Creating optimal code for GPU-accelerated CT reconstruction using ant colony optimization

Purpose: CT reconstruction algorithms implemented on the GPU are highly sensitive to their implementation details and the hardware they run on. Fine-tuning an implementation for optimal performance can be a time consuming task and require many updates when the hardware changes. There are some techni...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Medical physics (Lancaster) Ročník 40; číslo 3; s. 031110 - n/a
Hlavní autoři:	Papenhausen, Eric, Zheng, Ziyi, Mueller, Klaus
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States American Association of Physicists in Medicine 01.03.2013
Témata:	Algorithms ant colony optimization Architectures of general purpose stored programme computers Boundary value problems Computed tomography Computer Graphics Computer hardware Computerised tomographs computerised tomography Computers CT reconstruction Digital computing or data processing equipment or methods, specially adapted for specific applications filtered backprojection GPU Graphical methods graphics processing units image coding Image coding, e.g. from bit‐mapped to non bit‐mapped Image data processing or generation, in general Image Processing, Computer-Assisted - instrumentation Image Processing, Computer-Assisted - methods image reconstruction medical image processing Medical image quality Medical image reconstruction Medical imaging Numerical optimization Optimization Reconstruction separable footprint Solution processes Time Factors Tomography, X-Ray Computed - methods ant colony optimization separable footprint GPU CT reconstruction filtered backprojection
ISSN:	0094-2405, 2473-4209, 2473-4209
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Purpose: CT reconstruction algorithms implemented on the GPU are highly sensitive to their implementation details and the hardware they run on. Fine-tuning an implementation for optimal performance can be a time consuming task and require many updates when the hardware changes. There are some techniques that do automatic fine-tuning of GPU code. These techniques, however, are relatively narrow in their fine-tuning and are often based on heuristics which can be inaccurate. The goal of this paper is to present a framework that will automate the process of code optimization with maximum flexibility and produce a final result that is efficient and readable to the user. Methods: The authors propose a method that is able to tune high level implementation details by using the ant colony optimization algorithm to find the optimal implementation in a relatively short amount of time. Our framework does this by taking as input, a file that describes a graph, such that a path through this graph represents a potential implementation. They then use the ant colony optimization algorithm to find the optimal path through this graph based on the execution time and the quality of the image. Results: Two experimental studies are carried out. Using the presented framework, they optimize the performance of a GPU accelerated FDK backprojection implementation and a GPU accelerated separable footprint backprojection implementation. The authors demonstrate that the resulting optimal implementation can be different depending on the hardware specifications. They then compare the results of the framework produced with the results produced by manual optimization. Conclusions: The framework they present is a useful tool for increasing programmer productivity and reducing the overhead of leveraging hardware specific resources. By performing an intelligent search, our framework produces a more efficient image reconstruction implementation in a shorter amount of time.
