A subspace approach to spectral quantification for MR spectroscopic imaging

Li, Yudu

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https://hdl.handle.net/2142/99360 Copy

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

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

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http://hdl.handle.net/2142/99360

Copyright and License Information

Copyright 2017 Yudu Li

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