SPULTRA: Low-Dose CT Image Reconstruction With Joint Statistical and Learned Image Models

Low-dose CT image reconstruction has been a popular research topic in recent years. A typical reconstruction method based on post-log measurements is called penalized weighted-least squares (PWLS). Due to the underlying limitations of the post-log statistical model, the PWLS reconstruction quality i...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on medical imaging Jg. 39; H. 3; S. 729 - 741
Hauptverfasser:	Ye, Siqi, Ravishankar, Saiprasad, Long, Yong, Fessler, Jeffrey A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Algorithms Clustering Computed tomography Computer applications Data models Dictionaries efficient algorithms Humans Image coding Image processing Image Processing, Computer-Assisted - methods Image quality Image reconstruction Inverse problems Iterative methods Likelihood Functions Machine Learning Mathematical models nonconvex optimization Poisson Distribution Radiation Dosage shifted-Poisson model sparse representation Statistical analysis Statistical models Tomography, X-Ray Computed - methods transform learning Transforms Two dimensional displays X-ray imaging
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Low-dose CT image reconstruction has been a popular research topic in recent years. A typical reconstruction method based on post-log measurements is called penalized weighted-least squares (PWLS). Due to the underlying limitations of the post-log statistical model, the PWLS reconstruction quality is often degraded in low-dose scans. This paper investigates a shifted-Poisson (SP) model based likelihood function that uses the pre-log raw measurements that better represents the measurement statistics, together with a data-driven regularizer exploiting a Union of Learned TRAnsforms (SPULTRA). Both the SP induced data-fidelity term and the regularizer in the proposed framework are nonconvex. The proposed SPULTRA algorithm uses quadratic surrogate functions for the SP induced data-fidelity term. Each iteration involves a quadratic subproblem for updating the image, and a sparse coding and clustering subproblem that has a closed-form solution. The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.
AbstractList	Low-dose CT image reconstruction has been a popular research topic in recent years. A typical reconstruction method based on post-log measurements is called penalized weighted-least squares (PWLS). Due to the underlying limitations of the post-log statistical model, the PWLS reconstruction quality is often degraded in low-dose scans. This paper investigates a shifted-Poisson (SP) model based likelihood function that uses the pre-log raw measurements that better represents the measurement statistics, together with a data-driven regularizer exploiting a Union of Learned TRAnsforms (SPULTRA). Both the SP induced data-fidelity term and the regularizer in the proposed framework are nonconvex. The proposed SPULTRA algorithm uses quadratic surrogate functions for the SP induced data-fidelity term. Each iteration involves a quadratic subproblem for updating the image, and a sparse coding and clustering subproblem that has a closed-form solution. The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans. Low-dose CT image reconstruction has been a popular research topic in recent years. A typical reconstruction method based on post-log measurements is called penalized weighted-least squares (PWLS). Due to the underlying limitations of the post-log statistical model, the PWLS reconstruction quality is often degraded in low-dose scans. This paper investigates a shifted-Poisson (SP) model based likelihood function that uses the pre-log raw measurements that better represents the measurement statistics, together with a data-driven regularizer exploiting a Union of Learned TRAnsforms (SPULTRA). Both the SP induced data-fidelity term and the regularizer in the proposed framework are nonconvex. The proposed SPULTRA algorithm uses quadratic surrogate functions for the SP induced data-fidelity term. Each iteration involves a quadratic subproblem for updating the image, and a sparse coding and clustering subproblem that has a closed-form solution. The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.Low-dose CT image reconstruction has been a popular research topic in recent years. A typical reconstruction method based on post-log measurements is called penalized weighted-least squares (PWLS). Due to the underlying limitations of the post-log statistical model, the PWLS reconstruction quality is often degraded in low-dose scans. This paper investigates a shifted-Poisson (SP) model based likelihood function that uses the pre-log raw measurements that better represents the measurement statistics, together with a data-driven regularizer exploiting a Union of Learned TRAnsforms (SPULTRA). Both the SP induced data-fidelity term and the regularizer in the proposed framework are nonconvex. The proposed SPULTRA algorithm uses quadratic surrogate functions for the SP induced data-fidelity term. Each iteration involves a quadratic subproblem for updating the image, and a sparse coding and clustering subproblem that has a closed-form solution. The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.
