Date of Degree
PhD (Doctor of Philosophy)
Electrical and Computer Engineering
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Obtaining high spatial or spatiotemporal resolution along with good slice coverage is challenging in dynamic magnetic resonance imaging, MRI, due to the slow nature of the acquisition process. In recent years, there has been a rapid growth of MRI techniques that allow faster scan speed by exploiting spatial or spatiotemporal redundancy of the images. These techniques can improve the performance of imaging significantly across multiple clinical applications, including cardiac functional examinations, perfusion imaging, blood flow assessment, contrast-enhanced angiography, functional MRI, and interventional imaging, among others.
The ultimate goal of this thesis is to develop novel algorithms to reconstruct heavily undersampled sparse imaging. The designed schemes aim to achieve a shorter scan duration, higher spatial resolution, increased temporal resolution, signal-to-noise ratio and coverage in multidimensional multichannel MRI. In addition to improving patients comfort and compliance while imaging under the MRI device, the newly developed schemes will allow patients with arrhythmia problems, pediatric and obese subjects to breath freely without the need for any breath-hold scans. Shortening examination periods also reduces patient's stress, lowers the entire visit to the clinic and finally decreases the associated economic costs. Rapid imaging acquisitions will also allow for efficient extraction of quantitative information needed for the patients' diagnosis eg. tumor characterization and veins blockages through myocardial perfusion MRI. Current applications of interests include real-time CINE MRI and contrast changing perfusion MRI.
Image reconstruction, MRI, Shrinkage
xvi, 87 pages
Includes bibliographical references (pages 80-87).
Copyright © 2018 Yasir Qasim Mohsin
Mohsin, Yasir Qasim. "Novel MR image recovery using patch-smoothness iterative shrinkage algorithm." PhD (Doctor of Philosophy) thesis, University of Iowa, 2018.