UCLA

Brain tumors are one of the leading death causes worldwide. Magnetic resonance imaging is the most commonly applied imaging technique for brain tumor detection. However, magnetic resonance imaging loses its power when it comes to detect early stage tumor differs from normal tissue in terms of relaxation properties. To overcome this difficulty in early tumor detection, I investigated chaotic spin dynamics generated by radiation damping and utilized a novel MRI technique called the active-feedback to enhance the imaging contrast between normal tissue and early stage brain tumor. The theme of my Ph.D. thesis is summarized as following:

(1) Fixed Point Imaging with Continuous Wave in Presence of Active-feedback Field. Active-feedback field is a time-dependent magnetic field irradiation inspired by a natural radiation damping. It is shown that active-feedback field yields unique fixed points in spin evolution. Spins are excited to opposite directions by the field until they reach their corresponding fixed points.

(2) Contrast enhancement by selective self-excitation with active-feedback magnetic resonance imaging. It is shown by both theory and simulation that active-feedback magnetic resonance technique generates a selective self-excitation process that selectively excites spins and thus makes this technique very sensitive to local magnetic field inhomogeneity. Active-feedback shows potential in detecting tumors at an early stage.

(3) Local-magnetic-field-dependent active-feedback magnetic resonance imaging and its application in early glioblastoma detection. The active-feedback technique is further developed and an advanced technique called local-field-dependent active-feedback magnetic resonance imaging is introduced. This technique is tested on an early stage glioblastoma multiforme model to show its potential in detecting brain tumors at a very early stage.