Author	Long, Yong Fessler, Jeffrey A. Ye, Siqi Ravishankar, Saiprasad
Author_xml	– sequence: 1 givenname: Siqi orcidid: 0000-0002-2295-3123 surname: Ye fullname: Ye, Siqi email: yesiqi@sjtu.edu.cn organization: University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China – sequence: 2 givenname: Saiprasad orcidid: 0000-0002-5792-5827 surname: Ravishankar fullname: Ravishankar, Saiprasad email: ravisha3@msu.edu organization: Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA – sequence: 3 givenname: Yong orcidid: 0000-0002-2055-0662 surname: Long fullname: Long, Yong email: yong.long@sjtu.edu.cn organization: University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, China – sequence: 4 givenname: Jeffrey A. orcidid: 0000-0001-9998-3315 surname: Fessler fullname: Fessler, Jeffrey A. email: fessler@umich.edu organization: Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31425021$$D View this record in MEDLINE/PubMed
BookMark	eNp9kc1vEzEQxS1URNPCHQkJrcSFywZ_7drmgFSFr6BUoDYVcLK89rh1tbHL2gviv2ejpBX0wGkO83tPb-YdoYOYIiD0lOA5IVi9Wp8u5xQTNaeKccXYAzQjTSNr2vBvB2iGqZA1xi09REc5X2NMeIPVI3TICKcNpmSGvp9_uVitz05eV6v0q36bMlSLdbXcmEuozsCmmMsw2hJSrL6GclV9SiGW6ryYEnIJ1vSVia5agRkiuL3uNDno82P00Js-w5P9PEYX79-tFx_r1ecPy8XJqrYc01JbQyVI0kjHmZG4c0xI61TnQFLXcc-9kda3vhNAvRKYOQcdp55jj7lXhB2jNzvfm7HbgLMQy2B6fTOEjRl-62SC_ncTw5W-TD-1IAITwSaDl3uDIf0YIRe9CdlC35sIacyaMtYqSWlLJ_TFPfQ6jUOczpsowVspmkZN1PO_E91Fuf36BLQ7wA4p5wG8tmH70bQNGHpNsN7Wq6d69bZeva93EuJ7wlvv_0ie7SQBAO5wKRSXE_IHdaSvgQ
CODEN	ITMID4
CitedBy_id	crossref_primary_10_1109_TMI_2021_3074783 crossref_primary_10_1016_j_sigpro_2020_107871 crossref_primary_10_1109_ACCESS_2021_3079323 crossref_primary_10_1109_TCI_2024_3430469 crossref_primary_10_1088_1361_6560_ab8fc1 crossref_primary_10_1088_1361_6560_ac556e crossref_primary_10_1109_TMI_2021_3095310 crossref_primary_10_1002_mp_15013 crossref_primary_10_1137_21M1445697 crossref_primary_10_1002_mp_16645 crossref_primary_10_1002_mp_17406 crossref_primary_10_1002_mp_15414 crossref_primary_10_1088_1361_6560_acd238 crossref_primary_10_1002_mp_14449 crossref_primary_10_1002_mp_16307 crossref_primary_10_1109_TMI_2021_3139533 crossref_primary_10_1007_s10915_024_02638_7 crossref_primary_10_1016_j_radphyschem_2025_113140 crossref_primary_10_1515_bmt_2023_0581 crossref_primary_10_1109_MSP_2019_2951469
Cites_doi	10.1118/1.1915015 10.1118/1.2836423 10.1109/TMI.2017.2779406 10.1109/TMI.2010.2050898 10.1117/12.480302 10.1118/1.2955743 10.1117/3.831079.ch1 10.1118/1.598410 10.1117/12.465601 10.1109/TMI.2018.2805692 10.1137/17M112124X 10.1109/83.465108 10.1109/TMI.2018.2865202 10.1088/0031-9155/60/19/7437 10.1109/TMI.2015.2508780 10.1016/j.ejmp.2012.01.003 10.1109/TSP.2012.2226449 10.1088/0031-9155/57/2/309 10.1109/TMI.2014.2365179 10.1007/978-3-642-02431-3 10.1109/TMI.2012.2195669 10.1109/TSP.2015.2405503 10.1118/1.598392 10.1088/0031-9155/58/12/R63 10.1109/TMI.2016.2606338 10.1109/TMI.2018.2799231 