AbstractList	CT reconstruction algorithms implemented on the GPU are highly sensitive to their implementation details and the hardware they run on. Fine-tuning an implementation for optimal performance can be a time consuming task and require many updates when the hardware changes. There are some techniques that do automatic fine-tuning of GPU code. These techniques, however, are relatively narrow in their fine-tuning and are often based on heuristics which can be inaccurate. The goal of this paper is to present a framework that will automate the process of code optimization with maximum flexibility and produce a final result that is efficient and readable to the user.PURPOSECT reconstruction algorithms implemented on the GPU are highly sensitive to their implementation details and the hardware they run on. Fine-tuning an implementation for optimal performance can be a time consuming task and require many updates when the hardware changes. There are some techniques that do automatic fine-tuning of GPU code. These techniques, however, are relatively narrow in their fine-tuning and are often based on heuristics which can be inaccurate. The goal of this paper is to present a framework that will automate the process of code optimization with maximum flexibility and produce a final result that is efficient and readable to the user.The authors propose a method that is able to tune high level implementation details by using the ant colony optimization algorithm to find the optimal implementation in a relatively short amount of time. Our framework does this by taking as input, a file that describes a graph, such that a path through this graph represents a potential implementation. They then use the ant colony optimization algorithm to find the optimal path through this graph based on the execution time and the quality of the image.METHODSThe authors propose a method that is able to tune high level implementation details by using the ant colony optimization algorithm to find the optimal implementation in a relatively short amount of time. Our framework does this by taking as input, a file that describes a graph, such that a path through this graph represents a potential implementation. They then use the ant colony optimization algorithm to find the optimal path through this graph based on the execution time and the quality of the image.Two experimental studies are carried out. Using the presented framework, they optimize the performance of a GPU accelerated FDK backprojection implementation and a GPU accelerated separable footprint backprojection implementation. The authors demonstrate that the resulting optimal implementation can be different depending on the hardware specifications. They then compare the results of the framework produced with the results produced by manual optimization.RESULTSTwo experimental studies are carried out. Using the presented framework, they optimize the performance of a GPU accelerated FDK backprojection implementation and a GPU accelerated separable footprint backprojection implementation. The authors demonstrate that the resulting optimal implementation can be different depending on the hardware specifications. They then compare the results of the framework produced with the results produced by manual optimization.The framework they present is a useful tool for increasing programmer productivity and reducing the overhead of leveraging hardware specific resources. By performing an intelligent search, our framework produces a more efficient image reconstruction implementation in a shorter amount of time.CONCLUSIONSThe framework they present is a useful tool for increasing programmer productivity and reducing the overhead of leveraging hardware specific resources. By performing an intelligent search, our framework produces a more efficient image reconstruction implementation in a shorter amount of time. CT reconstruction algorithms implemented on the GPU are highly sensitive to their implementation details and the hardware they run on. Fine-tuning an implementation for optimal performance can be a time consuming task and require many updates when the hardware changes. There are some techniques that do automatic fine-tuning of GPU code. These techniques, however, are relatively narrow in their fine-tuning and are often based on heuristics which can be inaccurate. The goal of this paper is to present a framework that will automate the process of code optimization with maximum flexibility and produce a final result that is efficient and readable to the user. The authors propose a method that is able to tune high level implementation details by using the ant colony optimization algorithm to find the optimal implementation in a relatively short amount of time. Our framework does this by taking as input, a file that describes a graph, such that a path through this graph represents a potential implementation. They then use the ant colony optimization algorithm to find the optimal path through this graph based on the execution time and the quality of the image. Two experimental studies are carried out. Using the presented framework, they optimize the performance of a GPU accelerated FDK backprojection implementation and a GPU accelerated separable footprint backprojection implementation. The authors demonstrate that the resulting optimal implementation can be different depending on the hardware specifications. They then compare the results of the framework produced with the results produced by manual optimization. The framework they present is a useful tool for increasing programmer productivity and reducing the overhead of leveraging hardware specific resources. By performing an intelligent search, our framework produces a more efficient image reconstruction implementation in a shorter amount of time. Purpose: CT reconstruction algorithms implemented on the GPU are highly sensitive to their implementation details and the hardware they run on. Fine-tuning an implementation for optimal performance can be a time consuming task and require many updates when the hardware changes. There are some techniques that do automatic fine-tuning of GPU code. These techniques, however, are relatively narrow in their fine-tuning and are often based on heuristics which can be inaccurate. The goal of this paper is to present a framework that will automate the process of code optimization with maximum flexibility and produce a final result that is efficient and readable to the user. Methods: The authors propose a method that is able to tune high level implementation details by using the ant colony optimization algorithm to find the optimal implementation in a relatively short amount of time. Our framework does this by taking as input, a file that describes a graph, such that a path through this graph represents a potential implementation. They then use the ant colony optimization algorithm to find the optimal path through this graph based on the execution time and the quality of the image. Results: Two experimental studies are carried out. Using the presented framework, they optimize the performance of a GPU accelerated FDK backprojection implementation and a GPU accelerated separable footprint backprojection implementation. The authors demonstrate that the resulting optimal implementation can be different depending on the hardware specifications. They then compare the results of the framework produced with the results produced by manual optimization. Conclusions: The framework they present is a useful tool for increasing programmer productivity and reducing the overhead of leveraging hardware specific resources. By performing an intelligent search, our framework produces a more efficient image reconstruction implementation in a shorter amount of time.
