{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99360"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99360","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A subspace approach to spectral quantification for MR spectroscopic imaging","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Li, Yudu"],"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":2018,"date_issued":"2018-03-13T15:48:51Z","date_published":"2018-03-13T15:48:51Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Magnetic resonance spectroscopic imaging (MRSI)","Spectral estimation","Subspace","Spatiospectral constraints"],"languages":["en"],"rights":["Copyright 2017 Yudu Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99360","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":["Li, Yudu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-13T15:48:51Z","2017-12-04","2017-12"]},{"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 estimation","Subspace","Spatiospectral constraints"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Yudu Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99360"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-03-13 without embargo terms","The student, Yudu Li, accepted the attached license on 2017-12-04 at 10:49.","The student, Yudu Li, submitted this Thesis for approval on 2017-12-04 at 10:53.","This Thesis was approved for publication on 2017-12-04 at 11:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11825 on 2018-03-13 at 10:09:53","Made available in DSpace on 2018-03-13T15:48:51Z (GMT). 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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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-03-13 without embargo terms","The student, Yudu Li, accepted the attached license on 2017-12-04 at 10:49.","The student, Yudu Li, submitted this Thesis for approval on 2017-12-04 at 10:53.","This Thesis was approved for publication on 2017-12-04 at 11:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11825 on 2018-03-13 at 10:09:53","Made available in DSpace on 2018-03-13T15:48:51Z (GMT). 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