{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110754"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110754","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Applications of U-net to diffuse optical tomography data: Image reconstruction and superresolution","abstract":"Di use optical tomography (DOT) is being investigated for effective functional brain imaging. It serves as a cheaper, less bulky and safer alternative to fMRI imaging which is the current gold standard. However, due to the complicated nature of the system, analytical reconstruction approaches begin to fail. Currently functional signal activations are reconstructed using an analytical approach but the quality of the image, especially in terms of resolution, is low. This is one of the primary barriers to making DOT the gold standard for functional brain imaging. This thesis presents the theory behind the inverse problem and discusses possible solutions to image reconstruction and superresolution problems in the DOT brain imaging space. With the growth in deep learning and its applications in medical imaging, a U-net based architecture is proposed to learn the mapping and estimate a higher resolution image. This work shows that the proposed deep learning model trained on simulated images from real-world fMRI images of the human brain can reconstruct higher resolution images while reducing the number of hallucinations.","abstract_html":"Di use optical tomography (DOT) is being investigated for effective functional brain imaging. It serves as a cheaper, less bulky and safer alternative to fMRI imaging which is the current gold standard. However, due to the complicated nature of the system, analytical reconstruction approaches begin to fail. Currently functional signal activations are reconstructed using an analytical approach but the quality of the image, especially in terms of resolution, is low. This is one of the primary barriers to making DOT the gold standard for functional brain imaging. This thesis presents the theory behind the inverse problem and discusses possible solutions to image reconstruction and superresolution problems in the DOT brain imaging space. With the growth in deep learning and its applications in medical imaging, a U-net based architecture is proposed to learn the mapping and estimate a higher resolution image. This work shows that the proposed deep learning model trained on simulated images from real-world fMRI images of the human brain can reconstruct higher resolution images while reducing the number of hallucinations.","abstract_has_math":false,"creators":["Muralidaran, Siddharth"],"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":["Anastasio, Mark A"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T02:34:50Z","date_published":"2021-09-17T02:34:50Z","updated_at":"2026-07-22T22:24:52Z","subjects":["Diffuse Optical Tomography","Image Reconstruction","Deep Learning","Inverse Problems","Superresolution","U-net"],"languages":["en"],"rights":["Copyright 2021 Siddharth Muralidaran"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110754","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Anastasio, Mark A"]},{"key":"dc:creator","label":"Author","values":["Muralidaran, Siddharth"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T02:34:50Z","2023-09-17T02:34:57Z","2021-04-28","2021-05"]},{"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":["Diffuse Optical Tomography","Image Reconstruction","Deep Learning","Inverse Problems","Superresolution","U-net"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Siddharth Muralidaran"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110754"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Di use optical tomography (DOT) is being investigated for effective functional brain imaging. It serves as a cheaper, less bulky and safer alternative to fMRI imaging which is the current gold standard. However, due to the complicated nature of the system, analytical reconstruction approaches begin to fail. Currently functional signal activations are reconstructed using an analytical approach but the quality of the image, especially in terms of resolution, is low. This is one of the primary barriers to making DOT the gold standard for functional brain imaging. This thesis presents the theory behind the inverse problem and discusses possible solutions to image reconstruction and superresolution problems in the DOT brain imaging space. With the growth in deep learning and its applications in medical imaging, a U-net based architecture is proposed to learn the mapping and estimate a higher resolution image. This work shows that the proposed deep learning model trained on simulated images from real-world fMRI images of the human brain can reconstruct higher resolution images while reducing the number of hallucinations.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Siddharth Muralidaran, accepted the attached license on 2021-04-28 at 13:55.","The student, Siddharth Muralidaran, submitted this Thesis for approval on 2021-04-28 at 14:03.","This Thesis was approved for publication on 2021-04-28 at 14:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16605 on 2021-09-16 at 17:06:40","Made available in DSpace on 2021-09-17T02:34:50Z (GMT). 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It serves as a cheaper, less bulky and safer alternative to fMRI imaging which is the current gold standard. However, due to the complicated nature of the system, analytical reconstruction approaches begin to fail. Currently functional signal activations are reconstructed using an analytical approach but the quality of the image, especially in terms of resolution, is low. This is one of the primary barriers to making DOT the gold standard for functional brain imaging. This thesis presents the theory behind the inverse problem and discusses possible solutions to image reconstruction and superresolution problems in the DOT brain imaging space. With the growth in deep learning and its applications in medical imaging, a U-net based architecture is proposed to learn the mapping and estimate a higher resolution image. This work shows that the proposed deep learning model trained on simulated images from real-world fMRI images of the human brain can reconstruct higher resolution images while reducing the number of hallucinations.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Siddharth Muralidaran, accepted the attached license on 2021-04-28 at 13:55.","The student, Siddharth Muralidaran, submitted this Thesis for approval on 2021-04-28 at 14:03.","This Thesis was approved for publication on 2021-04-28 at 14:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16605 on 2021-09-16 at 17:06:40","Made available in DSpace on 2021-09-17T02:34:50Z (GMT). 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