Monitored Reconstruction: Computed Tomography as an Anytime Algorithm
Computed tomography is an important technique for non-destructive analysis of an object's internal structure, relevant for scientific studies, medical applications, and industry. Pressing challenges emerging in the field of tomographic imaging include speeding up reconstruction, reducing the ti...
Gespeichert in:
Veröffentlicht in: | IEEE access 2020, Vol.8, p.110759-110774 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 110774 |
---|---|
container_issue | |
container_start_page | 110759 |
container_title | IEEE access |
container_volume | 8 |
creator | Bulatov, Konstantin Chukalina, Marina Buzmakov, Alexey Nikolaev, Dmitry Arlazarov, Vladimir V. |
description | Computed tomography is an important technique for non-destructive analysis of an object's internal structure, relevant for scientific studies, medical applications, and industry. Pressing challenges emerging in the field of tomographic imaging include speeding up reconstruction, reducing the time required to obtain the X-ray projections, and reducing the radiation dose imparted to the object. In this paper, we introduce a model of a monitored reconstruction process, in which the acquiring of projections is interspersed with image reconstruction. This model allows to examine the tomographic reconstruction process as an anytime algorithm and consider a problem of finding the optimal stopping point, corresponding to the required number of X-ray projections for the currently scanned object. We outline the theoretical framework for the monitored reconstruction, propose ways of constructing stopping rules for various reconstruction quality metrics and provide their experimental evaluation. Due to stopping at different times for different objects, the proposed approach allows to achieve a higher mean reconstruction quality for a given mean number of X-ray projections. Conversely, fewer projections on average are used to achieve the same mean reconstruction quality. |
doi_str_mv | 10.1109/ACCESS.2020.3002019 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2454616938</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9115485</ieee_id><doaj_id>oai_doaj_org_article_3d05fc7c2eea403d96ac132b1dde902c</doaj_id><sourcerecordid>2454616938</sourcerecordid><originalsourceid>FETCH-LOGICAL-c408t-628271c54deebf46e93a15737e3a890f46af6104d8b15f3c8b7fe09d3961f3ad3</originalsourceid><addsrcrecordid>eNpNkF9LwzAUxYMoOHSfYC8Fnzvzp2kb30qZOpgIbj6HNLndOtamptnDvr2ZHcM83FzOveck_BCaETwnBIvnoiwX6_WcYornDIdKxA2aUJKKmHGW3v7r79F0GPY4nDxIPJugxYftGm8dmOgLtO0G747aN7Z7iUrb9kcfBhvb2q1T_e4UqSFSXVR0J9-0EBWHrXWN37WP6K5WhwGml_sBfb8uNuV7vPp8W5bFKtYJzn2c0pxmRPPEAFR1koJgivCMZcBULnBQVJ0SnJi8IrxmOq-yGrAwTKSkZsqwB7Qcc41Ve9m7plXuJK1q5J9g3VYq5xt9AMkM5rXONAVQCWZGpEoTRitiDAhMdch6GrN6Z3-OMHi5t0fXhe9LmvAkDYBYHrbYuKWdHQYH9fVVguUZvxzxyzN-ecEfXLPR1QDA1SEI4UnO2S-CWoCZ</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2454616938</pqid></control><display><type>article</type><title>Monitored Reconstruction: Computed Tomography as an Anytime Algorithm</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Bulatov, Konstantin ; Chukalina, Marina ; Buzmakov, Alexey ; Nikolaev, Dmitry ; Arlazarov, Vladimir V.</creator><creatorcontrib>Bulatov, Konstantin ; Chukalina, Marina ; Buzmakov, Alexey ; Nikolaev, Dmitry ; Arlazarov, Vladimir V.</creatorcontrib><description>Computed tomography is an important technique for non-destructive analysis of an object's internal structure, relevant for scientific studies, medical applications, and industry. Pressing challenges emerging in the field of tomographic imaging include speeding up reconstruction, reducing the time required to obtain the X-ray projections, and reducing the radiation dose imparted to the object. In this paper, we introduce a model of a monitored reconstruction process, in which the acquiring of projections is interspersed with image reconstruction. This model allows to examine the tomographic reconstruction process as an anytime algorithm and consider a problem of finding the optimal stopping point, corresponding to the required number of X-ray projections for the currently scanned object. We outline the theoretical framework for the monitored reconstruction, propose ways of constructing stopping rules for various reconstruction quality metrics and provide their experimental evaluation. Due to stopping at different times for different objects, the proposed approach allows to achieve a higher mean reconstruction quality for a given mean number of X-ray projections. Conversely, fewer projections on average are used to achieve the same mean reconstruction quality.