{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124727"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124727","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Self-supervised multi-contrast MRI denoising","abstract":"Magnetic Resonance Imaging (MRI) is pivotal in medical diagnostics, offering essential multi-contrast imaging capabilities. However, MRI quality is often compromised by inherent noise, which can hinder both image clarity and analytical accuracy. This thesis presents the \"Corruption2Self\" (C2S) framework, a self-supervised method for multi-contrast MRI denoising. C2S utilizes self-generated pseudo-labels from noisy data to enhance contrast fusion and Signal-to-Noise Ratio (SNR), providing a robust solution that facilitates shorter scanning times or improved spatial resolution—critical factors in enhancing patient experience and diagnostic precision. Comparative tests on the M4Raw dataset show that C2S substantially surpasses traditional methods like BM3D and Noise2Self in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). These results underscore the potential of self-supervised learning to improve multi-contrast MRI image quality.","abstract_html":"Magnetic Resonance Imaging (MRI) is pivotal in medical diagnostics, offering essential multi-contrast imaging capabilities. However, MRI quality is often compromised by inherent noise, which can hinder both image clarity and analytical accuracy. This thesis presents the "Corruption2Self" (C2S) framework, a self-supervised method for multi-contrast MRI denoising. C2S utilizes self-generated pseudo-labels from noisy data to enhance contrast fusion and Signal-to-Noise Ratio (SNR), providing a robust solution that facilitates shorter scanning times or improved spatial resolution—critical factors in enhancing patient experience and diagnostic precision. Comparative tests on the M4Raw dataset show that C2S substantially surpasses traditional methods like BM3D and Noise2Self in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). These results underscore the potential of self-supervised learning to improve multi-contrast MRI image quality.","abstract_has_math":false,"creators":["Tu, Jiachen"],"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":["Lam, Fan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-03","date_published":"2024-05-03","updated_at":"2026-07-22T22:25:02Z","subjects":["Mri","Denoising","Self-supervised Learning","Multi-contrast Mri"],"languages":["eng","en"],"rights":["Copyright 2024 Jiachen Tu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124727","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lam, Fan"]},{"key":"dc:creator","label":"Author","values":["Tu, Jiachen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05-03","2024-05"]},{"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":["Mri","Denoising","Self-supervised Learning","Multi-contrast Mri"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng","en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Jiachen Tu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124727"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Magnetic Resonance Imaging (MRI) is pivotal in medical diagnostics, offering essential multi-contrast imaging capabilities. However, MRI quality is often compromised by inherent noise, which can hinder both image clarity and analytical accuracy. This thesis presents the \"Corruption2Self\" (C2S) framework, a self-supervised method for multi-contrast MRI denoising. C2S utilizes self-generated pseudo-labels from noisy data to enhance contrast fusion and Signal-to-Noise Ratio (SNR), providing a robust solution that facilitates shorter scanning times or improved spatial resolution—critical factors in enhancing patient experience and diagnostic precision. Comparative tests on the M4Raw dataset show that C2S substantially surpasses traditional methods like BM3D and Noise2Self in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). These results underscore the potential of self-supervised learning to improve multi-contrast MRI image quality.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Jiachen Tu, accepted the attached license on 2024-05-02 at 19:23.","The student, Jiachen Tu, submitted this Thesis for approval on 2024-05-02 at 19:26.","This Thesis was approved for publication on 2024-05-03 at 09:16.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20769 on 2024-09-16 at 00:51:17"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Self-supervised multi-contrast MRI denoising"]}]}],"canonical_facts":{"dc:contributor":["Lam, Fan"],"dc:creator":["Tu, Jiachen"],"dc:date":["2024-05-03","2024-05"],"dc:description":["Magnetic Resonance Imaging (MRI) is pivotal in medical diagnostics, offering essential multi-contrast imaging capabilities. However, MRI quality is often compromised by inherent noise, which can hinder both image clarity and analytical accuracy. This thesis presents the \"Corruption2Self\" (C2S) framework, a self-supervised method for multi-contrast MRI denoising. C2S utilizes self-generated pseudo-labels from noisy data to enhance contrast fusion and Signal-to-Noise Ratio (SNR), providing a robust solution that facilitates shorter scanning times or improved spatial resolution—critical factors in enhancing patient experience and diagnostic precision. Comparative tests on the M4Raw dataset show that C2S substantially surpasses traditional methods like BM3D and Noise2Self in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). These results underscore the potential of self-supervised learning to improve multi-contrast MRI image quality.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Jiachen Tu, accepted the attached license on 2024-05-02 at 19:23.","The student, Jiachen Tu, submitted this Thesis for approval on 2024-05-02 at 19:26.","This Thesis was approved for publication on 2024-05-03 at 09:16.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20769 on 2024-09-16 at 00:51:17"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124727"],"dc:language":["eng","en"],"dc:rights":["Copyright 2024 Jiachen Tu"],"dc:subject":["Mri","Denoising","Self-supervised Learning","Multi-contrast Mri"],"dc:title":["Self-supervised multi-contrast MRI denoising"],"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":"2026-07-22T22:25:02Z"}