Document Type


Date of Degree

Spring 2011

Degree Name

PhD (Doctor of Philosophy)

Degree In

Electrical and Computer Engineering

First Advisor

Gary Edward Christensen

First Committee Member

Daniel R Thedens

Second Committee Member

Steve M Collins

Third Committee Member

Milan Sonka

Fourth Committee Member

Vincent A Magnotta


Parallel magnetic resonance imaging (MRI) with non-Cartesian sampling pattern is a promising technique that increases the scan speed using multiple receiver coils with reduced samples. However, reconstruction is challenging due to the increased complexity.

Three reconstruction methods were evaluated: gridding, blocked uniform resampling (BURS) and non-uniform FFT (NUFFT). Computer simulations of parallel reconstruction were performed. Root mean square error (RMSE) of the reconstructed images to the simulated phantom were used as image quality criterion. Gridding method showed best RMSE performance.

Two type of a priori constraints to reduce noise and artifacts were evaluated: edge preserving penalty, which suppresses noise and aliasing artifact in image while preventing over-smoothness, and object support penalty, which reduces background noise amplification. A trust region based step-ratio method that iteratively calculates the penalty coefficient was proposed for the penalty functions. Two methods to alleviate computation burden were evaluated: smaller over sampling ratio, and interpolation coefficient matrix compression. The performance were individually tested using computer simulations. Edge preserving penalty and object support penalty were shown to have consistent improvement on RMSE. The performance of calculated penalty coefficients on the two penalties were close to the best RMSE. Oversampling ratio as low as 1.125 was shown to have impact of less than one percent on RMSE for the radial sampling pattern reconstruction. The value reduced the three dimensional data requirement to less than 1/5 of what the conventional 2x grid needed. Interpolation matrix compression with compression ratio up to 50 percent showed small impact on RMSE.

The proposed method was validated on 25 MR data set from a GE MR scanner. Six image quality metrics were used to evaluate the performance. RMSE, normalized mutual information (NMI) and joint entropy (JE) relative to a reference image from a separate body coil scan were used to verify the fidelity of reconstruction to the reference. Region of interest (ROI) signal to noise ratio (SNR), two-data SNR and background noise were used to validate the quality of the reconstruction. The proposed method showed higher ROI SNR, two-data SNR, and lower background noise over conventional method with comparable RMSE, NMI and JE to the reference image at reduced computer resource requirement.


Iterative method, non-Catresian, Parallel MRI, Reconstruction


xi, 128 pages


Includes bibliographical references (pages 120-128).


Copyright 2011 Xuguang Jiang