{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105240"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105240","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Recovery of high-resolution magnetic field distribution inside the brain from limited MRI data using machine learning prior","abstract":"High-resolution field maps in brain magnetic resonance imaging (MRI) scans provide the field distribution information inside the brain which is essential in reconstructing high-quality MR images with no artifacts and distortions. These high-quality images are highly desired in clinical applications. However, the high-resolution field maps, which are used to obtain high-quality MR images, come with the cost of scan time. Recent advances in deep neural networks, particularly the generative adversarial networks (GANs), can learn the prior information through examples and generate the high-resolution field map using only one low-resolution field map counter- part. In this work, we apply the deep learning methods to solve the field map super-resolution problem and show that our GAN-based approach has the potential to generate the high-resolution field maps as a post-processing step and to speed up many clinical MRI applications.","abstract_html":"High-resolution field maps in brain magnetic resonance imaging (MRI) scans provide the field distribution information inside the brain which is essential in reconstructing high-quality MR images with no artifacts and distortions. These high-quality images are highly desired in clinical applications. However, the high-resolution field maps, which are used to obtain high-quality MR images, come with the cost of scan time. Recent advances in deep neural networks, particularly the generative adversarial networks (GANs), can learn the prior information through examples and generate the high-resolution field map using only one low-resolution field map counter- part. In this work, we apply the deep learning methods to solve the field map super-resolution problem and show that our GAN-based approach has the potential to generate the high-resolution field maps as a post-processing step and to speed up many clinical MRI applications.","abstract_has_math":false,"creators":["Lan, Rui"],"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":2019,"date_issued":"2019-08-23T20:48:21Z","date_published":"2019-08-23T20:48:21Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Field map","Super-resolution","Generative adversarial networks"],"languages":["en"],"rights":["Copyright 2019 Rui Lan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105240","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":["Lan, Rui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:48:21Z","2021-08-24T09:15:20Z","2019-04-22","2019-05"]},{"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":["Field map","Super-resolution","Generative adversarial networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Rui Lan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105240"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["High-resolution field maps in brain magnetic resonance imaging (MRI) scans provide the field distribution information inside the brain which is essential in reconstructing high-quality MR images with no artifacts and distortions. These high-quality images are highly desired in clinical applications. However, the high-resolution field maps, which are used to obtain high-quality MR images, come with the cost of scan time. Recent advances in deep neural networks, particularly the generative adversarial networks (GANs), can learn the prior information through examples and generate the high-resolution field map using only one low-resolution field map counter- part. In this work, we apply the deep learning methods to solve the field map super-resolution problem and show that our GAN-based approach has the potential to generate the high-resolution field maps as a post-processing step and to speed up many clinical MRI applications.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Rui Lan, accepted the attached license on 2019-04-22 at 09:46.","The student, Rui Lan, submitted this Thesis for approval on 2019-04-22 at 10:17.","This Thesis was approved for publication on 2019-04-22 at 14:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13812 on 2019-08-22 at 16:23:28","Made available in DSpace on 2019-08-23T20:48:21Z (GMT). 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These high-quality images are highly desired in clinical applications. However, the high-resolution field maps, which are used to obtain high-quality MR images, come with the cost of scan time. Recent advances in deep neural networks, particularly the generative adversarial networks (GANs), can learn the prior information through examples and generate the high-resolution field map using only one low-resolution field map counter- part. In this work, we apply the deep learning methods to solve the field map super-resolution problem and show that our GAN-based approach has the potential to generate the high-resolution field maps as a post-processing step and to speed up many clinical MRI applications.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Rui Lan, accepted the attached license on 2019-04-22 at 09:46.","The student, Rui Lan, submitted this Thesis for approval on 2019-04-22 at 10:17.","This Thesis was approved for publication on 2019-04-22 at 14:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13812 on 2019-08-22 at 16:23:28","Made available in DSpace on 2019-08-23T20:48:21Z (GMT). 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