| dc.description.abstract | Spinal Cord Injury (SCI) is a common injury in incorrect sitting position, sports and car accidents. Noninvasive imaging methods play a critical role in diagnosing SCI and monitoring the response to therapy. Magnetic Resonance Imaging (MRI), by the virtue of proving excellent soft tissue contrast, is the most promising imaging method for this application. However, spinal cord has a very small cross-section, which requires high-resolution images to visualize. Unfortunately, acquiring high-resolution spinal cord MRI images requires long acquisition time due to the present physical and physiological constraints. Meanwhile, long acquisition time focusing on Spinal Cord is very challenging to achieve since MRI scanner has high requirement on object’s stability and human body is moveable. In addition, reconstruction of high-resolution images also demands significant computer power and advanced logical algorithm. In this proposed undergraduate research project, we aim to develop and implement new algorithms that allow high-resolution images to be reconstructed from sparsely sampled, non-uniform k-space data that are acquired from parallel receive arrays, which will enable high-resolution MRI of spinal cords without significantly increase the imaging time. | en |