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2020.3002019</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Anytime algorithms ; Computed tomography ; dose reduction ; Image reconstruction ; Medical diagnostic imaging ; monitored reconstruction ; Monitoring ; Nondestructive testing ; optimal stopping ; Radiation dosage ; Tomography ; X-ray imaging ; X-ray tomography</subject><ispartof>IEEE access, 2020, Vol.8, p.110759-110774</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-628271c54deebf46e93a15737e3a890f46af6104d8b15f3c8b7fe09d3961f3ad3</citedby><cites>FETCH-LOGICAL-c408t-628271c54deebf46e93a15737e3a890f46af6104d8b15f3c8b7fe09d3961f3ad3</cites><orcidid>0000-0003-1644-5162 ; 0000-0003-0539-5606 ; 0000-0003-3260-9104 ; 0000-0002-1410-5175 ; 0000-0001-5560-7668</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9115485$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2095,4009,27612,27902,27903,27904,54911</link.rule.ids></links><search><creatorcontrib>Bulatov, Konstantin</creatorcontrib><creatorcontrib>Chukalina, Marina</creatorcontrib><creatorcontrib>Buzmakov, Alexey</creatorcontrib><creatorcontrib>Nikolaev, Dmitry</creatorcontrib><creatorcontrib>Arlazarov, Vladimir V.</creatorcontrib><title>Monitored Reconstruction: Computed Tomography as an Anytime Algorithm</title><title>IEEE access</title><addtitle>Access</addtitle><description>Computed tomography is an important technique for non-destructive analysis of an object's internal structure, relevant for scientific studies, medical applications, and industry. Pressing challenges emerging in the field of tomographic imaging include speeding up reconstruction, reducing the time required to obtain the X-ray projections, and reducing the radiation dose imparted to the object. In this paper, we introduce a model of a monitored reconstruction process, in which the acquiring of projections is interspersed with image reconstruction. This model allows to examine the tomographic reconstruction process as an anytime algorithm and consider a problem of finding the optimal stopping point, corresponding to the required number of X-ray projections for the currently scanned object. We outline the theoretical framework for the monitored reconstruction, propose ways of constructing stopping rules for various reconstruction quality metrics and provide their experimental evaluation. Due to stopping at different times for different objects, the proposed approach allows to achieve a higher mean reconstruction quality for a given mean number of X-ray projections. Conversely, fewer projections on average are used to achieve the same mean reconstruction quality.</description><subject>Algorithms</subject><subject>Anytime algorithms</subject><subject>Computed tomography</subject><subject>dose reduction</subject><subject>Image reconstruction</subject><subject>Medical diagnostic imaging</subject><subject>monitored reconstruction</subject><subject>Monitoring</subject><subject>Nondestructive testing</subject><subject>optimal stopping</subject><subject>Radiation dosage</subject><subject>Tomography</subject><subject>X-ray imaging</subject><subject>X-ray tomography</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkF9LwzAUxYMoOHSfYC8Fnzvzp2kb30qZOpgIbj6HNLndOtamptnDvr2ZHcM83FzOveck_BCaETwnBIvnoiwX6_WcYornDIdKxA2aUJKKmHGW3v7r79F0GPY4nDxIPJugxYftGm8dmOgLtO0G747aN7Z7iUrb9kcfBhvb2q1T_e4UqSFSXVR0J9-0EBWHrXWN37WP6K5WhwGml_sBfb8uNuV7vPp8W5bFKtYJzn2c0pxmRPPEAFR1koJgivCMZcBULnBQVJ0SnJi8IrxmOq-yGrAwTKSkZsqwB7Qcc41Ve9m7plXuJK1q5J9g3VYq5xt9AMkM5rXONAVQCWZGpEoTRitiDAhMdch6GrN6Z3-OMHi5t0fXhe9LmvAkDYBYHrbYuKWdHQYH9fVVguUZvxzxyzN-ecEfXLPR1QDA1SEI4UnO2S-CWoCZ</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Bulatov, Konstantin</creator><creator>Chukalina, Marina</creator><creator>Buzmakov, Alexey</creator><creator>Nikolaev, Dmitry</creator><creator>Arlazarov, Vladimir V.