10.1117/12.2223786 10.1118/1.3560878 10.1109/TMI.2011.2172951 10.1109/TCSVT.2016.2643009 10.1109/TMI.2017.2753138 10.1109/42.802758 10.1117/12.660281 10.1109/TMI.2006.875429 10.1137/141002293 10.1002/mp.12097 10.1109/TIP.2009.2017139 10.1118/1.3638125 10.1109/TMI.2018.2832007 10.1109/TMI.2016.2627004 10.1007/s11263-014-0761-1 10.1109/TCI.2016.2567299 10.1109/TMI.2006.882141 10.1097/RCT.0b013e318258e891 10.1118/1.4722751 10.1109/TMI.2018.2823756 10.1109/TMI.2016.2600249 10.1118/1.2789499 10.1109/TNS.2008.2004557 10.1109/TMI.2014.2350962 10.1109/42.993128 10.1364/JOSAA.1.000612
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
DBID	97E RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8 5PM
DOI	10.1109/TMI.2019.2934933
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Nursing & Allied Health Premium Biotechnology and BioEngineering Abstracts MEDLINE - Academic PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Civil Engineering Abstracts Aluminium Industry Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Ceramic Abstracts Materials Business File METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Aerospace Database Nursing & Allied Health Premium Engineered Materials Abstracts Biotechnology Research Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Corrosion Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic
DatabaseTitleList	MEDLINE MEDLINE - Academic Materials Research Database
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine Engineering
EISSN	1558-254X
EndPage	741
ExternalDocumentID	PMC7170173 31425021 10_1109_TMI_2019_2934933 8794829
Genre	orig-research Research Support, U.S. Gov't, Non-P.H.S Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural
GrantInformation_xml	– fundername: DARPA Young Faculty grantid: D14AP00086 – fundername: Office of Naval Research grantid: N00014-15-1-2141 funderid: 10.13039/100000006 – fundername: National Natural Science Foundation of China grantid: 61501292 funderid: 10.13039/501100001809 – fundername: National Institutes of Health grantid: U01 EB018753 funderid: 10.13039/100000002 – fundername: Army Research Office grantid: W911NF-11-1-0391; 2015-05174-05 funderid: 10.13039/100000183 – fundername: NIBIB NIH HHS grantid: U01 EB018753
GroupedDBID	--- -DZ -~X .GJ 0R~ 29I 4.4 53G 5GY 5RE 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT ACPRK AENEX AETIX AFRAH AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IBMZZ ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 MS~ O9- OCL P2P PQQKQ RIA RIE RNS RXW TAE TN5 VH1 AAYXX CITATION AAYOK CGR CUY CVF ECM EIF NPM RIG 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D NAPCQ P64 7X8 5PM
ID	FETCH-LOGICAL-c402t-ca28e8158d43a80bd378cd9bde82db4f4fa8cf6fb7e2f9703ddeb42f40f04f913
IEDL.DBID	RIE
ISICitedReferencesCount	27
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000525262100017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0278-0062 1558-254X
