| Summary: | 博士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 99 === Magnetic Resonance Imaging (MRI) is a noninvasive and nonradiative medical imaging modality. It has been used in many different fields for research and clinical applications. One of its imaging applications is called functional MRI (fMRI) which can acquire functional brain imaging dynamically with the help of the blood-oxygen-level-dependent (BOLD) contrast generated by external-body stimuli. fMRI has been applied to many areas related to the cognitive neuroscience, ranging from medicine, psychology, economics, and even sociology. Such popularity comes from the advantage that MRI is by far the only noninvasive imaging method acquiring both anatomical and functional information for human brain.
The temporal resolution is crucial to fMRI since the fast transient behavior of the hemodynamic response needs to be captured correctly. Conventional fMRI can achieve the temporal resolution of one to three seconds. Recently-proposed dynamic magnetic resonance (MR) inverse imaging (InI) is a novel parallel imaging reconstruction technique capable of improving the temporal resolution of BOLD contrast fMRI to the order of milliseconds at the cost of moderate spatial resolution. Volumetric InI reconstructs spatial information from projection data by solving ill-posed inverse problems using simultaneous acquisitions from a RF coil array. Previously a spatial filtering technique based on linearly constrained minimum variance (LCMV) beamformer was suggested to localize the hemodynamic changes of dynamic InI data with improved spatial resolution and sensitivity. Here we report an advancement of the spatial filtering method, which combines the eigenspace projection of the measured data and the l1-norm minimization of the spatial filters’ output noise amplitude, to further improve the detection power of BOLD-contrast fMRI data. Using numerical simulation and in vivo data, we demonstrate that this eigen-space linearly constrained minimum amplitude (eLCMA) beamformer can reconstruct spatiotemporal hemodynamic signals with high statistical significance values and high spatial resolution in event-related two-choice reaction time visuomotor experiments.
In this dissertation, we will provide the brief review for MRI, fMRI, InI in Chap.1, and introduce fMRI data acquisition reconstruction in Chap. 2. We then provide the InI reconstruction theory in Chap. 3. We will show the simulation and experimentresults in Chap. 4, and discuss and conclude our study in Chap. 5.
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