{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122267"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122267","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning based quantification and removal of lipid signals from MR spectroscopic imaging of the brain","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","abstract_has_math":false,"creators":["Zhang, Yuanbo"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Liang, Zhi-Pei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Magnetic Resonance Spectroscopic Imaging (mrsi)","Spectral Quantification","Lipid Removal","Bayesian Estimation","Machine Learning"],"languages":["en","eng"],"rights":["Copyright 2023 Yuanbo Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122267","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liang, Zhi-Pei"]},{"key":"dc:creator","label":"Author","values":["Zhang, Yuanbo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-06"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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 (mrsi)","Spectral Quantification","Lipid Removal","Bayesian Estimation","Machine Learning"]}]},{"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 2023 Yuanbo Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122267"]}]},{"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 2025-12-01","The student, Yuanbo Zhang, accepted the attached license on 2023-12-05 at 21:07.","The student, Yuanbo Zhang, submitted this Thesis for approval on 2023-12-05 at 21:25.","This Thesis was approved for publication on 2023-12-06 at 11:23.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20121 on 2024-03-01 at 13:57:21","Magnetic resonance spectroscopic imaging (MRSI) has long promised to be a powerful noninvasive molecular imaging technique for metabolites and nuero-transmitters. Recently proposed fast J-resolved MRSI has further enabled the separation of J-coupled overlapping molecules, elevating the molecular MRSI to a new level. However, the overwhelming nuisance signal such as the lipid signal has made it extremely difficult to separate the desired molecular signal from the nuisance signal which is usually orders of magnitude higher than the desired molecular signal. Therefore, it is necessary to remove the nuisance signal. The nuisance removal problem is especially more challenging in the case of J-resolved MRSI due to its additional encoding dimension thus limited spatial resolution. To solve this problem, reference-based approach is necessary to help lipid removal since estimating the lipid signal from the data itself is an ill-conditioned problem. However, reference-based approach usually requires precise quantification of the reference signal, which is rather challenging due to the complicated structure of the lipid signal. In this work, we present a novel lipid quantification method to improve quantification performance by utilizing refined prior information, a restrictive lipid signal model, and machine learning. The proposed method not only enables incorporation of tissue dependent prior, but are also accelerated with the help of machine learning, thus achieves improved quantification performance with good robustness. With the improved performance of lipid quantification of the reference signal, the reference-based method can better help lipid removal from the J-resolved MRSI data. Eventually, a higher quality molecular map can be obtained. The propose method has been evaluated on both simulation data and in-vivo data, producing encouraging results. The proposed method could further provide a potential framework for spectral quantification of MRSI data."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning based quantification and removal of lipid signals from MR spectroscopic imaging of the brain"]}]}],"canonical_facts":{"dc:contributor":["Liang, Zhi-Pei"],"dc:creator":["Zhang, Yuanbo"],"dc:date":["2023-12","2023-12-06"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01","The student, Yuanbo Zhang, accepted the attached license on 2023-12-05 at 21:07.","The student, Yuanbo Zhang, submitted this Thesis for approval on 2023-12-05 at 21:25.","This Thesis was approved for publication on 2023-12-06 at 11:23.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20121 on 2024-03-01 at 13:57:21","Magnetic resonance spectroscopic imaging (MRSI) has long promised to be a powerful noninvasive molecular imaging technique for metabolites and nuero-transmitters. Recently proposed fast J-resolved MRSI has further enabled the separation of J-coupled overlapping molecules, elevating the molecular MRSI to a new level. However, the overwhelming nuisance signal such as the lipid signal has made it extremely difficult to separate the desired molecular signal from the nuisance signal which is usually orders of magnitude higher than the desired molecular signal. Therefore, it is necessary to remove the nuisance signal. The nuisance removal problem is especially more challenging in the case of J-resolved MRSI due to its additional encoding dimension thus limited spatial resolution. To solve this problem, reference-based approach is necessary to help lipid removal since estimating the lipid signal from the data itself is an ill-conditioned problem. However, reference-based approach usually requires precise quantification of the reference signal, which is rather challenging due to the complicated structure of the lipid signal. In this work, we present a novel lipid quantification method to improve quantification performance by utilizing refined prior information, a restrictive lipid signal model, and machine learning. The proposed method not only enables incorporation of tissue dependent prior, but are also accelerated with the help of machine learning, thus achieves improved quantification performance with good robustness. With the improved performance of lipid quantification of the reference signal, the reference-based method can better help lipid removal from the J-resolved MRSI data. Eventually, a higher quality molecular map can be obtained. The propose method has been evaluated on both simulation data and in-vivo data, producing encouraging results. The proposed method could further provide a potential framework for spectral quantification of MRSI data."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122267"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Yuanbo Zhang"],"dc:subject":["Magnetic Resonance Spectroscopic Imaging (mrsi)","Spectral Quantification","Lipid Removal","Bayesian Estimation","Machine Learning"],"dc:title":["Learning based quantification and removal of lipid signals from MR spectroscopic imaging of the brain"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}