Author	Zheng, Ziyi Papenhausen, Eric Mueller, Klaus
Author_xml	– sequence: 1 givenname: Eric surname: Papenhausen fullname: Papenhausen, Eric email: epapenhausen@cs.sunysb.edu organization: Visual Analytics and Imaging Lab, Center of Visual Computing, Computer Science Department, Stony Brook University, Stony Brook, New York 11794-4400 – sequence: 2 givenname: Ziyi surname: Zheng fullname: Zheng, Ziyi email: zizhen@cs.sunysb.edu organization: Visual Analytics and Imaging Lab, Center of Visual Computing, Computer Science Department, Stony Brook University, Stony Brook, New York 11794-4400 – sequence: 3 givenname: Klaus surname: Mueller fullname: Mueller, Klaus email: mueller@cs.sunysb.edu organization: Visual Analytics and Imaging Lab, Center of Visual Computing, Computer Science Department, Stony Brook University, Stony Brook, New York 11794-4400
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/23464290$$D View this record in MEDLINE/PubMed
BookMark	eNp9kEtPwzAQhC0EghY48AdQjoAU8CsPH1EFBQkEh_bAyXKcNQpK42I7oPLrSUmLEK_TarXfjGZniDYb2wBCBwSfEkLyM3LKs4xhnmygAeUZiznFYhMNMBY8phwnO2jo_RPGOGUJ3kY7lPGUU4EH6GHkQIWqeYzsPFQzVUfalhAZ66Lx_TRWWkMNTgUoo9EkcqBt44NrdahsE7V-KVRN6ES1bRa9R_Wmltc9tGVU7WF_NXfR9PJiMrqKb-7G16Pzm1gzwZKYGW4ymuK8W0xCRBdSQJ4rUeRGqCIRmujCZCUIRkCntCBpWmashIxrRSmwXXTU-86dfW7BBzmrfJe6Vg3Y1kvCSJKyFDPSoYcrtC1mUMq56z52C7muowPOekA7670DI3UVPr4JTlW1JFguC5dErgrvFMffFGvT39i4Z1-rGhZ_g_L2fsWf9Lxfp_jUvFj3hZ-X5j_4Z5J3NCmouA
CODEN	MPHYA6
CitedBy_id	crossref_primary_10_1088_1361_6560_ab416d crossref_primary_10_1016_j_jss_2014_03_083 crossref_primary_10_1016_j_ejmp_2017_07_024 crossref_primary_10_1118_1_4903265 crossref_primary_10_1016_j_cl_2018_05_003 crossref_primary_10_1118_1_4966134
Cites_doi	10.1016/0161‐7346(84)90008‐7 10.1109/TMI.2010.2050898 10.1109/3477.484436 10.1016/j.parco.2011.09.001 10.1109/TMI.1982.4307558 10.1088/0031‐9155/49/11/024 10.1364/JOSAA.1.000612 10.1109/4235.585892 10.1118/1.3180956
ContentType	Journal Article
Copyright	American Association of Physicists in Medicine 2013 American Association of Physicists in Medicine
Copyright_xml	– notice: American Association of Physicists in Medicine – notice: 2013 American Association of Physicists in Medicine
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8
DOI	10.1118/1.4773045
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic MEDLINE
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: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine Physics
EISSN	2473-4209
EndPage	n/a
ExternalDocumentID	23464290 10_1118_1_4773045 MP3045
Genre	article Research Support, U.S. Gov't, Non-P.H.S Journal Article
GrantInformation_xml	– fundername: NSF grantid: IIS-1117132 – fundername: NSF grantid: IIS-1050477 – fundername: NSF grantid: CNS-0959979 – fundername: NSF funderid: IIS‐1050477 – fundername: NSF funderid: IIS‐1117132 – fundername: NSF funderid: CNS‐0959979
GroupedDBID	--- --Z -DZ .GJ 0R~ 1OB 1OC 29M 2WC 33P 36B 3O- 4.4 476 53G 5GY 5RE 5VS AAHHS AANLZ AAQQT AASGY AAXRX AAZKR ABCUV ABEFU ABFTF ABJNI ABLJU ABQWH ABTAH ABXGK ACAHQ ACBEA ACCFJ ACCZN ACGFO ACGFS ACGOF ACPOU ACSMX ACXBN ACXQS ADBBV ADBTR ADKYN ADOZA ADXAS ADZMN AEEZP AEGXH AEIGN AENEX AEQDE AEUYR AFBPY AFFPM AHBTC AIACR AIAGR AIURR AIWBW AJBDE ALMA_UNASSIGNED_HOLDINGS ALUQN AMYDB ASPBG BFHJK C45 CS3 DCZOG DRFUL DRMAN DRSTM DU5 EBD EBS EJD EMB EMOBN F5P G8K HDBZQ HGLYW I-F KBYEO LATKE LEEKS LOXES LUTES LYRES MEWTI O9- OVD P2P P2W PALCI PHY RJQFR RNS ROL SAMSI SUPJJ SV3 TEORI TN5 TWZ USG WOHZO WXSBR XJT ZGI ZVN ZXP ZY4 ZZTAW AAHQN AAIPD AAMNL AAYCA ABDPE AFWVQ AITYG ALVPJ AAMMB AAYXX ABUFD ADMLS AEFGJ AEYWJ AGHNM AGXDD AGYGG AIDQK AIDYY AIQQE CITATION LH4 CGR CUY CVF ECM EIF NPM 7X8
