| dc.description.abstract | Achieving micron resolutions in magnetic resonance imaging is constrained first by limitations in available signal strength as voxel sizes decrease, and second, by acceptable acquisition times due to the large data sets required. The latter is problematic due to an increased sensitivity to patient bulk motion and physiological effects, and prevalence of distortion and blurring artifacts caused by susceptibility variation. Signal constraints can be mitigated using ultra-high field strengths, such as 7T, but face field dependent challenges such as increased B1 inhomogeneity and shorter T2* values. Scan times can be minimized using reduced field-of-view (FOV) imaging techniques that localize excitations to smaller regions of an object to achieve diminished imaging dimensions, but have largely been unexplored at 7T.
To address this deficiency with the goal of improving human imaging resolutions, this thesis first implements and compares multiple reduced-FOV methods at 7T, assessing relative ability to localize excitation, suppress unwanted signal, minimize artifacts, and constrain power deposition. Inner-Volume Imaging (IVI) and Outer-Volume Suppression (OVS) methods optimized from this comparison are then synergistically combined with rapid parallel and echo planar imaging (EPI) techniques to obtain 160 to 500 μm2 in vivo images throughout the human brain in 3 to 12 minutes, accelerated 160 to 1400 fold for multi-slice and 3D scans, respectively. Compared to full-FOV scans, this approach demonstrates reduced geometric distortion and motion artifacts, with improved visibility of features at the high resolution. The parallel reduced-FOV method is similarly applied for diffusion tensor and cervical spine imaging prone to motion and susceptibility artifacts to obtain 1mm2 DTI images and 300 μm2 in the spine, with localized measurement of diffusion properties. Overall, the reduced-FOV approach provides reduction in scan times, artifact minimization, and achieves resolutions that exceed prior studies. | |
