{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/109583"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/109583","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Model-based myelin water fraction mapping: analyses and improvement","abstract":"In this thesis, the problem of model-based myelin water fraction (MWF) mapping is addressed. We first focus on three of the most widely used signal models for T2*-myelin water imaging (MWI), i.e., the NNLS-multi-exponential model, the magnitude-3-exponential model, and the complex-3-exponential model, and investigate their sensitivities to practical perturbations such as random noise and field-related structured errors. We demonstrate through both Cramér-Rao lower bound (CRLB) analyses and Monte Carlo simulations that the three signal models are all very unstable inherently. Comparatively speaking, however, we demonstrate the theoretical advantage of the 3-exponential models over the multi-exponential model in handling noise, and the practical advantage of the magnitude models over the complex model in handling phase-related perturbations for T2*-MWI. We also illustrate the necessity and effects of incorporating various types of constraints for additional sensitivity gain. Using the insights obtained in the sensitivity analyses, we then propose a new MWF fitting scheme that leverages an improved signal model and a set of more effective constraints. In particular, a relaxed magnitude-3-exponential model with additional frequency compensation terms is introduced to better represent voxels with large field variations; a set of statistical distributions learned from in vivo training data is further imposed on the model parameters for additional constraints. Using phantom simulation and in vivo experiments, we then evaluate and compare the proposed method with several popular conventional MWF fitting schemes to demonstrate the improved accuracy and robustness of the proposed method. In this thesis, a literature review on the study of myelin and the development of MWF mapping is provided at the start of the work. Background materials on the CRLB theories are also provided to facilitate reading.","abstract_html":"In this thesis, the problem of model-based myelin water fraction (MWF) mapping is addressed. We first focus on three of the most widely used signal models for T2*-myelin water imaging (MWI), i.e., the NNLS-multi-exponential model, the magnitude-3-exponential model, and the complex-3-exponential model, and investigate their sensitivities to practical perturbations such as random noise and field-related structured errors. We demonstrate through both Cramér-Rao lower bound (CRLB) analyses and Monte Carlo simulations that the three signal models are all very unstable inherently. Comparatively speaking, however, we demonstrate the theoretical advantage of the 3-exponential models over the multi-exponential model in handling noise, and the practical advantage of the magnitude models over the complex model in handling phase-related perturbations for T2*-MWI. We also illustrate the necessity and effects of incorporating various types of constraints for additional sensitivity gain. Using the insights obtained in the sensitivity analyses, we then propose a new MWF fitting scheme that leverages an improved signal model and a set of more effective constraints. In particular, a relaxed magnitude-3-exponential model with additional frequency compensation terms is introduced to better represent voxels with large field variations; a set of statistical distributions learned from in vivo training data is further imposed on the model parameters for additional constraints. Using phantom simulation and in vivo experiments, we then evaluate and compare the proposed method with several popular conventional MWF fitting schemes to demonstrate the improved accuracy and robustness of the proposed method. In this thesis, a literature review on the study of myelin and the development of MWF mapping is provided at the start of the work. Background materials on the CRLB theories are also provided to facilitate reading.","abstract_has_math":false,"creators":["Xiong, Jiahui"],"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":2021,"date_issued":"2021-03-05T21:45:32Z","date_published":"2021-03-05T21:45:32Z","updated_at":"2026-07-22T22:24:50Z","subjects":["myelin water fraction","sensitivity analysis","Bayesian estimation","Cramér-Rao lower bound"],"languages":["en"],"rights":["Copyright 2020 Jiahui Xiong"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/109583","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":["Xiong, Jiahui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-03-05T21:45:32Z","2023-03-05T21:47:41Z","2020-11-18","2020-12"]},{"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":["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":["myelin water fraction","sensitivity analysis","Bayesian estimation","Cramér-Rao lower 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 2020 Jiahui Xiong"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/109583"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this thesis, the problem of model-based myelin water fraction (MWF) mapping is addressed. We first focus on three of the most widely used signal models for