Fast Magnetic Resonance Imaging by Data Sharing: Generalized Series Imaging and Parallel Imaging

The second part of this thesis addresses the problem of data acquisition and image reconstruction in data-sharing imaging. Specifically, two imaging techniques based on a generalized series (GS) model and a parallel data acquisition strategy are developed to improve imaging speed with a minimal concomitant loss of image quality. First, a new GS imaging method is presented which can effectively share information between different time frames of an image sequence. By optimizing the data acquisition scheme and the image reconstruction algorithm, we have successfully addressed the challenging problem of capturing transient dynamic features in the presence of nonrigid-body motions. Second, a new algorithm is proposed for image reconstruction from undersampled data acquired using multiple phased-array receiver coils. Specifically, wavelet denoising and motion compensation techniques are used to make the parallel imaging model more accurate. A support vector machine (SVM) regression method is developed to overcome the ill-conditioning of the model matrix. An adaptive scheme for choosing the regularization parameters is also proposed. Experimental results show that the proposed algorithm can reconstruct images with fewer artifacts and higher signal-to-noise ratios. This is particularly useful when data-sharing imaging is pushed to achieve very large acceleration factors.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Ji, Jim Xiuquan

Contributors dc:contributor

• Liang, Zhi-Pei

• Engineering, Biomedical

Language dc:language

eng

Identifier

(MiAaPQ)AAI3101872

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/80837