Magnetic resonance flow imaging techniques provide quantitative and qualitative information that can be attributed to flow related clinical pathologies. Clinical use of MR flow quantification requires fast acquisition and reconstruction schemes, and minimization of post processing errors. The purpose of this work is to provide improvements to the post processing of volumetric phase contrast MRI (PCMRI) data, identify a source of flow bias for cine PCMRI that has not been previously reported in the literature, and investigate a dynamic approach to image bulk cerebrospinal fluid (CSF) drainage in ventricular shunts. The proposed improvements are implemented as three research projects.

In the first project, the improvements to post processing are made by proposing a new approach to estimating noise statistics for a single spiral acquisition, and using the estimated noise statistics to generate a mask distinguishing flow regions from background noise and static tissue in an image volume. The mask is applied towards reducing the computation time of phase unwrapping. The proposed noise estimation is shown to have comparable noise statistics as that of a vendor specific noise dynamic scan, with the added advantage of reduced scan time. The sparse flow region subset of the image volume is shown to speed up phase unwrapping for multidirectional velocity encoded 3D PCMRI scans. The second research project explores the extent of bias in cine PCMRI based flow estimates is investigated for CSF flow in the cerebral aqueduct. The dependance of the bias on spatial and temporal velocity gradient components is described. A critical velocity threshold is presented to prospectively determine the extent of bias as a function of scan acquisition parameters.

Phase contrast MR imaging is not sensitive to measure bulk CSF drainage. A dynamic approach using a CSF label is investigated in the third project to detect bulk flow in a ventricular shunt. The proposed approach uses a preparatory pulse to label CSF signal and a variable delay between the preparatory pulse and data acquisition enables tracking of the CSF bulk flow.

Dynamic susceptibility contrast MRI (DSC-MRI) is a powerful tool used to quantitatively measure parameters related to blood flow and volume in the brain. The technique is known as a “bolus-tracking” method and relies upon very fast scanning to accurately measure the flow of contrast agent into and out of a region of interest. The need for high temporal resolution to measure contrast agent dynamics limits the spatial coverage of perfusion parameter maps which limits the utility of DSC-perfusion studies in pathologies involving the entire brain. Typical clinical DSC-perfusion studies are capable of acquiring 10-15 slices, generally centered on a known lesion or pathology.

The methods developed in this work improve the spatial coverage of whole-brain DSC-MRI by combining a highly efficient 3D spiral k-space trajectory with Generalized Autocalibrating Partial Parallel Acquisition (GRAPPA) parallel imaging without increasing temporal resolution. The proposed method is capable of acquiring 30 slices with a temporal resolution of under 1 second, covering the entire cerebrum with isotropic spatial resolution of 3 mm. Additionally, the acquisition method allows for correction of T1-enhancing leakage effects by virtue of collecting two echoes, which confound DSC perfusion measurements. The proposed DSC-perfusion method results in high quality perfusion parameter maps across a larger volume than is currently available with current clinical standards, improving diagnostic utility of perfusion MRI methods, which ultimately improves patient care.

Magnetic Resonance Imaging using spiral trajectories has many advantages in speed, efficiency in data-acquistion and robustness to motion and flow related artifacts. The increase in sampling speed, however, requires high performance of the gradient system. Hardware inaccuracies from system delays and eddy currents can cause spatial and temporal distortions in the encoding gradient waveforms. This causes sampling discrepancies between the actual and the ideal k-space trajectory. Reconstruction assuming an ideal trajectory can result in shading and blurring artifacts in spiral images. Current methods to estimate such hardware errors require many modifications to the pulse sequence, phantom measurements or specialized hardware. This work presents a new method to estimate time-varying system delays for spiral-based trajectories. It requires a minor modification of a conventional stack-of-spirals sequence and analyzes data collected on three orthogonal cylinders. The method is fast, robust to off-resonance effects, requires no phantom measurements or specialized hardware and estimate variable system delays for the three gradient channels over the data-sampling period. The initial results are presented for acquired phantom and in-vivo data, which show a substantial reduction in the artifacts and improvement in the image quality.

The aim of this study was to investigate the microstructural sensitivity of the statistical distribution and diffusion kurtosis (DKI) models of non-monoexponential signal attenuation in the brain using diffusion-weighted MRI (DWI). We first developed a simulation of 2-D water diffusion inside simulated tissue consisting of semi-permeable cells and a variable cell size. We simulated a DWI acquisition using a pulsed gradient spin echo (PGSE) pulse sequence, and fitted the models to the simulated DWI signals using b-values up to 2500 s/mm2. For comparison, we calculated the apparent diffusion coefficient (ADC) of the monoexponential model (b-value = 1000 s/mm2). In separate experiments, we varied the cell size (5-10-15 μ), cell volume fraction (0.50-0.65-0.80), and membrane permeability (0.001-0.01-0.1 mm/s) to study how the fitted parameters tracked simulated microstructural changes. The ADC was sensitive to all the simulated microstructural changes except the decrease in membrane permeability. The σstat of the statistical distribution model increased exclusively with a decrease in cell volume fraction. The Kapp of the DKI model increased exclusively with decreased cell size and decreased with increasing membrane permeability. These results suggest that the non-monoexponential models have different, specific microstructural sensitivity, and a combination of the models may give insights into the microstructural underpinning of tissue pathology. Faster PROPELLER DWI acquisitions, such as Turboprop and X-prop, remain subject to phase errors inherent to a gradient echo readout, which ultimately limits the applied turbo factor and thus scan time reductions. This study introduces a new phase correction to Turboprop, called Turboprop+. This technique employs calibration blades, which generate 2-D phase error maps and are rotated in accordance with the data blades, to correct phase errors arising from off-resonance and system imperfections. The results demonstrate that with a small increase in scan time for collecting calibration blades, Turboprop+ had a superior immunity to the off-resonance related artifacts when compared to standard Turboprop and recently proposed X-prop with the high turbo factor (turbo factor = 7). Thus, low specific absorption rate (SAR) and short scan time can be achieved in Turboprop+ using a high turbo factor, while off-resonance related artifacts are minimized.

