{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/109445"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/109445","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"X-ray CT scatter correction by a physics-motivated deep neural network","abstract":"A fundamental problem in X-ray Computed Tomography (CT) is the scatter occurring due to the interaction of photons with the imaged object. Unless it is corrected, this phenomenon manifests itself as degradations in the reconstructions in the form of various artifacts. This makes scatter correction a critical step to obtain the desired reconstruction quality. Scatter correction methods consist of two groups: hardware-based and software-based. Despite success in specific settings, hardware-based methods require modification in the hardware or an increase in the scan time or dose. This makes software-based methods attractive. In this context, Monte-Carlo based scatter estimation, analytical-numerical and kernel-based methods were developed. Furthermore, the capacity of data-driven approaches to tackle this problem was recently demonstrated. In this thesis, two novel physics-motivated deep-learning-based methods are proposed. The methods estimate and correct for the scatter in the obtained projection measurements. They incorporate both an initial reconstruction of the object of interest and the scatter-corrupted measurements related to it. They use a common specific deep neural network architecture and a cost function adapted to the problem. Numerical experiments with data obtained by Monte-Carlo simulations of the imaging of phantoms reveal noticeable improvement over a recent projection-domain deep neural network correction method.","abstract_html":"A fundamental problem in X-ray Computed Tomography (CT) is the scatter occurring due to the interaction of photons with the imaged object. Unless it is corrected, this phenomenon manifests itself as degradations in the reconstructions in the form of various artifacts. This makes scatter correction a critical step to obtain the desired reconstruction quality. Scatter correction methods consist of two groups: hardware-based and software-based. Despite success in specific settings, hardware-based methods require modification in the hardware or an increase in the scan time or dose. This makes software-based methods attractive. In this context, Monte-Carlo based scatter estimation, analytical-numerical and kernel-based methods were developed. Furthermore, the capacity of data-driven approaches to tackle this problem was recently demonstrated. In this thesis, two novel physics-motivated deep-learning-based methods are proposed. The methods estimate and correct for the scatter in the obtained projection measurements. They incorporate both an initial reconstruction of the object of interest and the scatter-corrupted measurements related to it. They use a common specific deep neural network architecture and a cost function adapted to the problem. Numerical experiments with data obtained by Monte-Carlo simulations of the imaging of phantoms reveal noticeable improvement over a recent projection-domain deep neural network correction method.","abstract_has_math":false,"creators":["Iskender, Berk"],"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":["Bresler, Yoram"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-03-05T21:38:23Z","date_published":"2021-03-05T21:38:23Z","updated_at":"2026-07-22T22:24:50Z","subjects":["Computed Tomography (CT)","Deep learning","X-ray CT scatter","Scatter estimation","Scatter correction","Physics-motivated","Monte-Carlo"],"languages":["en"],"rights":["Copyright 2020 Berk Iskender"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/109445","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bresler, Yoram"]},{"key":"dc:creator","label":"Author","values":["Iskender, Berk"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-03-05T21:38:23Z","2020-12-08","2020-12"]},{"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":["Computed Tomography (CT)","Deep learning","X-ray CT scatter","Scatter estimation","Scatter correction","Physics-motivated","Monte-Carlo"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Berk Iskender"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/109445"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A fundamental problem in X-ray Computed Tomography (CT) is the scatter occurring due to the interaction of photons with the imaged object. Unless it is corrected, this phenomenon manifests itself as degradations in the reconstructions in the form of various artifacts. This makes scatter correction a critical step to obtain the desired reconstruction quality. Scatter correction methods consist of two groups: hardware-based and software-based. Despite success in specific settings, hardware-based methods require modification in the hardware or an increase in the scan time or dose. This makes software-based methods attractive. In this context, Monte-Carlo based scatter estimation, analytical-numerical and kernel-based methods were developed. Furthermore, the capacity of data-driven approaches to tackle this problem was recently demonstrated. In this thesis, two novel physics-motivated deep-learning-based methods are proposed. The methods estimate and correct for the scatter in the obtained projection measurements. They incorporate both an initial reconstruction of the object of interest and the scatter-corrupted measurements related to it. They use a common specific deep neural network architecture and a cost function adapted to the problem. Numerical experiments with data obtained by Monte-Carlo simulations of the imaging of phantoms reveal noticeable improvement over a recent projection-domain deep neural network correction method.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-03-04 without embargo terms","The student, Berk Iskender, accepted the attached license on 2020-12-08 at 09:58.","The student, Berk Iskender, submitted this Thesis for approval on 2020-12-08 at 10:20.","This Thesis was approved for publication on 2020-12-08 at 11:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16087 on 2021-03-04 at 15:36:17","Made available in DSpace on 2021-03-05T21:38:23Z (GMT). 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Scatter correction methods consist of two groups: hardware-based and software-based. Despite success in specific settings, hardware-based methods require modification in the hardware or an increase in the scan time or dose. This makes software-based methods attractive. In this context, Monte-Carlo based scatter estimation, analytical-numerical and kernel-based methods were developed. Furthermore, the capacity of data-driven approaches to tackle this problem was recently demonstrated. In this thesis, two novel physics-motivated deep-learning-based methods are proposed. The methods estimate and correct for the scatter in the obtained projection measurements. They incorporate both an initial reconstruction of the object of interest and the scatter-corrupted measurements related to it. They use a common specific deep neural network architecture and a cost function adapted to the problem. Numerical experiments with data obtained by Monte-Carlo simulations of the imaging of phantoms reveal noticeable improvement over a recent projection-domain deep neural network correction method.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-03-04 without embargo terms","The student, Berk Iskender, accepted the attached license on 2020-12-08 at 09:58.","The student, Berk Iskender, submitted this Thesis for approval on 2020-12-08 at 10:20.","This Thesis was approved for publication on 2020-12-08 at 11:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16087 on 2021-03-04 at 15:36:17","Made available in DSpace on 2021-03-05T21:38:23Z (GMT). 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