IngestDate	Tue Sep 30 15:37:38 EDT 2025 Sat Sep 27 16:51:59 EDT 2025 Mon Jun 30 04:25:20 EDT 2025 Thu Apr 03 07:08:03 EDT 2025 Tue Nov 18 22:32:51 EST 2025 Sat Nov 29 05:14:07 EST 2025 Wed Aug 27 02:30:47 EDT 2025
IsPeerReviewed	false
IsScholarly	true
Issue	3
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c402t-ca28e8158d43a80bd378cd9bde82db4f4fa8cf6fb7e2f9703ddeb42f40f04f913
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-2295-3123 0000-0001-9998-3315 0000-0002-5792-5827 0000-0002-2055-0662
PMID	31425021
PQID	2374687559
PQPubID	85460
PageCount	13
ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_7170173 pubmed_primary_31425021 proquest_journals_2374687559 proquest_miscellaneous_2336982262 crossref_citationtrail_10_1109_TMI_2019_2934933 crossref_primary_10_1109_TMI_2019_2934933 ieee_primary_8794829
PublicationCentury	2000
PublicationDate	2020-03-01
PublicationDateYYYYMMDD	2020-03-01
PublicationDate_xml	– month: 03 year: 2020 text: 2020-03-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: New York
PublicationTitle	IEEE transactions on medical imaging
PublicationTitleAbbrev	TMI
PublicationTitleAlternate	IEEE Trans Med Imaging
PublicationYear	2020
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref57 ref13 ref56 ding (ref11) 2016 ref12 ref59 ref15 ref58 ref14 ref53 ref52 ref55 ref10 mordukhovich (ref54) 2006 ref17 ref16 ref19 ref18 ref51 ref50 xu (ref25) 2009; 18 ref46 ref47 ref42 ref41 chun (ref48) 2017 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ye (ref39) 2017 ref34 ref37 ref36 ref31 ref30 ref2 ref1 ref38 hayes (ref22) 2019; 10948 xu (ref32) 2012; 31 ref24 ref23 ref26 ref20 ref21 luo (ref33) 2016; 9847 ref28 ref27 garey (ref35) 2002; 29 ref29 han (ref45) 2016
References_xml	– ident: ref23 doi: 10.1118/1.1915015 – ident: ref27 doi: 10.1118/1.2836423 – ident: ref26 doi: 10.1109/TMI.2017.2779406 – year: 2006 ident: ref54 publication-title: Variational Analysis and Generalized Differentiation I Basic Theory II Applications – ident: ref58 doi: 10.1109/TMI.2010.2050898 – ident: ref8 doi: 10.1117/12.480302 – ident: ref56 doi: 10.1118/1.2955743 – ident: ref5 doi: 10.1117/3.831079.ch1 – ident: ref6 doi: 10.1118/1.598410 – ident: ref7 doi: 10.1117/12.465601 – ident: ref43 doi: 10.1109/TMI.2018.2805692 – ident: ref52 doi: 10.1137/17M112124X – ident: ref21 doi: 10.1109/83.465108 – ident: ref44 doi: 10.1109/TMI.2018.2865202 – ident: ref3 doi: 10.1088/0031-9155/60/19/7437 – ident: ref50 doi: 10.1109/TMI.2015.2508780 – ident: ref14 doi: 10.1016/j.ejmp.2012.01.003 – start-page: 1 year: 2017 ident: ref39 article-title: Adaptive sparse modeling and shifted-Poisson likelihood based approach for low-dose CT image reconstruction publication-title: Proc IEEE Workshop Mach Learn Signal Process – ident: ref36 doi: 10.1109/TSP.2012.2226449 – ident: ref2 doi: 10.1088/0031-9155/57/2/309 – ident: ref49 doi: 10.1109/TMI.2014.2365179 – ident: ref53 doi: 