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A subspace approach to spectral quantification for MR spectroscopic imaging
Li, Yudu
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https://hdl.handle.net/2142/99360
Description
- Title
- A subspace approach to spectral quantification for MR spectroscopic imaging
- Author(s)
- Li, Yudu
- Issue Date
- 2017-12-04
- Director of Research (if dissertation) or Advisor (if thesis)
- Liang, Zhi-Pei
- Department of Study
- Electrical & Computer Eng
- Discipline
- Electrical & Computer Engr
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- M.S.
- Degree Level
- Thesis
- Date of Ingest
- 2018-03-13T15:48:51Z
- Keyword(s)
- Magnetic resonance spectroscopic imaging (MRSI)
- Spectral estimation
- Subspace
- Spatiospectral constraints
- Abstract
- The problem of spectral quantification for magnetic resonance spectroscopic imaging (MRSI) is addressed in this thesis. We present a novel approach to solving this problem, incorporating both spatial and spectral prior information. More specifically, a new signal model is proposed which represents the spectral variations of each molecule as a subspace and the entire spectrum as a union-of-subspaces. The proposed model enables an efficient computational framework to quantify the unknown spectral parameters using both spectral and spatial prior information. Particularly, based on this model, the spectral quantification can be solved in two steps: (1) subspace estimation based on the empirical distributions of the spectral parameters obtained by initial spectral quantification imposing the spectral constraints, and (2) parameter estimation for the union-of-subspaces model imposing the spatial constraints. The proposed method has been evaluated using both simulated and experimental data, producing very impressive results. The resulting algorithm is expected to be useful for any metabolic imaging studies using MRSI. In this thesis, background materials including a brief review of the existing spectral quantification methods are firstly presented. Then the proposed subspace spectral model is introduced followed by a detailed description of the resulting quantification algorithm. Finally, spectral quantification results from both simulated and in vivo MRSI data are presented to demonstrate the performance of the proposed method.
- Graduation Semester
- 2017-12
- Type of Resource
- text
- Permalink
- http://hdl.handle.net/2142/99360
- Copyright and License Information
- Copyright 2017 Yudu Li
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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at IllinoisDissertations and Theses - Electrical and Computer Engineering
Dissertations and Theses in Electrical and Computer EngineeringManage Files
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