ID	FETCH-LOGICAL-c3935-3f4f72608935f5190009e88a9b8f9ab59c1cbf7de931ec62b166d73de74ca22e3
IEDL.DBID	DRFUL
ISICitedReferencesCount	9
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000338348200011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0094-2405 2473-4209
IngestDate	Fri Sep 05 13:59:25 EDT 2025 Mon Jul 21 05:49:40 EDT 2025 Sat Nov 29 01:32:14 EST 2025 Tue Nov 18 22:07:51 EST 2025 Wed Jan 22 16:20:56 EST 2025 Fri Jun 21 00:28:33 EDT 2024 Sun Jul 14 10:05:21 EDT 2019
IsPeerReviewed	true
IsScholarly	true
Issue	3
Keywords	ant colony optimization separable footprint GPU CT reconstruction filtered backprojection
Language	English
License	0094-2405/2013/40(3)/031110/7/$30.00 http://onlinelibrary.wiley.com/termsAndConditions#vor
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c3935-3f4f72608935f5190009e88a9b8f9ab59c1cbf7de931ec62b166d73de74ca22e3
Notes	epapenhausen@cs.sunysb.edu zizhen@cs.sunysb.edu Electronic mail mueller@cs.sunysb.edu ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
PMID	23464290
PQID	1315636031
PQPubID	23479
PageCount	7
ParticipantIDs	pubmed_primary_23464290 crossref_citationtrail_10_1118_1_4773045 crossref_primary_10_1118_1_4773045 wiley_primary_10_1118_1_4773045_MP3045 scitation_primary_10_1118_1_4773045 proquest_miscellaneous_1315636031
PublicationCentury	2000
PublicationDate	March 2013
PublicationDateYYYYMMDD	2013-03-01
PublicationDate_xml	– month: 03 year: 2013 text: March 2013
PublicationDecade	2010
PublicationPlace	United States
PublicationPlace_xml	– name: United States
PublicationTitle	Medical physics (Lancaster)
PublicationTitleAlternate	Med Phys
PublicationYear	2013
Publisher	American Association of Physicists in Medicine
Publisher_xml	– name: American Association of Physicists in Medicine
References	Long, Fessler, Balter (c4) 2010; 29 Dorigo, Maniezzo, Colorni (c12) 1996; 26 De Man, Basu (c16) 2004; 49 Klockner, Pinto, Lee, Catanzaro, Ivanov, Fasih (c9) 2011; 38 Dorigo, Gambardella (c11) 1997; 1 Andersen, Kak (c1) 1984; 6 Shepp, Vardi (c3) 1982; 1 Feldkamp, Davis, Kress (c2) 1984; 1 Rohkohl, Keck, Hofmann, Hornegger (c13) 2009; 36 2009; 36 1982; 1 1990 2011 2010; 29 2010 2004; 49 1984; 1 1984; 6 2009 2007; 6 1997; 1 2011; 38 1996; 26 Papenhausen E. (e_1_2_9_7_1) 2011 e_1_2_9_10_1 e_1_2_9_13_1 e_1_2_9_12_1 Wu M. (e_1_2_9_15_1) 2011 e_1_2_9_5_1 e_1_2_9_4_1 Zheng Z. (e_1_2_9_9_1) 2010 e_1_2_9_3_1 Westover L. (e_1_2_9_16_1) 1990 e_1_2_9_2_1 Scherl H. (e_1_2_9_8_1) 2007 Rudy G. (e_1_2_9_11_1) 2010 e_1_2_9_14_1 e_1_2_9_17_1 e_1_2_9_18_1 Xu W. (e_1_2_9_6_1) 2009
