All ETDs from UAB

Advisory Committee Chair

Donald B Twieg

Advisory Committee Members

Narasimha A Akella

Stanley J Reeves

Document Type


Date of Award


Degree Name by School

Master of Science in Biomedical Engineering (MSBME) School of Engineering


A novel and recently developed fast magnetic resonance imaging (MRI) technique, Single-Shot Parameter Assessment by Retrieval from Signal Encoding (SSPARSE), promises significant improvements in robustness and accuracy of local signal parameter estimates compared to the conventional MRI methods. In using a more accurate signal model, the reconstruction for SS-PARSE differs from the traditional Fourier transform method. An iterative estimation algorithm, progressive length conjugate gradient (PLCG), is currently employed by SS-PARSE to reconstruct independent parameter maps of local magnetization, transverse relaxation rate and frequency. In practice, the large number of degrees of freedom and partial poor conditioning of the problem itself brings tremendous difficulties for PLCG convergence. In this thesis, investigations of the PLCG convergence were executed using simulations and experiments. Simulation studies were performed on a numerical clock shaped phantom to explore the convergence envelope, which contains the successfully “converged” parameter values but not the partially or “non-converged” parameter values. Also, various PLCG control parameter settings are tested, and the best setting in terms of the convergence envelope is a steadily increasing data length with a small regularization operator. Experimental four-tube phantom results confirmed the convergence characteristics predicted by simulation studies. Further, a signal-area based clustering iii method is presented which segments and eliminates background noise and unreliable estimates where local convergence failed.

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