Loading…

Factorized Projection-Domain Spatio-Temporal Regularization for Dynamic Tomography

Dynamic tomography is an ill-posed inverse problem where the object evolves during the sequential acquisition of projections. The goal is to reconstruct the object for each time instant. However, performing a direct reconstruction using this inconsistent set of projections is impossible. In this pap...

Full description

Saved in:

Bibliographic Details
Main Authors:	Iskender, Berk, Klasky, Marc L., Patterson, Brian M., Bresler, Yoram
Format:	Conference Proceeding
Language:	English
Subjects:	Acoustics Bilinear Computed tomography Dynamic tomography Heuristic algorithms Inverse problems Partially-separable Signal processing Signal processing algorithms Spatio-temporal regularization Speech processing
Online Access:	Request full text
Tags:	Add Tag No Tags, Be the first to tag this record!

cited_by
cites
container_end_page	5
container_issue
container_start_page	1
container_title
container_volume
creator	Iskender, Berk Klasky, Marc L. Patterson, Brian M. Bresler, Yoram
description	Dynamic tomography is an ill-posed inverse problem where the object evolves during the sequential acquisition of projections. The goal is to reconstruct the object for each time instant. However, performing a direct reconstruction using this inconsistent set of projections is impossible. In this paper, we propose an object-domain recovery algorithm using a variational formulation that combines a partially separable spatio-temporal prior with a basic total-variation spatial regularization for improved performance, while preserving full interpretability. Numerical experiments on data derived from real object CT data demonstrate the advantages of the proposed algorithm over recent projection-domain and deep-prior-based methods.
doi_str_mv	10.1109/ICASSP49357.2023.10095791
format	conference_proceeding
fullrecord	<record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_10095791</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10095791</ieee_id><sourcerecordid>10095791</sourcerecordid><originalsourceid>FETCH-LOGICAL-i1701-13de94631df00e96d62311f1a261573d85b2446ea8d3793f6f22e5e5e7d60d6b3</originalsourceid><addsrcrecordid>eNo1UFFLwzAYjILgNv0HPsQf0Pp9SZs0j7K5KQwcawXfRtYkM6NtSjof5q-3onIPxx3HcRwh9wgpIqiHl_ljWW4yxXOZMmA8RQCVS4UXZIqSFSg4k_KSTBiXKkEF79dkOgxHAChkVkzIdqnrU4j-yxq6ieFo65MPXbIIrfYdLXs9yqSybR-ibujWHj4bPaZ_7I66EOni3OnW17QKbThE3X-cb8iV081gb_94Rt6WT9X8OVm_rsa968SjBEyQG6sywdE4AKuEEYwjOtRMYC65KfI9yzJhdWHG7dwJx5jNR0gjwIg9n5G7315vrd310bc6nnf_B_BvPzpRng</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Factorized Projection-Domain Spatio-Temporal Regularization for Dynamic Tomography</title><source>IEEE Xplore All Conference Series</source><creator>Iskender, Berk ; Klasky, Marc L. ; Patterson, Brian M. ; Bresler, Yoram</creator><creatorcontrib>Iskender, Berk ; Klasky, Marc L. ; Patterson, Brian M. ; Bresler, Yoram</creatorcontrib><description>Dynamic tomography is an ill-posed inverse problem where the object evolves during the sequential acquisition of projections. The goal is to reconstruct the object for each time instant. However, performing a direct reconstruction using this inconsistent set of projections is impossible. In this paper, we propose an object-domain recovery algorithm using a variational formulation that combines a partially separable spatio-temporal prior with a basic total-variation spatial regularization for improved performance, while preserving full interpretability. Numerical experiments on data derived from real object CT data demonstrate the advantages of the proposed algorithm over recent projection-domain and deep-prior-based methods.</description><identifier>EISSN: 2379-190X</identifier><identifier>EISBN: 1728163277</identifier><identifier>EISBN: 9781728163277</identifier><identifier>DOI: 10.1109/ICASSP49357.2023.10095791</identifier><language>eng</language><publisher>IEEE</publisher><subject>Acoustics ; Bilinear ; Computed tomography ; Dynamic tomography ; Heuristic algorithms ; Inverse problems ; Partially-separable ; Signal processing ; Signal processing algorithms ; Spatio-temporal regularization ; Speech processing</subject><ispartof>ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, p.1-5</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10095791$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,4050,4051,23930,23931,25140,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10095791$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Iskender, Berk</creatorcontrib><creatorcontrib>Klasky, Marc L.</creatorcontrib><creatorcontrib>Patterson, Brian M.</creatorcontrib><creatorcontrib>Bresler, Yoram</creatorcontrib><title>Factorized Projection-Domain Spatio-Temporal Regularization for Dynamic Tomography</title><title>ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</title><addtitle>ICASSP</addtitle><description>Dynamic tomography is an ill-posed inverse problem where the object evolves during the sequential acquisition of projections. The goal is to reconstruct the object for each time instant. However, performing a direct reconstruction using this inconsistent set of projections is impossible. In this paper, we propose an object-domain recovery algorithm using a variational formulation that combines a partially separable spatio-temporal prior with a basic total-variation spatial regularization for improved performance, while preserving full interpretability. Numerical experiments on data derived from real object CT data demonstrate the advantages of the proposed algorithm over recent projection-domain and deep-prior-based methods.