{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/89025"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/89025","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Spectral estimation with spatio-spectral constraints for magnetic resonance spectroscopic imaging","abstract":"Magnetic resonance spectroscopic imaging (MRSI) is a promising tool to acquire in vivo biochemical information, and spectral estimation (quantification) of MRSI data is an important step towards quantitative studies. Although a large body of work has been done on spectral estimation over the past decades, it remains challenging due to model nonlinearity and extremely low signal-to-noise ratio (SNR). Building on the existing methods which effectively incorporate spectral prior knowledge in the form of basis functions, this work addresses the spectral estimation problem by incorporating both spectral and spatial prior information. Specifically, we jointly estimate the spectra over all the voxels of interest, incorporating prior spatial information in a regularization framework. The effectiveness of the proposed method has been evaluated using both simulated and experimental data. A theoretical analysis based on Cramer-Rao Bound is proposed to further assess the performance improvement of the proposed method over state-of-the-art methods. The proposed spectral estimation method should prove useful in various MRSI studies.","abstract_html":"Magnetic resonance spectroscopic imaging (MRSI) is a promising tool to acquire in vivo biochemical information, and spectral estimation (quantification) of MRSI data is an important step towards quantitative studies. Although a large body of work has been done on spectral estimation over the past decades, it remains challenging due to model nonlinearity and extremely low signal-to-noise ratio (SNR). Building on the existing methods which effectively incorporate spectral prior knowledge in the form of basis functions, this work addresses the spectral estimation problem by incorporating both spectral and spatial prior information. Specifically, we jointly estimate the spectra over all the voxels of interest, incorporating prior spatial information in a regularization framework. The effectiveness of the proposed method has been evaluated using both simulated and experimental data. A theoretical analysis based on Cramer-Rao Bound is proposed to further assess the performance improvement of the proposed method over state-of-the-art methods. The proposed spectral estimation method should prove useful in various MRSI studies.","abstract_has_math":false,"creators":["Ning, Qiang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engineering","degree_department":null,"school":null,"contributors":["Liang, Zhi-Pei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-03-02T19:34:03Z","date_published":"2016-03-02T19:34:03Z","updated_at":"2026-07-22T22:26:32Z","subjects":["Magnetic resonance spectroscopic imaging (MRSI)","spectral estimation","spatial regularization","sparsity constraint","Cramer-Rao Bound"],"languages":["en"],"rights":["Copyright 2015 Qiang Ning"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/89025","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":["Ning, Qiang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-03-02T19:34:03Z","2015-11-30","2015-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engineering"]},{"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","spatial regularization","sparsity constraint","Cramer-Rao Bound"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Qiang Ning"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/89025"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Magnetic resonance spectroscopic imaging (MRSI) is a promising tool to acquire in vivo biochemical information, and spectral estimation (quantification) of MRSI data is an important step towards quantitative studies. Although a large body of work has been done on spectral estimation over the past decades, it remains challenging due to model nonlinearity and extremely low signal-to-noise ratio (SNR). Building on the existing methods which effectively incorporate spectral prior knowledge in the form of basis functions, this work addresses the spectral estimation problem by incorporating both spectral and spatial prior information. Specifically, we jointly estimate the spectra over all the voxels of interest, incorporating prior spatial information in a regularization framework. The effectiveness of the proposed method has been evaluated using both simulated and experimental data. A theoretical analysis based on Cramer-Rao Bound is proposed to further assess the performance improvement of the proposed method over state-of-the-art methods. The proposed spectral estimation method should prove useful in various MRSI studies.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-03-02 without embargo terms","The student, Qiang Ning, accepted the attached license on 2015-11-30 at 14:07.","The student, Qiang Ning, submitted this Thesis for approval on 2015-11-30 at 14:39.","This Thesis was approved for publication on 2015-11-30 at 16:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8874 on 2016-03-02 at 12:50:46","Made available in DSpace on 2016-03-02T19:34:03Z (GMT). No. of bitstreams: 2 NING-THESIS-2015.pdf: 2506233 bytes, checksum: 4d6306a0a6685e722d313cc44ee97cf7 (MD5) LICENSE.txt: 4207 bytes, checksum: e5c7f2a735b7651afbd4af78c49aeca7 (MD5) Previous issue date: 2015-11-30","Open Restriction set for Item 91226 on 2019-09-23T15:37:32Z with date null by fschaef2@illinois.edu.","Open Restriction set for Item 91226 on 2019-09-23T15:37:38Z with date null by fschaef2@illinois.edu.","Open"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Spectral estimation with spatio-spectral constraints for magnetic resonance spectroscopic imaging"]}]}],"canonical_facts":{"dc:contributor":["Liang, Zhi-Pei"],"dc:creator":["Ning, Qiang"],"dc:date":["2016-03-02T19:34:03Z","2015-11-30","2015-12"],"dc:description":["Magnetic resonance spectroscopic imaging (MRSI) is a promising tool to acquire in vivo biochemical information, and spectral estimation (quantification) of MRSI data is an important step towards quantitative studies. Although a large body of work has been done on spectral estimation over the past decades, it remains challenging due to model nonlinearity and extremely low signal-to-noise ratio (SNR). Building on the existing methods which effectively incorporate spectral prior knowledge in the form of basis functions, this work addresses the spectral estimation problem by incorporating both spectral and spatial prior information. Specifically, we jointly estimate the spectra over all the voxels of interest, incorporating prior spatial information in a regularization framework. The effectiveness of the proposed method has been evaluated using both simulated and experimental data. A theoretical analysis based on Cramer-Rao Bound is proposed to further assess the performance improvement of the proposed method over state-of-the-art methods. The proposed spectral estimation method should prove useful in various MRSI studies.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-03-02 without embargo terms","The student, Qiang Ning, accepted the attached license on 2015-11-30 at 14:07.","The student, Qiang Ning, submitted this Thesis for approval on 2015-11-30 at 14:39.","This Thesis was approved for publication on 2015-11-30 at 16:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8874 on 2016-03-02 at 12:50:46","Made available in DSpace on 2016-03-02T19:34:03Z (GMT). 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