m distribution histogram metrics such as the skewness and full width at quarter maximum also differed significantly between patients and healthy volunteers. Quantitative comparison of ffa and ffd in the same muscles revealed a monotonic relationship deviating from linearity due to differing deviations from the assumed ideal signal behaviour in each method. Finally, the effects upon estimation accuracy and precision of practically realisable pulse sequence parameter choices were explored in simulations and with real data. Recommendations are presented for optimal choices. Clinically practical conventional CPMG sequences, combined with an appropriate signal model and parameter estimation method can provide robust T2m and ffa measures which change in disease and may sensitively reflect different aspects of neuromuscular pathology

Characterization of the BOLD signal in functional MRI

In the last two decades, functional magnetic resonance imaging (fMRI) has become an important and widely used imaging technique for functional brain mapping. However, blood oxygen level dependent (BOLD) technique is quite insensitive and task invoked BOLD signal change at 3T is typically in the order of a few percent. Furthermore, the coupling between BOLD signal changes and neuronal activities is quite complicated, involving a cascade of events remaining poorly understood even today. In this thesis, some of the basic characteristics of the BOLD signal are investigated. Better understanding of the BOLD signal characteristics can be beneficial for the design of BOLD fMRI experiment aimed to improve the time efficiency. It can also provide guidelines for developing fMRI data processing strategies. In study I and II, a single-shot dual-echo spiral acquisition technique was used for characterizing the T2* changes associated with motor activation task. In study I, the optimal strategy for head motion correction was investigated. Based on the improvement in the detection of brain activation, the best strategy is to perform the head motion correction using the imaging data from the second echo and then apply the derived motion correction parameters to the first echo, instead of conducting motion correction of the individual echoes independently. In study II, several aspects of brain mapping methods based on T2*-weighted imaging and T2* (R2*=1/T2*) mapping were quantitatively compared, including the detected activation volume, functional contrast, signal-to-noise ratio, and contrast-to-noise ratio. fMRI studies based T2* mapping have the following potential advantages: maximum functional contrast, independence of echo time; and reduced inflow effects. The sensitivity for brain activation detection is significantly correlated with the contrast-to-noise ratio, which is determined by both the signal-to-noise ratio and functional contrast. In study III, the hemodynamic responses to functional activation were characterized using T2*-weighted BOLD imaging, arterial spin labeling, and bolus tracking of MRI contrast agent. In addition to the BOLD signal change, the relative cerebral blood flow and cerebral blood volume associated with brain activation were independently determined. In study IV, the characteristics of the global signal in resting-state fMRI were investigated. It was found that the global signal time courses and regional contributions differ individually. However, after removing the contribution from the cerebral spinal fluid, a consistent brain network responsible for the remaining global signal changes was identified. The involved brain regions include: posterior cingulate cortex, precuneus, superior temporal gyrus, medial frontal gyrus and the cerebellar vermis, which is likely to be related to the perception and cognitive processes of the brain occurred in the specific environments during resting-state fMRI

Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning

Deep learning (DL) has emerged as a leading approach in accelerating MR imaging. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MR imaging involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MR imaging along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.Comment: 46 pages, 5figures, 1 tabl

Spectral estimation with spatio-spectral constraints for magnetic resonance spectroscopic imaging

Magnetic resonance spectroscopic imaging (MRSI) is a promising tool to acquire in vivo biochemical information, and spectral estimation (quantification) of MRSI data is an important step towards quantitative studies. Although a large body of work has been done on spectral estimation over the past decades, it remains challenging due to model nonlinearity and extremely low signal-to-noise ratio (SNR). Building on the existing methods which effectively incorporate spectral prior knowledge in the form of basis functions, this work addresses the spectral estimation problem by incorporating both spectral and spatial prior information. Specifically, we jointly estimate the spectra over all the voxels of interest, incorporating prior spatial information in a regularization framework. The effectiveness of the proposed method has been evaluated using both simulated and experimental data. A theoretical analysis based on Cramer-Rao Bound is proposed to further assess the performance improvement of the proposed method over state-of-the-art methods. The proposed spectral estimation method should prove useful in various MRSI studies.Ope

Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure

Magnetic resonance spectroscopic imaging (MRSI) is a rapidly developing medical imaging modality, capable of conferring both spatial and spectral information content, and has become a powerful clinical tool. The ability to non-invasively observe spatial maps of metabolite concentrations, for instance, in the human brain, can offer functional, as well as pathological insights, perhaps even before structural aberrations or behavioral symptoms are evinced. Despite its lofty clinical prospects, MRSI has traditionally remained encumbered by a number of practical limitations. Of primary concern are the vastly reduced concentrations of tissue metabolites when compared to that of water, which forms the basis for conventional MR imaging. Moreover, the protracted exam durations required by MRSI routinely approach the limits for patient compliance. Taken in conjunction, the above considerations effectively circumscribe the data collection process, ultimately translating to coarse image resolutions that are of diminished clinical utility. Such shortcomings are compounded by spectral contamination artifacts due to the system pointspread function, which arise as a natural consequence when reconstructing non-band-limited data by the inverse Fourier transform. These artifacts are especially pronounced near regions characterized by substantial discrepancies in signal intensity, for example, the interface between normal brain and adipose tissue, whereby the metabolite signals are inundated by the dominant lipid resonances. In recent years, concerted efforts have been made to develop alternative, non-Fourier MRSI reconstruction strategies that aim to surmount the aforementioned limitations. In this dissertation, we build upon the burgeoning medley of innovative and promising techniques, proffering a novel superresolution reconstruction framework predicated on the recent interest in low-rank signal modeling, along with state-of-the-art regularization methods. The proposed framework is founded upon a number of key tenets. Firstly, we proclaim that the underlying spatio-spectral distribution of the investigated object admits a bilinear representation, whereby spatial and spectral signal components can be effectively segregated. We further maintain that the dimensionality of the subspace spanned by the components is, in principle, bounded by a modest number of observable metabolites. Secondly, we assume that local susceptibility effects represent the primary sources of signal corruption that tend to disallow such representations. Finally, we assert that the spatial components belong to a class of real-valued, non-negative, and piecewise linear functions, compelled in part through the use of a total variation regularization penalty. After demonstrating superior spatial and spectral localization properties in both numerical and physical phantom data when compared against standard Fourier methods, we proceed to evaluate reconstruction performance in typical in vivo settings, whereby the method is extended in order to promote the recovery of signal variations throughout the MRSI slice thickness. Aside from the various technical obstacles, one of the cardinal prospective challenges for high-resolution MRSI reconstruction is the shortfall of reliable ground truth data prudent for validation, thereby prompting reservations surrounding the resulting experimental outcomes. [...