AuthorGraff, Christian George
Committee ChairClarkson, Eric W.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractThis work concerns practical quantitative magnetic resonance (MR) imaging techniques and their implementation and use in clinical MR systems. First, background information on MR imaging is given, including the physics of the magnetic resonance, relaxation effects and how imaging is accomplished.Subsequently, the first part of this work describes the estimation of the T2 relaxation parameter from fast spin-echo (FSE) data. Various complications are considered, including partial volume and data from multiple receiver coils along with the effects of the timing parameters on the accuracy of T2 estimates. Next, the problem of classifying small (1 cm diameter) liver lesions using T2 estimates obtained from radially-acquired FSE data collected in a single breath-hold is considered. Several algorithms are proposed for obtaining lesion T2 estimates, and these algorithms are evaluated with a task-based metric, their ability to separate two classes of lesions, benign and malignant. A novel computer-generated phantom is developed for the generation of the data used in this evaluation.The second part of this work describes techniques that perform the separation of water and lipid signals while simultaneously estimating relaxation parameters that have clinical relevance. The acquisition sequences used here are Cartesian and radial versions of Gradient and Spin-Echo (GRASE). The radial GRASE technique is post-processed with a novel algorithm that estimates the T2 of the water signal independent of the lipid signal. The accuracy of this algorithm is evaluated in phantom and its potential use for detecting inflammation of the liver is evaluated using clinical data. Cartesian GRASE data is processed to obtain T2-dagger and lipid fraction estimates in bone which can be used to assess bone quality. The algorithm is tested in phantom and in vivo, and preliminary results are given.In the concluding chapter results are summarized and directions for future work are indicated.
Degree ProgramApplied Mathematics