Simultaneous fMRI and metabolic imaging of the brain using spice

Guo, Rong

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https://hdl.handle.net/2142/102916 Copy

Description

Title

Simultaneous fMRI and metabolic imaging of the brain using spice

Author(s)

Guo, Rong

Issue Date

2018-11-20

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)

• fMRI, metabolic imaging
• SPICE
• sparse sampling
• subspace modeling
• partial separability

Abstract

In this thesis, we propose a novel approach to achieve simultaneous acquisition of high resolution MRSI and fMRI in a fast scan. The proposed acquisition scheme adds an EVI-based sequence module into a subspace-based imaging technique called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation). With the features of ultrashort TE/short TR, no water and lipid suppression, extended k-space coverage by prolonged EPSI readout and highly sparse sampling, the data acquisition captures both the spatiospectral information of brain metabolites and the dynamic information of brain functional activation. The data processing and reconstruction are based on the subspace modeling and involve pre-trained basis functions and spatial prior information. Moreover, the complementary information between fMRI and MRSI is utilized to further improve the quality of both fMRI and metabolic imaging. The in vivo experimental results demonstrate that the proposed method can achieve whole brain covered, simultaneous fMRI at spatial resolution of 3.0 × 3.0 × 1.8 mm, temporal resolution 3 seconds, along with metabolic imaging at nominal spatial resolution of 1.9 × 2.3 × 3.0 mm in a single 6-minute scan. The high-quality metabolic maps, spatially resolved spectra, resting-state functional networks and task time courses corresponding to the task events can all be obtained in the in vivo scans. This technique, when fully developed, will become a powerful tool to study the brain metabolism and function activities.

Graduation Semester

2018-12

Type of Resource

text

Permalink

http://hdl.handle.net/2142/102916

Copyright and License Information

Copyright 2018 Rong Guo

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