Multi-dimensional Excitation in MRI: New Development and Applications

In magnetic resonance imaging (MRI), multi-dimensional excitation can select signals from a specific spatial region and/or spectral component. The overall goal of this PhD project is to further develop MRI multi-dimensional excitation techniques to enable two new applications: (a) phosphorus (31P) imaging of selected metabolites at an ultra-high magnetic field of 9.4 Tesla (T) on human subjects, and (b) high-resolution high-b-value non-Gaussian diffusion imaging at 3 Tesla on tumor patients. Current in vivo 31P imaging techniques, such as magnetic resonance spectroscopy (MRS) and chemical shift imaging (CSI), are time-consuming ( >30 min) and unable to provide sufficient spatial coverage with adequate spatial resolution. We have developed a novel multi-dimensional excitation MRI technique to selectively excite specific phosphorous metabolites of interest. The images from the selected 31P metabolite (e.g., Phosphocreatine) have been acquired on human subjects within a clinically acceptable time (~10 min) and with adequate spatial coverage (e.g., 24×24×18 cm3) by utilizing the UIC’s state-of-the-art 9.4 T MRI scanner. This constitutes the first specific aim of the project. Second, non-Gaussian diffusion imaging, serving as a potentially powerful tool for probing tissue microstructures and micro-environment, is vulnerable to image distortion and low spatial resolution inherent to diffusion-weighted single-shot echo planar imaging (DW-ssEPI) pulse sequences. We have developed a new pulse sequence strategy by utilizing a novel 2D echo planar radiofrequency (EPRF) pulse with a tilted excitation plane. The new technique is able to zoom in the targeted region in vivo (e.g., the brain stem) with reduced field-of-view (FOV) in order to increase the spatial resolution while decreasing image distortion. The high-resolution and distortion-free diffusion images have been acquired from the brain stem of healthy human subjects and demonstrated on a non-Gaussian diffusion model known as the fractional order calculus (FROC) model. Moreover, we have applied the FROC model on three clinical applications. The new FROC parameters has shown clinical significance in differentiating the brain tumor grades in pediatric and adult patients, and in predicting the response of chemotherapy in gastrointestinal stromal tumors (GIST).