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Description
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.
ContributorsBhavsar, Payal (Author) / Pipe, James G (Thesis advisor) / Frakes, David (Committee member) / Kodibagkar, Vikram (Committee member) / Arizona State University (Publisher)
Created2013
Description
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.
ContributorsLee, Chu-Yu (Author) / Debbins, Josef P (Thesis advisor) / Bennett, Kevin M (Thesis advisor) / Karam, Lina (Committee member) / Pipe, James G (Committee member) / Arizona State University (Publisher)
Created2012
Description
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.
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.
ContributorsRagunathan, Sudarshan (Author) / Pipe, James G (Thesis advisor) / Frakes, David (Thesis advisor) / Kodibagkar, Vikram (Committee member) / Sadleir, Rosalind (Committee member) / Hu, Houchun (Committee member) / Arizona State University (Publisher)
Created2017