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-1644-5162</orcidid><orcidid>https://orcid.org/0000-0003-0539-5606</orcidid><orcidid>https://orcid.org/0000-0003-3260-9104</orcidid><orcidid>https://orcid.org/0000-0002-1410-5175</orcidid><orcidid>https://orcid.org/0000-0001-5560-7668</orcidid></search><sort><creationdate>2020</creationdate><title>Monitored Reconstruction: Computed Tomography as an Anytime Algorithm</title><author>Bulatov, Konstantin ; Chukalina, Marina ; Buzmakov, Alexey ; Nikolaev, Dmitry ; Arlazarov, Vladimir V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-628271c54deebf46e93a15737e3a890f46af6104d8b15f3c8b7fe09d3961f3ad3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Anytime algorithms</topic><topic>Computed tomography</topic><topic>dose reduction</topic><topic>Image reconstruction</topic><topic>Medical diagnostic imaging</topic><topic>monitored reconstruction</topic><topic>Monitoring</topic><topic>Nondestructive testing</topic><topic>optimal stopping</topic><topic>Radiation dosage</topic><topic>Tomography</topic><topic>X-ray imaging</topic><topic>X-ray tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bulatov, Konstantin</creatorcontrib><creatorcontrib>Chukalina, Marina</creatorcontrib><creatorcontrib>Buzmakov, Alexey</creatorcontrib><creatorcontrib>Nikolaev, Dmitry</creatorcontrib><creatorcontrib>Arlazarov, Vladimir V.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bulatov, Konstantin</au><au>Chukalina, Marina</au><au>Buzmakov, Alexey</au><au>Nikolaev, Dmitry</au><au>Arlazarov, Vladimir V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Monitored Reconstruction: Computed Tomography as an Anytime Algorithm</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2020</date><risdate>2020</risdate><volume>8</volume><spage>110759</spage><epage>110774</epage><pages>110759-110774</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Computed tomography is an important technique for non-destructive analysis of an object's internal structure, relevant for scientific studies, medical applications, and industry. Pressing challenges emerging in the field of tomographic imaging include speeding up reconstruction, reducing the time required to obtain the X-ray projections, and reducing the radiation dose imparted to the object. In this paper, we introduce a model of a monitored reconstruction process, in which the acquiring of projections is interspersed with image reconstruction. This model allows to examine the tomographic reconstruction process as an anytime algorithm and consider a problem of finding the optimal stopping point, corresponding to the required number of X-ray projections for the currently scanned object. We outline the theoretical framework for the monitored reconstruction, propose ways of constructing stopping rules for various reconstruction quality metrics and provide their experimental evaluation. Due to stopping at different times for different objects, the proposed approach allows to achieve a higher mean reconstruction quality for a given mean number of X-ray projections. Conversely, fewer projections on average are used to achieve the same mean reconstruction quality.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.3002019</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0003-1644-5162</orcidid><orcidid>https://orcid.org/0000-0003-0539-5606</orcidid><orcidid>https://orcid.org/0000-0003-3260-9104</orcidid><orcidid>https://orcid.org/0000-0002-1410-5175</orcidid><orcidid>https://orcid.org/0000-0001-5560-7668</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2169-3536 |
ispartof | IEEE access, 2020, Vol.8, p.110759-110774 |
issn | 2169-3536 2169-3536 |
language | eng |
recordid | cdi_proquest_journals_2454616938 |
source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
subjects | Algorithms Anytime algorithms Computed tomography dose reduction Image reconstruction Medical diagnostic imaging monitored reconstruction Monitoring Nondestructive testing optimal stopping Radiation dosage Tomography X-ray imaging X-ray tomography |
title | Monitored Reconstruction: Computed Tomography as an Anytime Algorithm |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-23T18%3A31%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Monitored%20Reconstruction:%20Computed%20Tomography%20as%20an%20Anytime%20Algorithm&rft.jtitle=IEEE%20access&rft.au=Bulatov,%20Konstantin&rft.date=2020&rft.volume=8&rft.spage=110759&rft.epage=110774&rft.pages=110759-110774&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2020.3002019&rft_dat=%3Cproquest_cross%3E2454616938%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2454616938&rft_id=info:pmid/&rft_ieee_id=9115485&rft_doaj_id=oai_doaj_org_article_3d05fc7c2eea403d96ac132b1dde902c&rfr_iscdi=true |