References_xml	– volume: 1 start-page: 113 year: 1982 ident: c3 article-title: Maximum likelihood reconstruction for emission tomography publication-title: IEEE Trans. Med. Imaging – volume: 1 start-page: 53 year: 1997 ident: c11 article-title: Ant colony system: A cooperative learning approach to the traveling salesman problem publication-title: IEEE Trans. Evol. Comput. – volume: 36 start-page: 3940 year: 2009 ident: c13 article-title: RabbitCT—an open platform for benchmarking 3D cone-beam reconstruction algorithms publication-title: Med. Phys. – volume: 1 start-page: 612 year: 1984 ident: c2 article-title: Practical cone-beam algorithm publication-title: J. Opt. Soc. Am. – volume: 38 start-page: 157 year: 2011 ident: c9 article-title: Pycuda and pyopencl: A scripting-based approach to GPU run-time code generation publication-title: Parallel Comput. – volume: 49 start-page: 2463 year: 2004 ident: c16 article-title: Distance-driven projection and backprojection in three dimensions publication-title: Phys. Med. Biol. – volume: 6 start-page: 81 year: 1984 ident: c1 article-title: Simultaneous algebraic reconstruction technique (SART): A superior implementation of the ART algorithm publication-title: Ultrason. Imaging – volume: 29 start-page: 1839 year: 2010 ident: c4 article-title: 3D forward and backprojection for X-ray CT using separable footprints publication-title: IEEE Trans. Med. Imaging – volume: 26 start-page: 29 year: 1996 ident: c12 article-title: Ant system: Optimization by a colony of cooperating agents publication-title: IEEE Trans. Syst., Man, Cybern., Part B: Cybern. – volume: 49 start-page: 2463 issue: 11 year: 2004 end-page: 2475 article-title: Distance‐driven projection and backprojection in three dimensions publication-title: Phys. Med. Biol. – volume: 38 start-page: 157 issue: 3 year: 2011 end-page: 174 article-title: Pycuda and pyopencl: A scripting‐based approach to GPU run‐time code generation publication-title: Parallel Comput. – year: 2009 article-title: Learning effective parameter settings for iterative ct reconstruction algorithms publication-title: Proceedings of the International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, Beijing, China – volume: 6 start-page: 4464 year: 2007 end-page: 4466 article-title: Fast GPU‐based CT reconstruction using the common unified device architecture (CUDA) – volume: 1 start-page: 113 issue: 2 year: 1982 end-page: 122 article-title: Maximum likelihood reconstruction for emission tomography publication-title: IEEE Trans. Med. Imaging – year: 2010 article-title: Cache‐aware GPU memory scheduling scheme for CT back‐projection – start-page: 367 year: 1990 end-page: 376 article-title: Footprint evaluation for volume rendering – volume: 1 start-page: 612 issue: A6 year: 1984 end-page: 619 article-title: Practical cone‐beam algorithm publication-title: J. Opt. Soc. Am. – volume: 36 start-page: 3940 year: 2009 end-page: 3944 article-title: RabbitCT—an open platform for benchmarking 3D cone‐beam reconstruction algorithms publication-title: Med. Phys. – volume: 6 start-page: 81 year: 1984 end-page: 94 article-title: Simultaneous algebraic reconstruction technique (SART): A superior implementation of the ART algorithm publication-title: Ultrason. Imaging – volume: 29 start-page: 1839 issue: 11 year: 2010 end-page: 1850 article-title: 3D forward and backprojection for X‐ray CT using separable footprints publication-title: IEEE Trans. Med. Imaging – year: 2011 article-title: GPU acceleration of 3D forward and backward projection using separable footprints for x‐ray CT image reconstruction publication-title: Proceedings of the International