Further development of magnetic resonance spectroscopic imaging using subspace-based models
type: CreativeWork
description: Magnetic resonance spectroscopic imaging (MRSI) is a powerful, non-invasive imaging modality capable of providing in vivo measurements of chemical distributions within an object. This makes it an ideal tool for metabolic studies in clinical and research settings; yet, despite more than four decades of development, MRSI has yet to achieve clinical utility due to an inherently poor trade-off between imaging speed, resolution, and signal-to-noise-ratio (SNR). The recent integration of subspace-based models into MRSI has significantly improved this trade-off and demonstrated the potential for high-resolution, high-SNR metabolic mapping at clinically relevant imaging speeds. This work further extends subspace-based MRSI methods along two major avenues. First, we have developed a novel method for simultaneously providing tissue magnetic susceptibility information and metabolic information. By using a specialized data acquisition and processing scheme, we are able to acquire the 1H-MRSI signals from water as well as the metabolic signals of interest and use the water image for quantitative susceptibility mapping (QSM). The proposed method has been evaluated in in vivo experiments and shown capable of providing state-of-the-art MRSI and QSM of the brain at millimeter-scale resolutions from a 7 min scan. We have also developed two novel methods to improve the robustness of this method by correcting for the effects of intrascan motion. Secondly, we have developed a molecular-subspace based method for performing dynamic 31P-MRSI. Existing subspace-based MRSI methods require several minutes to generate a single image. As such, they are not suitable for studying rapid metabolic processes; however, the proposed method provides dynamic MRSI with frame-rates on the order of 1 s while maintaining sub-centimeter spatial resolutions. We have validated the method's capability to provide reproducible dynamic metabolic maps in both preclinical and clinical settings using in vivo and in v…
abstract: Magnetic resonance spectroscopic imaging (MRSI) is a powerful, non-invasive imaging modality capable of providing in vivo measurements of chemical distributions within an object. This makes it an ideal tool for metabolic studies in clinical and research settings; yet, despite more than four decades of development, MRSI has yet to achieve clinical utility due to an inherently poor trade-off between imaging speed, resolution, and signal-to-noise-ratio (SNR). The recent integration of subspace-based models into MRSI has significantly improved this trade-off and demonstrated the potential for high-resolution, high-SNR metabolic mapping at clinically relevant imaging speeds. This work further extends subspace-based MRSI methods along two major avenues. First, we have developed a novel method for simultaneously providing tissue magnetic susceptibility information and metabolic information. By using a specialized data acquisition and processing scheme, we are able to acquire the 1H-MRSI signals from water as well as the metabolic signals of interest and use the water image for quantitative susceptibility mapping (QSM). The proposed method has been evaluated in in vivo experiments and shown capable of providing state-of-the-art MRSI and QSM of the brain at millimeter-scale resolutions from a 7 min scan. We have also developed two novel methods to improve the robustness of this method by correcting for the effects of intrascan motion. Secondly, we have developed a molecular-subspace based method for performing dynamic 31P-MRSI. Existing subspace-based MRSI methods require several minutes to generate a single image. As such, they are not suitable for studying rapid metabolic processes; however, the proposed method provides dynamic MRSI with frame-rates on the order of 1 s while maintaining sub-centimeter spatial resolutions. We have validated the method's capability to provide reproducible dynamic metabolic maps in both preclinical and clinical settings using in vivo and in v…
author: Clifford, Bryan
language: en
keywords: Magnetic resonance spectroscopic imaging, subspace model, low rank, metabolic mapping, mitochondrial oxida- tive capacity, magnetic susceptibility mapping, motion correction
date published: 2019-11-26T20:56:45Z
date modified: 2026-07-22T22:24:45Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/105859