T2*-myelin water imaging (MWI), i.e., the NNLS-multi-exponential model, the magnitude-3-exponential model, and the complex-3-exponential model, and investigate their sensitivities to practical perturbations such as random noise and field-related structured errors. We demonstrate through both Cramér-Rao lower bound (CRLB) analyses and Monte Carlo simulations that the three signal models are all very unstable inherently. Comparatively speaking, however, we demonstrate the theoretical advantage of the 3-exponential models over the multi-exponential model in handling noise, and the practical advantage of the magnitude models over the complex model in handling phase-related perturbations for T2*-MWI. We also illustrate the necessity and effects of incorporating various types of constraints for additional sensitivity gain. Using the insights obtained in the sensitivity analyses, we then propose a new MWF fitting scheme that leverages an improved signal model and a set of more effective constraints. In particular, a relaxed magnitude-3-exponential model with additional frequency compensation terms is introduced to better represent voxels with large field variations; a set of statistical distributions learned from in vivo training data is further imposed on the model parameters for additional constraints. Using phantom simulation and in vivo experiments, we then evaluate and compare the proposed method with several popular conventional MWF fitting schemes to demonstrate the improved accuracy and robustness of the proposed method. In this thesis, a literature review on the study of myelin and the development of MWF mapping is provided at the start of the work. Background materials on the CRLB theories are also provided to facilitate reading.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01","The student, Jiahui Xiong, accepted the attached license on 2020-11-17 at 12:52.","The student, Jiahui Xiong, submitted this Thesis for approval on 2020-11-17 at 13:03.","This Thesis was approved for publication on 2020-11-18 at 14:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15895 on 2021-03-04 at 16:31:59","Made available in DSpace on 2021-03-05T21:45:32Z (GMT). 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We first focus on three of the most widely used signal models for T2*-myelin water imaging (MWI), i.e., the NNLS-multi-exponential model, the magnitude-3-exponential model, and the complex-3-exponential model, and investigate their sensitivities to practical perturbations such as random noise and field-related structured errors. We demonstrate through both Cramér-Rao lower bound (CRLB) analyses and Monte Carlo simulations that the three signal models are all very unstable inherently. Comparatively speaking, however, we demonstrate the theoretical advantage of the 3-exponential models over the multi-exponential model in handling noise, and the practical advantage of the magnitude models over the complex model in handling phase-related perturbations for T2*-MWI. We also illustrate the necessity and effects of incorporating various types of constraints for additional sensitivity gain. Using the insights obtained in the sensitivity analyses, we then propose a new MWF fitting scheme that leverages an improved signal model and a set of more effective constraints. In particular, a relaxed magnitude-3-exponential model with additional frequency compensation terms is introduced to better represent voxels with large field variations; a set of statistical distributions learned from in vivo training data is further imposed on the model parameters for additional constraints. Using phantom simulation and in vivo experiments, we then evaluate and compare the proposed method with several popular conventional MWF fitting schemes to demonstrate the improved accuracy and robustness of the proposed method. In this thesis, a literature review on the study of myelin and the development of MWF mapping is provided at the start of the work. Background materials on the CRLB theories are also provided to facilitate reading.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01","The student, Jiahui Xiong, accepted the attached license on 2020-11-17 at 12:52.","The student, Jiahui Xiong, submitted this Thesis for approval on 2020-11-17 at 13:03.","This Thesis was approved for publication on 2020-11-18 at 14:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15895 on 2021-03-04 at 16:31:59","Made available in DSpace on 2021-03-05T21:45:32Z (GMT). No. of bitstreams: 2 XIONG-THESIS-2020.pdf: 11672242 bytes, checksum: 4f0060436c20d10f6c990dffb93b1d2f (MD5) LICENSE.txt: 4209 bytes, checksum: 386f1d8975a06576e18f881ab10f5939 (MD5) Previous issue date: 2020-11-18","Embargo set by: Seth Robbins for item 117288 Lift date: 2023-03-05T21:45:47Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 117288 Lift date: 2023-03-05T21:47:41Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/109583"],"dc:language":["en"],"dc:rights":["Copyright 2020 Jiahui Xiong"],"dc:subject":["myelin water fraction","sensitivity analysis","Bayesian estimation","Cramér-Rao lower bound"],"dc:title":["Model-based myelin water fraction mapping: analyses and improvement"],"dc:type":["text","Thesis"],"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":"2026-07-22T22:24:50Z"}