{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122083"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122083","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Denoising for deuterium magnetic resonance spectroscopic imaging based on posterior-score-guided subspace modeling","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_has_math":false,"creators":["Xu, Ziyang"],"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":["Deuterium Magnetic Spectroscopic Imaging","Denoising","Score-based Diffusion Model","Posterior Sampling","Machine Learning"],"languages":["en","eng"],"rights":["Copyright 2023 Ziyang Xu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122083","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":["Xu, Ziyang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-08"]},{"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":["Deuterium Magnetic Spectroscopic Imaging","Denoising","Score-based Diffusion Model","Posterior Sampling","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 Ziyang Xu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122083"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","The student, Ziyang Xu, accepted the attached license on 2023-12-05 at 11:02.","The student, Ziyang Xu, submitted this Thesis for approval on 2023-12-05 at 11:11.","This Thesis was approved for publication on 2023-12-08 at 15:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18986 on 2024-03-01 at 13:29:08","Deuterium magnetic resonance spectroscopic imaging (DMRSI) has recently been recognized as a powerful tool for imaging the energy metabolism and tumors of the brain. However, the low sensitivity of DMRSI has primarily limited its practical utility in research and clinical studies. In this work, we present a novel approach to improving the sensitivity of DMRSI, incorporating both physics-based subspace modeling and data-driven distribution learning to remove measurement noise. Particularly, a union-of-subspaces model is used to represent the spatial-spectral-temporal variations of the desired DMRSI signal, leading to a significant reduction in degrees of freedom. The subspace structures are pre-learned from spin physics and training data, incorporating known resonance structures and a priori experimental variations. The subspace coefficients are treated as random variables, whose joint probabilistic distributions are learned using an advanced diffusion model. With the proposed subspace model and learned signal priors, denoising is accomplished using the Langevin dynamics process. The proposed method has been evaluated using both simulated and experimental data, producing very promising results. The resulting algorithm is expected to enhance the practical utility of DMRSI and could also be useful for denoising MRSI of other nuclei. In this thesis, background materials on subspace modeling of MRSI signals, Bayesian parameter estimation theory and diffusion generative models will be first presented. Then, a detailed description of the proposed denoising algorithm is provided. Finally, simulation and in vivo evaluation results are presented to demonstrate the efficacy of the proposed method for dynamic DMRSI denoising."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Denoising for deuterium magnetic resonance spectroscopic imaging based on posterior-score-guided subspace modeling"]}]}],"canonical_facts":{"dc:contributor":["Liang, Zhi-Pei"],"dc:creator":["Xu, Ziyang"],"dc:date":["2023-12","2023-12-08"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","The student, Ziyang Xu, accepted the attached license on 2023-12-05 at 11:02.","The student, Ziyang Xu, submitted this Thesis for approval on 2023-12-05 at 11:11.","This Thesis was approved for publication on 2023-12-08 at 15:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18986 on 2024-03-01 at 13:29:08","Deuterium magnetic resonance spectroscopic imaging (DMRSI) has recently been recognized as a powerful tool for imaging the energy metabolism and tumors of the brain. However, the low sensitivity of DMRSI has primarily limited its practical utility in research and clinical studies. In this work, we present a novel approach to improving the sensitivity of DMRSI, incorporating both physics-based subspace modeling and data-driven distribution learning to remove measurement noise. Particularly, a union-of-subspaces model is used to represent the spatial-spectral-temporal variations of the desired DMRSI signal, leading to a significant reduction in degrees of freedom. The subspace structures are pre-learned from spin physics and training data, incorporating known resonance structures and a priori experimental variations. The subspace coefficients are treated as random variables, whose joint probabilistic distributions are learned using an advanced diffusion model. With the proposed subspace model and learned signal priors, denoising is accomplished using the Langevin dynamics process. The proposed method has been evaluated using both simulated and experimental data, producing very promising results. The resulting algorithm is expected to enhance the practical utility of DMRSI and could also be useful for denoising MRSI of other nuclei. In this thesis, background materials on subspace modeling of MRSI signals, Bayesian parameter estimation theory and diffusion generative models will be first presented. Then, a detailed description of the proposed denoising algorithm is provided. Finally, simulation and in vivo evaluation results are presented to demonstrate the efficacy of the proposed method for dynamic DMRSI denoising."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122083"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Ziyang Xu"],"dc:subject":["Deuterium Magnetic Spectroscopic Imaging","Denoising","Score-based Diffusion Model","Posterior Sampling","Machine Learning"],"dc:title":["Denoising for deuterium magnetic resonance spectroscopic imaging based on posterior-score-guided subspace modeling"],"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"}