{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125805"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125805","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Ultrafast magnetic resonance spectroscopic imaging: technical development and applications","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","abstract_has_math":false,"creators":["Zhao, Yibo"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Liang, Zhi-Pei","Anastasio, Mark A","Do, Minh N","Oelze, Michael L","Sutton, Bradley P"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-08","date_published":"2024-08","updated_at":"2026-07-22T22:25:02Z","subjects":["Magnetic Resonance Spectroscopic Imaging","Metabolite","Neurotransmitter","Metabolic Imaging","Molecular Imaging","Subspace Imaging","Brain Mapping","Brain Tumor","Multiple Sclerosis"],"languages":["en","eng"],"rights":["Copyright 2024 Yibo Zhao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125805","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liang, Zhi-Pei","Anastasio, Mark A","Do, Minh N","Oelze, Michael L","Sutton, Bradley P"]},{"key":"dc:creator","label":"Author","values":["Zhao, Yibo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-08","2024-07-09"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Magnetic Resonance Spectroscopic Imaging","Metabolite","Neurotransmitter","Metabolic Imaging","Molecular Imaging","Subspace Imaging","Brain Mapping","Brain Tumor","Multiple Sclerosis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Yibo Zhao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125805"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","The student, Yibo Zhao, accepted the attached license on 2024-07-09 at 13:52.","The student, Yibo Zhao, submitted this Dissertation for approval on 2024-07-09 at 14:46.","This Dissertation was approved for publication on 2024-07-09 at 16:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21013 on 2025-02-04 at 21:25:44","The goal of this dissertation research is to develop a highly accelerated magnetic resonance spectroscopic imaging (MRSI) method for high-resolution mapping of metabolites and neurotransmitters (neurometabolites), and to evaluate its feasibility in neuroimaging and clinical applications. MRSI is a unique tool for label-free, non-invasive molecular imaging of the brain, with the capability to simultaneously map a range of endogenous neurometabolites. These molecules, providing vital information about neuronal integrity, glial proliferation, cell membrane turnover, astrocytosis, and excitatory/inhibitory synaptic neurotransmission, have been demonstrated useful for understanding and characterizing brain function under normal and disease conditions. Despite its promise, the practical utility of MRSI has been rather limited due to several longstanding technical obstacles, including long data acquisition time, poor spatial resolution, low signal-to-noise-ratio (SNR), overwhelming background water and lipid signals, and significant spectral overlaps of different molecules. As a result, the state-of-the-art MRSI methods can only achieve single-voxel or single-slice spatial coverage, low spatial resolution (5–10 mm) in a long scan time (~20 min). This dissertation research develops a novel data acquisition sequence, which synergistically integrates two types of pulse sequences, free-induction-decay (FID) and spin-echo (SE), to exploit their complementary information for ultrafast MRSI. This sequence is also embedded with several novel features to facilitate its translation into neuroimaging and clinical applications. More specifically, the FID and SE signals are jointly used for improved separation overlapping resonances (especially macromolecules and neurotransmitters). Besides, this sequence adopts a variable-density sampling scheme for improved resolution-speed tradeoff, and samples the most informative data according to statistical estimation theory. Moreover, motion and field drift navigators are also embedded into the FID and SE sequences, providing the robustness needed for practical applications. The key processing issues given this data acquisition include: (a) removal of tissue water and lipid, especially from low-resolution SE data, (b) reconstruction of MRSI signals from sparse and noisy data, and (c) spectral separation and quantification of macromolecules and neurometabolites using joint FID/SE spectra. These processing issues have been successfully addressed in this dissertation research using the union-of-subspaces framework with subspace learning strategies. As a result, mapping of neurometabolites of large brain coverage (e.g., field of view 240×240×72 mm3) can be achieved with 3.1×2.0×3.0 mm3 nominal spatial resolution in a practically feasible scan time (e.g., 11.2 minutes). This imaging methodology has also been applied for accelerated mapping of metabolite T1 values. Metabolite T1 values are needed for correcting the T1 weighting effects, which is crucial for accurate estimation of metabolite concentrations. However, measuring metabolite T1 values requires the acquisition of a series of T1-weighted spectroscopic signals, which adds another dimension to the imaging problem and exacerbates the challenge of achieving fast imaging with high spatial resolution, large spatial coverage, and good SNR. As a result, current technologies for measuring metabolite T1 values have been limited to single-voxel or single-slice experiments. This problem has been addressed by extending the union-of-subspaces model to a union-of-low-rank-tensors model. By exploiting the intrinsic correlation among different data dimensions, the degrees-of-freedom of the imaging problem has been substantially reduced, enabling three-dimensional metabolite T1 mapping in a 14.5-minute scan. The proposed ultrafast high-resolution MRSI technology has been validated through phantom and healthy subject experiments, showing very impressive performance. The experimental results revealed heterogeneous neurometabolite distributions across different brain regions. The reproducibility of the proposed technology has been evaluated in test-retest experiments at different imaging sites. High-resolution neurometabolite maps have also been obtained in regular clinical settings from brain tumor and multiple sclerosis patients, revealing substantial neurometabolic alterations in these pathological conditions. These results demonstrate the transformative potential of the proposed technology in diagnosis, characterization, and monitoring of therapeutic efficacy in clinical applications. In addition, high-quality and reproducible metabolite T1 maps have been obtained from both phantom and healthy subjects, demonstrating the capability of quantitative metabolite measurements. In summary, this dissertation research developed ultrafast MRSI technology for high-resolution mapping of brain neurometabolites. The feasibility of this technology has been successfully demonstrated in phantom studies, reproducibility experiments, and clinical applications. This technology is expected to provide a long-desired capability for noninvasive label-free neurometabolic imaging of brain function and diseases for both neuroscience and clinical applications."