Meeting on Fully Three‐Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Potsdam, Germany – start-page: 136 year: 2010 end-page: 150 article-title: A programming language interface to describe transformations and code generation – year: 2011 article-title: GPU‐accelerated back‐projecting revisited: Squeezing performance by careful tuning publication-title: Proceedings of the International Meeting on Fully Three‐Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Potsdam, Germany – volume: 1 start-page: 53 issue: 1 year: 1997 end-page: 66 article-title: Ant colony system: A cooperative learning approach to the traveling salesman problem publication-title: IEEE Trans. Evol. Comput. – volume: 26 start-page: 29 issue: 1 year: 1996 end-page: 41 article-title: Ant system: Optimization by a colony of cooperating agents publication-title: IEEE Trans. Syst., Man, Cybern., Part B: Cybern. – year: 2009 ident: e_1_2_9_6_1 article-title: Learning effective parameter settings for iterative ct reconstruction algorithms publication-title: Proceedings of the International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine, Beijing, China – ident: e_1_2_9_2_1 doi: 10.1016/0161‐7346(84)90008‐7 – ident: e_1_2_9_5_1 doi: 10.1109/TMI.2010.2050898 – start-page: 4464 volume-title: Proceedings of the IEEE Medical Imaging Conference, Honolulu, HI year: 2007 ident: e_1_2_9_8_1 – ident: e_1_2_9_13_1 doi: 10.1109/3477.484436 – start-page: 136 volume-title: Proceedings of the 23rd International Conference on Languages and Compilers for Parallel Computing (LCPC’10) year: 2010 ident: e_1_2_9_11_1 – ident: e_1_2_9_10_1 doi: 10.1016/j.parco.2011.09.001 – ident: e_1_2_9_4_1 doi: 10.1109/TMI.1982.4307558 – volume-title: Proceedings of the IEEE Medical Imaging Conference year: 2010 ident: e_1_2_9_9_1 – ident: e_1_2_9_18_1 – year: 2011 ident: e_1_2_9_7_1 article-title: GPU‐accelerated back‐projecting revisited: Squeezing performance by careful tuning publication-title: Proceedings of the International Meeting on Fully Three‐Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Potsdam, Germany – ident: e_1_2_9_17_1 doi: 10.1088/0031‐9155/49/11/024 – start-page: 367 volume-title: Proceedings of the International Meeting on International Conference on Computer Graphics Interactive Techniques year: 1990 ident: e_1_2_9_16_1 – ident: e_1_2_9_3_1 doi: 10.1364/JOSAA.1.000612 – ident: e_1_2_9_12_1 doi: 10.1109/4235.585892 – year: 2011 ident: e_1_2_9_15_1 article-title: GPU acceleration of 3D forward and backward projection using separable footprints for x‐ray CT image reconstruction publication-title: Proceedings of the International Meeting on Fully Three‐Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Potsdam, Germany – ident: e_1_2_9_14_1 doi: 10.1118/1.3180956
SSID	ssj0006350
Score	2.106025
Snippet	Purpose: CT reconstruction algorithms implemented on the GPU are highly sensitive to their implementation details and the hardware they run on. Fine-tuning an... Purpose: CT reconstruction algorithms implemented on the GPU are highly sensitive to their implementation details and the hardware they run on. Fine‐tuning an... CT reconstruction algorithms implemented on the GPU are highly sensitive to their implementation details and the hardware they run on. Fine-tuning an...