10.1007/978-3-642-02431-3 – volume: 31 start-page: 1682 year: 2012 ident: ref32 article-title: Low-dose X-ray CT reconstruction via dictionary learning publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2012.2195669 – ident: ref37 doi: 10.1109/TSP.2015.2405503 – ident: ref1 doi: 10.1118/1.598392 – ident: ref12 doi: 10.1088/0031-9155/58/12/R63 – ident: ref20 doi: 10.1109/TMI.2016.2606338 – ident: ref41 doi: 10.1109/TMI.2018.2799231 – year: 2016 ident: ref45 article-title: Deep residual learning for compressed sensing CT reconstruction via persistent homology analysis publication-title: arXiv 1611 06391 – volume: 9847 start-page: 98470l year: 2016 ident: ref33 article-title: 2.5D dictionary learning based computed tomography reconstruction publication-title: Proc SPIE doi: 10.1117/12.2223786 – volume: 10948 year: 2019 ident: ref22 article-title: Unbiased statistical image reconstruction in low-dose CT publication-title: Proc SPIE – ident: ref28 doi: 10.1118/1.3560878 – ident: ref29 doi: 10.1109/TMI.2011.2172951 – ident: ref34 doi: 10.1109/TCSVT.2016.2643009 – ident: ref42 doi: 10.1109/TMI.2017.2753138 – ident: ref51 doi: 10.1109/42.802758 – ident: ref19 doi: 10.1117/12.660281 – ident: ref24 doi: 10.1109/TMI.2006.875429 – ident: ref55 doi: 10.1137/141002293 – ident: ref31 doi: 10.1002/mp.12097 – volume: 18 start-page: 1228 year: 2009 ident: ref25 article-title: Electronic noise modeling in statistical iterative reconstruction publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2009.2017139 – start-page: 399 year: 2016 ident: ref11 article-title: Modeling mixed Poisson-Gaussian noise in statistical image reconstruction for X-ray CT publication-title: Proc 2nd Int Meeting Image Formation X-ray CT – ident: ref30 doi: 10.1118/1.3638125 – start-page: 115 year: 2017 ident: ref48 article-title: Sparse-view X-ray CT reconstruction using $\ell_{1}$ regularization with learned sparsifying transform publication-title: Proc of International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine – ident: ref38 doi: 10.1109/TMI.2018.2832007 – ident: ref13 doi: 10.1109/TMI.2016.2627004 – ident: ref40 doi: 10.1007/s11263-014-0761-1 – ident: ref47 doi: 10.1109/TCI.2016.2567299 – ident: ref18 doi: 10.1109/TMI.2006.882141 – ident: ref9 doi: 10.1097/RCT.0b013e318258e891 – volume: 29 year: 2002 ident: ref35 publication-title: Computers and Intractability – ident: ref10 doi: 10.1118/1.4722751 – ident: ref46 doi: 10.1109/TMI.2018.2823756 – ident: ref57 doi: 10.1109/TMI.2016.2600249 – ident: ref15 doi: 10.1118/1.2789499 – ident: ref4 doi: 10.1109/TNS.2008.2004557 – ident: ref16 doi: 10.1109/TMI.2014.2350962 – ident: ref17 doi: 10.1109/42.993128 – ident: ref59 doi: 10.1364/JOSAA.1.000612
SSID	ssj0014509
Score	2.457823
Snippet	Low-dose CT image reconstruction has been a popular research topic in recent years. A typical reconstruction method based on post-log measurements is called...