</description><subject>Acoustics</subject><subject>Bilinear</subject><subject>Computed tomography</subject><subject>Dynamic tomography</subject><subject>Heuristic algorithms</subject><subject>Inverse problems</subject><subject>Partially-separable</subject><subject>Signal processing</subject><subject>Signal processing algorithms</subject><subject>Spatio-temporal regularization</subject><subject>Speech processing</subject><issn>2379-190X</issn><isbn>1728163277</isbn><isbn>9781728163277</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1UFFLwzAYjILgNv0HPsQf0Pp9SZs0j7K5KQwcawXfRtYkM6NtSjof5q-3onIPxx3HcRwh9wgpIqiHl_ljWW4yxXOZMmA8RQCVS4UXZIqSFSg4k_KSTBiXKkEF79dkOgxHAChkVkzIdqnrU4j-yxq6ieFo65MPXbIIrfYdLXs9yqSybR-ibujWHj4bPaZ_7I66EOni3OnW17QKbThE3X-cb8iV081gb_94Rt6WT9X8OVm_rsa968SjBEyQG6sywdE4AKuEEYwjOtRMYC65KfI9yzJhdWHG7dwJx5jNR0gjwIg9n5G7315vrd310bc6nnf_B_BvPzpRng</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Iskender, Berk</creator><creator>Klasky, Marc L.</creator><creator>Patterson, Brian M.</creator><creator>Bresler, Yoram</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2023</creationdate><title>Factorized Projection-Domain Spatio-Temporal Regularization for Dynamic Tomography</title><author>Iskender, Berk ; Klasky, Marc L. ; Patterson, Brian M. ; Bresler, Yoram</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i1701-13de94631df00e96d62311f1a261573d85b2446ea8d3793f6f22e5e5e7d60d6b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Acoustics</topic><topic>Bilinear</topic><topic>Computed tomography</topic><topic>Dynamic tomography</topic><topic>Heuristic algorithms</topic><topic>Inverse problems</topic><topic>Partially-separable</topic><topic>Signal processing</topic><topic>Signal processing algorithms</topic><topic>Spatio-temporal regularization</topic><topic>Speech processing</topic><toplevel>online_resources</toplevel><creatorcontrib>Iskender, Berk</creatorcontrib><creatorcontrib>Klasky, Marc L.</creatorcontrib><creatorcontrib>Patterson, Brian M.</creatorcontrib><creatorcontrib>Bresler, Yoram</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Iskender, Berk</au><au>Klasky, Marc L.</au><au>Patterson, Brian M.</au><au>Bresler, Yoram</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Factorized Projection-Domain Spatio-Temporal Regularization for Dynamic Tomography</atitle><btitle>ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</btitle><stitle>ICASSP</stitle><date>2023</date><risdate>2023</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><eissn>2379-190X</eissn><eisbn>1728163277</eisbn><eisbn>9781728163277</eisbn><abstract>Dynamic tomography is an ill-posed inverse problem where the object evolves during the sequential acquisition of projections. The goal is to reconstruct the object for each time instant. However, performing a direct reconstruction using this inconsistent set of projections is impossible. In this paper, we propose an object-domain recovery algorithm using a variational formulation that combines a partially separable spatio-temporal prior with a basic total-variation spatial regularization for improved performance, while preserving full interpretability. Numerical experiments on data derived from real object CT data demonstrate the advantages of the proposed algorithm over recent projection-domain and deep-prior-based methods.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP49357.2023.10095791</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record>
fulltext	fulltext_linktorsrc
identifier	EISSN: 2379-190X
ispartof	ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, p.1-5
issn	2379-190X
language	eng
recordid	cdi_ieee_primary_10095791
source	IEEE Xplore All Conference Series
subjects	Acoustics Bilinear Computed tomography Dynamic tomography Heuristic algorithms Inverse problems Partially-separable Signal processing Signal processing algorithms Spatio-temporal regularization Speech processing
title	Factorized Projection-Domain Spatio-Temporal Regularization for Dynamic Tomography
url	http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T01%3A03%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Factorized%20Projection-Domain%20Spatio-Temporal%20Regularization%20for%20Dynamic%20Tomography&rft.btitle=ICASSP%202023%20-%202023%20IEEE%20International%20Conference%20on%20Acoustics,%20Speech%20and%20Signal%20Processing%20(ICASSP)&rft.au=Iskender,%20Berk&rft.date=2023&rft.spage=1&rft.epage=5&rft.pages=1-5&rft.eissn=2379-190X&rft_id=info:doi/10.1109/ICASSP49357.2023.10095791&rft.eisbn=1728163277&rft.eisbn_list=9781728163277&rft_dat=%3Cieee_CHZPO%3E10095791%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i1701-13de94631df00e96d62311f1a261573d85b2446ea8d3793f6f22e5e5e7d60d6b3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=10095791&rfr_iscdi=true