The goal of the works presented in this volume is to develop a magnetic resonance imaging (MRI) probe for non-invasive detection of extracellular matrix (ECM) underlying fenestrated endothelia. The ECM is the scaffold that supports tissue structure in all organs. In fenestrated structures the such as the kidney glomerulus and the hepatic sinusoid the ECM serves a unique role in blood filtration and is directly exposed to blood plasma. An assessment of the ECM in fenestrated organs such as the kidney and liver reports on the organ's ability to filter blood - a process critical to maintaining homeostasis. Unfortunately, clinical assessment of the ECM in most organs requires biopsy, which is focal and invasive. This work will focus on visualizing the ECM underlying fenestrated endothelia with natural nanoparticles and MRI. The superparamagnetic ferritin protein has been proposed as a useful naturally-derived, MRI-detectable nanoparticle due to its biocompatibility, ease of functionalization, and modifiable metallic core. We will show that cationized ferritin (CF) specifically binds to the anionic proteoglycans of the ECM underlying fenestrated endothelia and that its accumulation is MRI-detectable. We will then demonstrate the use of CF and MRI in identifying and measuring all glomeruli in the kidney. We will also explore the toxicity of intravenously injected CF and consider other avenues for its application, including detection of microstructural changes in the liver due to chronic liver disease. This work will show that CF is useful in detected fenestrated microstructures in small animals and humans alike, indicating that CF may find broad application in detecting and monitoring disease in both preclinical and clinical settings.

Phase contrast magnetic resonance angiography (PCMRA) is a non-invasive imaging modality that is capable of producing quantitative vascular flow velocity information. The encoding of velocity information can significantly increase the imaging acquisition and reconstruction durations associated with this technique. The purpose of this work is to provide mechanisms for reducing the scan time of a 3D phase contrast exam, so that hemodynamic velocity data may be acquired robustly and with a high sensitivity. The methods developed in this work focus on the reduction of scan duration and reconstruction computation of a neurovascular PCMRA exam. The reductions in scan duration are made through a combination of advances in imaging and velocity encoding methods. The imaging improvements are explored using rapid 3D imaging techniques such as spiral projection imaging (SPI), Fermat looped orthogonally encoded trajectories (FLORET), stack of spirals and stack of cones trajectories. Scan durations are also shortened through the use and development of a novel parallel imaging technique called Pretty Easy Parallel Imaging (PEPI). Improvements in the computational efficiency of PEPI and in general MRI reconstruction are made in the area of sample density estimation and correction of 3D trajectories. A new method of velocity encoding is demonstrated to provide more efficient signal to noise ratio (SNR) gains than current state of the art methods. The proposed velocity encoding achieves improved SNR through the use of high gradient moments and by resolving phase aliasing through the use measurement geometry and non-linear constraints.

Magnetic resonance (MR) imaging with data acquisition on a non-rectangular grid permits a variety of approaches to cover k-space. This flexibility can be exploited to achieve clinically relevant characteristics -- fast yet full coverage for short scan times, center out schemes for short Te, over-sampled k-space for robustness to motion, long acquisition time for improved signal-to-noise (SNR) performance and benign under-sampling (aliasing) artifact. This dissertation presents advances in Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) trajectory design and improved reconstruction for spiral imaging. Scan time in PROPELLER imaging can be reduced by tailoring the trajectory to the required Field-Of-View (FOV). A technique to design the PROPELLER trajectory for an elliptical FOV is described. The proposed solution is a set of empirically derived closed form equations that preserve the standard PROPELLER geometry and specify the minimum number of blades necessary. Reconstructing spiral scans requires accurate trajectory information. A simple method to measure the deviation from the designed trajectory due to gradient coupling is presented. A line phantom is used to force a uniform structure in a predetermined orientation in k-space. This uniformity permits measurements of zeroth order trajectory deviations due to gradient coupling. Spiral reconstruction is also sensitive to B0 inhomogeneities (variations in the external magnetic field). This sensitivity manifests itself as a spatially varying blur. An algorithm to correct for concomitant field and first order B0 inhomogeneity effects is developed based on de-blurring via convolution by separable kernels. To reduce computation time, an empirical equation for sufficient kernel length is derived. It is also necessary to know the noise characteristics of the proposed algorithm; this is investigated via Monte-Carlo simulations. The algorithm is further extended to correct for concomitant field artifacts by modeling these artifacts as blurring due to a temporally static field map. This approach has the potential for further reduction in computational cost by combining the B0 map with the concomitant field map to simultaneously correct for artifacts resulting from both field inhomogeneities and concomitant field map.