SourceID	proquest pubmed crossref wiley scitation
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	031110
SubjectTerms	Algorithms ant colony optimization Architectures of general purpose stored programme computers Boundary value problems Computed tomography Computer Graphics Computer hardware Computerised tomographs computerised tomography Computers CT reconstruction Digital computing or data processing equipment or methods, specially adapted for specific applications filtered backprojection GPU Graphical methods graphics processing units image coding Image coding, e.g. from bit‐mapped to non bit‐mapped Image data processing or generation, in general Image Processing, Computer-Assisted - instrumentation Image Processing, Computer-Assisted - methods image reconstruction medical image processing Medical image quality Medical image reconstruction Medical imaging Numerical optimization Optimization Reconstruction separable footprint Solution processes Time Factors Tomography, X-Ray Computed - methods
Title	Creating optimal code for GPU-accelerated CT reconstruction using ant colony optimization
URI	http://dx.doi.org/10.1118/1.4773045 https://onlinelibrary.wiley.com/doi/abs/10.1118%2F1.4773045 https://www.ncbi.nlm.nih.gov/pubmed/23464290 https://www.proquest.com/docview/1315636031
Volume	40
WOSCitedRecordID	wos000338348200011&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: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 2473-4209 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006350 issn: 0094-2405 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB61W54HHqXQ5VGZh6peQpPYjmNxQgsLB1qtUFdaiUPkZ4XUZqsuVOLGT-A39pcwk2SDKgpC4pTL2Ek8Hs83nhfAi2CkDK60SZC-SATF1hjOXSK9DKlQqTcmbZpNqP39cjbTkxV4tcyFaetD9BduJBnNeU0CbmzXhSSjwPXspVCK_HyrsJbjvhUDWHvzcTz90B_EqEvbDBQtyIkgu8JCOHy3H3xRHf2GMW_CddRErVP8Inxt9M_49n99-R241cFO9rrdJ3dhJdTrcG2vc6yvw9UmEtQt7sGnUQMj60M2x9PkGAdR1jtDbMveTabn338Y51BVUYUJz0YHrLGo-yq0jOLoDxmyi1E57PpbO0uX7LkB0_Hbg9H7pOvAkDhK2U14FFGhxYOgRkbEeoTIQlkabcuojZXaZc5G5YPmWXBFbrOi8Ir7oIQzeR74fRjU8zpsAotoDDudeSlcIVxaWK1Sq6KNpUXIl8Yh7CwZUS1XnLpkHFWtmVJWWdUt2xCe9aQnbU2Oy4ieLrlZocSQG8TUYf51UWUcbVZO3bWH8KBlcz9NzgUaZDodwvOe7397xyVUZ_PTXxTVicc_2252w5_nqfYm9Hj4r4SP4EbedOWgULjHMEAehydwxZ19-bw43YJVNSu3OmH4CT0gB74