SourceID	pubmedcentral proquest pubmed crossref ieee
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	729
SubjectTerms	Accuracy Algorithms Clustering Computed tomography Computer applications Data models Dictionaries efficient algorithms Humans Image coding Image processing Image Processing, Computer-Assisted - methods Image quality Image reconstruction Inverse problems Iterative methods Likelihood Functions Machine Learning Mathematical models nonconvex optimization Poisson Distribution Radiation Dosage shifted-Poisson model sparse representation Statistical analysis Statistical models Tomography, X-Ray Computed - methods transform learning Transforms Two dimensional displays X-ray imaging
Title	SPULTRA: Low-Dose CT Image Reconstruction With Joint Statistical and Learned Image Models
URI	https://ieeexplore.ieee.org/document/8794829 https://www.ncbi.nlm.nih.gov/pubmed/31425021 https://www.proquest.com/docview/2374687559 https://www.proquest.com/docview/2336982262 https://pubmed.ncbi.nlm.nih.gov/PMC7170173
Volume	39
WOSCitedRecordID	wos000525262100017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-254X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014509 issn: 0278-0062 databaseCode: RIE dateStart: 19820101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3da9RAEB_aIqIPVls_YmtZwRfB9JLdZD_6VqrFyrUUveL5FPaTHtREvKv--84mubQnRfAtsLNJyG92diYz-xuAN8FxZmSp04JTgwFKMKmxNE-lVqgt2grRdS0Zi7MzOZ2q8zV4N5yF8d63xWd-P162uXzX2Ov4q2wkUXkkVeuwLgTvzmoNGYOi7Mo5aGSMzThdpiQzNZqcnsQaLrWPW1uhGFvZgtqeKne5l39XSd7ado43_--FH8Oj3r0kh50-PIE1X2_Bw1ukg1tw_7RPp2_Dty_nF-PJ58MDMm5-p--buSdHE3LyHW0MiXHpDbss-TpbXJJPzaxekOiftvTO-CBdO9JStHrXz4vN1a7mT-Hi-MPk6GPa91pILUaQi9RqKr3MS-kKpmVmHBPSOmWcl9SZIhRBSxt4MMLToNBMoFk0BQ2IblYElbNnsFE3tX8BBE2ockowpkwMPrnWNLOxnYfLA_MlS2C0_PyV7YnIYz-Mq6oNSDJVIWBVBKzqAUvg7TDjR0fC8Q_Z7YjDINdDkMDuEuGqX6XzijJRcAzYShx-PQzj-opJE1375jrKMB45DjlN4HmnEMO9WY4WD52kBMSKqgwCkbt7daSeXbYc3iLy4Av28u633YEHNMb1ba3bLmwg2P4V3LO_EN6fe6j-U7nXqv8f0e4A-Q
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VgigceLQFAgWMxAWJdBPbiW1uVaHqQnZVQaqWU-SnulJJqu4W_j52kg1dVCFxi-RxEuUbj2cy428A3jqTE8UzGdMcKx-gOBUrjdOYS-G1RWrGuq4lBZtO-empOFqD98NZGGttW3xmd8Nlm8s3jb4Kv8pG3CsPx-IW3M4oxUl3WmvIGdCsK-jAgTM2yfEyKZmIUTkZhyouses3NyoIWdmE2q4qNzmYf9dJXtt4Dh7-3ys_gge9g4n2Oo14DGu23oT712gHN-HupE-ob8H3b0fHRfl17wMqml_xx2Zu0X6Jxj-8lUEhMv3DL4tOZosz9LmZ1QsUPNSW4Nk_SNYGtSSt1vTzQnu18_k2HB98KvcP477bQqx9DLmItcTc8jTjhhLJE2UI49oIZSzHRlFHneTa5U4xi53whsIbRkWx8_gm1ImUPIH1uqntM0DeiAojGCFChfAzlxInOjT0MKkjNiMRjJafv9I9FXnoiHFetSFJIioPWBUAq3rAIng3zLjoaDj-IbsVcBjkeggi2FkiXPXrdF5hwmjuQ7bMD78Zhv0KC2kTWdvmKsiQPLAc5jiCp51CDPcmqbd53k2KgK2oyiAQ2LtXR-rZWcvizQITPiPPb37b17BxWE6KqhhPv7yAezhE-W3l2w6se-DtS7ijf3qoL1-1i-A3P8oDWA
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=SPULTRA%3A+Low-Dose+CT+Image+Reconstruction+With+Joint+Statistical+and+Learned+Image+Models&rft.jtitle=IEEE+transactions+on+medical+imaging&rft.au=Ye%2C+Siqi&rft.au=Ravishankar%2C+Saiprasad&rft.au=Long%2C+Yong&rft.au=Fessler%2C+Jeffrey+A&rft.date=2020-03-01&rft.issn=1558-254X&rft.eissn=1558-254X&rft.volume=39&rft.issue=3&rft.spage=729&rft_id=info:doi/10.1109%2FTMI.2019.2934933&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0278-0062&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0278-0062&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0278-0062&client=summon