{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124655"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124655","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Application of neural networks to the reconstruction of diffuse optical tomography neuroimages","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Granstedt, Jason Louis"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Anastasio, Mark A","Forsyth, David A","Wang, Yuxiong","Culver, Joseph P"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["Neural Networks","Deep Learning","Image Reconstruction","Diffuse Optical Tomography","Neuroimaging"],"languages":["en","eng"],"rights":["Copyright 2024 Jason Granstedt"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124655","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Anastasio, Mark A","Forsyth, David A","Wang, Yuxiong","Culver, Joseph P"]},{"key":"dc:creator","label":"Author","values":["Granstedt, Jason Louis"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-15"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Neural Networks","Deep Learning","Image Reconstruction","Diffuse Optical Tomography","Neuroimaging"]}]},{"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 2024 Jason Granstedt"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124655"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Jason Granstedt, accepted the attached license on 2024-04-11 at 17:02.","The student, Jason Granstedt, submitted this Dissertation for approval on 2024-04-11 at 17:25.","This Dissertation was approved for publication on 2024-04-15 at 15:23.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20371 on 2024-09-16 at 00:49:16","Neuroimaging has led to several advancements in understanding brain functionality and neurological disorders. Diffuse optical tomography (DOT) offers a safe and portable neuroimaging modality. However, the resolution of DOT lags behind functional magnetic resonance imaging (fMRI). One avenue for increasing this resolution is via more advanced reconstruction methods. Neural networks have recently achieved state-of-the-art performance for many reconstruction problems, but conventional methods require a set of paired images for training not currently available for DOT. This project proposes a method for improving the resolution of DOT images via neural network reconstruction techniques. A dataset of paired images for training networks is constructed through exploiting the correlation between DOT and fMRI neuroimages. A virtual imaging pipeline was constructed to simulate DOT measurements from fMRI images and applied to a dataset with retinotopic stimuli. An SVD and null space analysis were performed to characterize the DOT imaging system. Three state-of-the-art network architectures are modified to support the ill-conditioned low-rank DOT imaging operator and employed for the reconstruction problem. Hallucination maps and in-distribution Rayleigh tasks were developed to properly assess the quality of the network reconstructed images and compare them with conventional methods. The networks trained on simulated data were then adapted to in-vivo DOT data and compared to the current state-of-the-art reconstruction approach. Statistically significant decoding results on in-vivo DOT data were observed for several models for the considered DOT imaging systems, indicating that the developed methods are promising for future development of network-based reconstructions for DOT applications."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Application of neural networks to the reconstruction of diffuse optical tomography neuroimages"]}]}],"canonical_facts":{"dc:contributor":["Anastasio, Mark A","Forsyth, David A","Wang, Yuxiong","Culver, Joseph P"],"dc:creator":["Granstedt, Jason Louis"],"dc:date":["2024-05","2024-04-15"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Jason Granstedt, accepted the attached license on 2024-04-11 at 17:02.","The student, Jason Granstedt, submitted this Dissertation for approval on 2024-04-11 at 17:25.","This Dissertation was approved for publication on 2024-04-15 at 15:23.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20371 on 2024-09-16 at 00:49:16","Neuroimaging has led to several advancements in understanding brain functionality and neurological disorders. Diffuse optical tomography (DOT) offers a safe and portable neuroimaging modality. However, the resolution of DOT lags behind functional magnetic resonance imaging (fMRI). One avenue for increasing this resolution is via more advanced reconstruction methods. Neural networks have recently achieved state-of-the-art performance for many reconstruction problems, but conventional methods require a set of paired images for training not currently available for DOT. This project proposes a method for improving the resolution of DOT images via neural network reconstruction techniques. A dataset of paired images for training networks is constructed through exploiting the correlation between DOT and fMRI neuroimages. A virtual imaging pipeline was constructed to simulate DOT measurements from fMRI images and applied to a dataset with retinotopic stimuli. An SVD and null space analysis were performed to characterize the DOT imaging system. Three state-of-the-art network architectures are modified to support the ill-conditioned low-rank DOT imaging operator and employed for the reconstruction problem. Hallucination maps and in-distribution Rayleigh tasks were developed to properly assess the quality of the network reconstructed images and compare them with conventional methods. The networks trained on simulated data were then adapted to in-vivo DOT data and compared to the current state-of-the-art reconstruction approach. Statistically significant decoding results on in-vivo DOT data were observed for several models for the considered DOT imaging systems, indicating that the developed methods are promising for future development of network-based reconstructions for DOT applications."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124655"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Jason Granstedt"],"dc:subject":["Neural Networks","Deep Learning","Image Reconstruction","Diffuse Optical Tomography","Neuroimaging"],"dc:title":["Application of neural networks to the reconstruction of diffuse optical tomography neuroimages"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}