linkProvider	Wiley-Blackwell
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB6VLVA48GihLE_zEOolNIntOJa4oIWliN3VCu1KlThYflZIJVt1SyVu_AR-I7-EcZINqigIiVMuYyfxeDzfeF4Az7zm3NvSJJ67ImExtkZTahPuuE-ZSJ3Wad1sQkwm5f6-nK7By1UuTFMfortwi5JRn9dRwOOFdCvlMXI9e8GEiI6-C7DOcBvxHqy__jCcj7qTGJVpk4IiWfQi8LayEA7f7Qaf1Ue_gcyrsIGqqPGKn8WvtQIaXv-_T78B11rgSV41O-UmrPlqEy6PW9f6JlyqY0Htcgs-DmogWR2QBZ4nn3FQzHsniG7J2-n8x7fv2lpUVrHGhCODGalt6q4OLYmR9AcEGUZiQezqazNLm-55C-bDN7PBXtL2YEhsTNpNaGBBoM2DsIYHRHsRk_my1NKUQWrDpc2sCcJ5STNvi9xkReEEdV4wq_Pc09vQqxaVvwMkoDlsZeY4swWzaWGkSI0IJpQGQV8a-rCz4oRaLXnsk3GoGkOlVJlql60PTzrSo6Yqx3lEj1fsVCgz0RGiK7_4slQZRauVxv7afdhu-NxNk1OGJplM-_C0Y_zf3nEO1eni-BeFOnL4Z8_r7fDnedR4Gh93_5XwEWzszcYjNXo3eX8PruR1j44YGHcfeshv_wAu2tOTT8vjh61M_ATUwwrG
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB6VFgo9FGh5pLRgHkK9BJLYjmOJC9qyBdGuVqgrVeJg-VkhQbbq0kq98RP4jfwSxkk2qKIgJE65jCeRx-P5JvMCeOY1595WJvXclSmLuTWaUptyx33GROa0zpphE2I0qg4P5XgBXs1rYdr-EP0Pt6gZzX0dFdwfu9Bpecxcz18wIWKg7wosMS5LVMulnQ_DyV5_E6MxbUtQJItRBN51FsLlL_vFF-3RbyBzBa6jKWqj4hfxa2OAhjf_79NvwWoHPMnr9qTchgVfr8HyfhdaX4NrTS6ona3Dx0EDJOsjMsX75AsuinXvBNEt2R1Pfnz7rq1FYxV7TDgyOCCNT933oSUxk_6IoMBIbIhdn7dcunLPOzAZvjkYvE27GQypjUW7KQ0sCPR5ENbwgGgvYjJfVVqaKkhtuLS5NUE4L2nubVmYvCydoM4LZnVReHoXFutp7e8DCegOW5k7zmzJbFYaKTIjggmVQdCXhQS255JQ8y2PczI-q9ZRqVSuum1L4ElPetx25biM6PFcnAp1JgZCdO2npzOVU_RaaZyvncC9Vs49m4IydMlklsDTXvB_e8clVGfTk18UCiWfwPPmOPyZj9ofx8fGvxI-guXxzlDtvRu9fwA3imZER8yL24RFFLffgqv27Oun2cnDTiV-Ak9DCkE
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=Creating+optimal+code+for+GPU-accelerated+CT+reconstruction+using+ant+colony+optimization&rft.jtitle=Medical+physics+%28Lancaster%29&rft.au=Papenhausen%2C+Eric&rft.au=Zheng%2C+Ziyi&rft.au=Mueller%2C+Klaus&rft.date=2013-03-01&rft.issn=0094-2405&rft.eissn=2473-4209&rft.volume=40&rft.issue=3&rft_id=info:doi/10.1118%2F1.4773045
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0094-2405&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0094-2405&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0094-2405&client=summon