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Ultrafast magnetic resonance spectroscopic imaging: technical development and applications"]}]}],"canonical_facts":{"dc:contributor":["Liang, Zhi-Pei","Anastasio, Mark A","Do, Minh N","Oelze, Michael L","Sutton, Bradley P"],"dc:creator":["Zhao, Yibo"],"dc:date":["2024-08","2024-07-09"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","The student, Yibo Zhao, accepted the attached license on 2024-07-09 at 13:52.","The student, Yibo Zhao, submitted this Dissertation for approval on 2024-07-09 at 14:46.","This Dissertation was approved for publication on 2024-07-09 at 16:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21013 on 2025-02-04 at 21:25:44","The goal of this dissertation research is to develop a highly accelerated magnetic resonance spectroscopic imaging (MRSI) method for high-resolution mapping of metabolites and neurotransmitters (neurometabolites), and to evaluate its feasibility in neuroimaging and clinical applications. MRSI is a unique tool for label-free, non-invasive molecular imaging of the brain, with the capability to simultaneously map a range of endogenous neurometabolites. These molecules, providing vital information about neuronal integrity, glial proliferation, cell membrane turnover, astrocytosis, and excitatory/inhibitory synaptic neurotransmission, have been demonstrated useful for understanding and characterizing brain function under normal and disease conditions. Despite its promise, the practical utility of MRSI has been rather limited due to several longstanding technical obstacles, including long data acquisition time, poor spatial resolution, low signal-to-noise-ratio (SNR), overwhelming background water and lipid signals, and significant spectral overlaps of different molecules. As a result, the state-of-the-art MRSI methods can only achieve single-voxel or single-slice spatial coverage, low spatial resolution (5–10 mm) in a long scan time (~20 min). This dissertation research develops a novel data acquisition sequence, which synergistically integrates two types of pulse sequences, free-induction-decay (FID) and spin-echo (SE), to exploit their complementary information for ultrafast MRSI. This sequence is also embedded with several novel features to facilitate its translation into neuroimaging and clinical applications. More specifically, the FID and SE signals are jointly used for improved separation overlapping resonances (especially macromolecules and neurotransmitters). Besides, this sequence adopts a variable-density sampling scheme for improved resolution-speed tradeoff, and samples the most informative data according to statistical estimation theory. Moreover, motion and field drift navigators are also embedded into the FID and SE sequences, providing the robustness needed for practical applications. The key processing issues given this data acquisition include: (a) removal of tissue water and lipid, especially from low-resolution SE data, (b) reconstruction of MRSI signals from sparse and noisy data, and (c) spectral separation and quantification of macromolecules and neurometabolites using joint FID/SE spectra. These processing issues have been successfully addressed in this dissertation research using the union-of-subspaces framework with subspace learning strategies. As a result, mapping of neurometabolites of large brain coverage (e.g., field of view 240×240×72 mm3) can be achieved with 3.1×2.0×3.0 mm3 nominal spatial resolution in a practically feasible scan time (e.g., 11.2 minutes). This imaging methodology has also been applied for accelerated mapping of metabolite T1 values. Metabolite T1 values are needed for correcting the T1 weighting effects, which is crucial for accurate estimation of metabolite concentrations. However, measuring metabolite T1 values requires the acquisition of a series of T1-weighted spectroscopic signals, which adds another dimension to the imaging problem and exacerbates the challenge of achieving fast imaging with high spatial resolution, large spatial coverage, and good SNR. As a result, current technologies for measuring metabolite T1 values have been limited to single-voxel or single-slice experiments. This problem has been addressed by extending the union-of-subspaces model to a union-of-low-rank-tensors model. By exploiting the intrinsic correlation among different data dimensions, the degrees-of-freedom of the imaging problem has been substantially reduced, enabling three-dimensional metabolite T1 mapping in a 14.5-minute scan. The proposed ultrafast high-resolution MRSI technology has been validated through phantom and healthy subject experiments, showing very impressive performance. The experimental results revealed heterogeneous neurometabolite distributions across different brain regions. The reproducibility of the proposed technology has been evaluated in test-retest experiments at different imaging sites. High-resolution neurometabolite maps have also been obtained in regular clinical settings from brain tumor and multiple sclerosis patients, revealing substantial neurometabolic alterations in these pathological conditions. These results demonstrate the transformative potential of the proposed technology in diagnosis, characterization, and monitoring of therapeutic efficacy in clinical applications. In addition, high-quality and reproducible metabolite T1 maps have been obtained from both phantom and healthy subjects, demonstrating the capability of quantitative metabolite measurements. In summary, this dissertation research developed ultrafast MRSI technology for high-resolution mapping of brain neurometabolites. The feasibility of this technology has been successfully demonstrated in phantom studies, reproducibility experiments, and clinical applications. This technology is expected to provide a long-desired capability for noninvasive label-free neurometabolic imaging of brain function and diseases for both neuroscience and clinical applications."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125805"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Yibo Zhao"],"dc:subject":["Magnetic Resonance Spectroscopic Imaging","Metabolite","Neurotransmitter","Metabolic Imaging","Molecular Imaging","Subspace Imaging","Brain Mapping","Brain Tumor","Multiple Sclerosis"],"dc:title":["Ultrafast magnetic resonance spectroscopic imaging: technical development and applications"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}