Automated inversion time selection for black-blood late gadolinium enhancement cardiac imaging in clinical practice
Objective To simplify black-blood late gadolinium enhancement (BL-LGE) cardiac imaging in clinical practice using an image-based algorithm for automated inversion time (TI) selection. Materials and methods The algorithm selects from BL-LGE TI scout images, the TI corresponding to the image with the...
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| Veröffentlicht in: | Magma (New York, N.Y.) N.Y.), 2023-12, Vol.36 (6), p.877-885 |
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| creator | Maillot, Aurélien Sridi, Soumaya Pineau, Xavier André-Billeau, Amandine Hosteins, Stéphanie Maes, Jean-David Montier, Géraldine Nuñez-Garcia, Marta Quesson, Bruno Sermesant, Maxime Cochet, Hubert Stuber, Matthias Bustin, Aurélien |
| description | Objective
To simplify black-blood late gadolinium enhancement (BL-LGE) cardiac imaging in clinical practice using an image-based algorithm for automated inversion time (TI) selection.
Materials and methods
The algorithm selects from BL-LGE TI scout images, the TI corresponding to the image with the highest number of sub-threshold pixels within a region of interest (ROI) encompassing the blood-pool and myocardium. The threshold value corresponds to the most recurrent pixel intensity of all scout images within the ROI. ROI dimensions were optimized in 40 patients’ scans. The algorithm was validated retrospectively (80 patients) versus two experts and tested prospectively (5 patients) on a 1.5 T clinical scanner.
Results
Automated TI selection took ~ 40 ms per dataset (manual: ~ 17 s). Fleiss’ kappa coefficient for automated-manual, intra-observer and inter-observer agreements were
κ
¯
= 0.73,
κ
¯
= 0.70 and
κ
¯
= 0.63, respectively. The agreement between the algorithm and any expert was better than the agreement between the two experts or between two selections of one expert.
Discussion
Thanks to its good performance and simplicity of implementation, the proposed algorithm is a good candidate for automated BL-LGE imaging in clinical practice. |
| doi_str_mv | 10.1007/s10334-023-01101-2 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2824685795</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2824685795</sourcerecordid><originalsourceid>FETCH-LOGICAL-c391t-9f0b6056b45afa097527b898950f5c105425f5a173f6de5d177cdf70cd29aa7a3</originalsourceid><addsrcrecordid>eNp9kE1vFSEUQInR2Fr9A10Ylm5GLzAMM8umaW2TJt3omtzh40nLwBNmTPz38nzVdNUVEM49yT2EnDP4zADUl8pAiL4DLjpgDFjHX5FTJiTvxmFgr5_dT8i7Wh8AOJMg3pITofjU91ycknqxrXnB1Vka0i9XasiJrmFxtLrozHp4-lzoHNE8dnPM2dLYcLpDm2NIYVuoSz8wGbe4tFKDxQY0NCy4C2nXpNQcMIOR7gs2oXHvyRuPsboPT-cZ-X599e3ypru7_3p7eXHXGTGxtZs8zAPIYe4leoRJSa7mcRonCV4aBrLn0ktkSvjBOmmZUsZ6BcbyCVGhOCOfjt59yT83V1e9hGpcjJhc3qrmI--HUapJNpQfUVNyrcV5vS9thfJbM9CH2PoYW7fY-m9szdvQxyf_Ni_O_h_5V7cB4gjU9pV2ruiHvJXUdn5J-wf8PIur</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2824685795</pqid></control><display><type>article</type><title>Automated inversion time selection for black-blood late gadolinium enhancement cardiac imaging in clinical practice</title><source>SpringerLink Journals</source><creator>Maillot, Aurélien ; Sridi, Soumaya ; Pineau, Xavier ; André-Billeau, Amandine ; Hosteins, Stéphanie ; Maes, Jean-David ; Montier, Géraldine ; Nuñez-Garcia, Marta ; Quesson, Bruno ; Sermesant, Maxime ; Cochet, Hubert ; Stuber, Matthias ; Bustin, Aurélien</creator><creatorcontrib>Maillot, Aurélien ; Sridi, Soumaya ; Pineau, Xavier ; André-Billeau, Amandine ; Hosteins, Stéphanie ; Maes, Jean-David ; Montier, Géraldine ; Nuñez-Garcia, Marta ; Quesson, Bruno ; Sermesant, Maxime ; Cochet, Hubert ; Stuber, Matthias ; Bustin, Aurélien</creatorcontrib><description>Objective
To simplify black-blood late gadolinium enhancement (BL-LGE) cardiac imaging in clinical practice using an image-based algorithm for automated inversion time (TI) selection.
Materials and methods
The algorithm selects from BL-LGE TI scout images, the TI corresponding to the image with the highest number of sub-threshold pixels within a region of interest (ROI) encompassing the blood-pool and myocardium. The threshold value corresponds to the most recurrent pixel intensity of all scout images within the ROI. ROI dimensions were optimized in 40 patients’ scans. The algorithm was validated retrospectively (80 patients) versus two experts and tested prospectively (5 patients) on a 1.5 T clinical scanner.
Results
Automated TI selection took ~ 40 ms per dataset (manual: ~ 17 s). Fleiss’ kappa coefficient for automated-manual, intra-observer and inter-observer agreements were
κ
¯
= 0.73,
κ
¯
= 0.70 and
κ
¯
= 0.63, respectively. The agreement between the algorithm and any expert was better than the agreement between the two experts or between two selections of one expert.
Discussion
Thanks to its good performance and simplicity of implementation, the proposed algorithm is a good candidate for automated BL-LGE imaging in clinical practice.</description><identifier>ISSN: 1352-8661</identifier><identifier>EISSN: 1352-8661</identifier><identifier>DOI: 10.1007/s10334-023-01101-2</identifier><identifier>PMID: 37294423</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Biomedical Engineering and Bioengineering ; Clinical Applications - Cardiac ; Computer Appl. in Life Sciences ; Health Informatics ; Imaging ; Medicine ; Medicine & Public Health ; Radiology ; Research Article ; Solid State Physics</subject><ispartof>Magma (New York, N.Y.), 2023-12, Vol.36 (6), p.877-885</ispartof><rights>The Author(s) 2023</rights><rights>2023. The Author(s).</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c391t-9f0b6056b45afa097527b898950f5c105425f5a173f6de5d177cdf70cd29aa7a3</citedby><cites>FETCH-LOGICAL-c391t-9f0b6056b45afa097527b898950f5c105425f5a173f6de5d177cdf70cd29aa7a3</cites><orcidid>0000-0002-2845-8617</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10334-023-01101-2$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10334-023-01101-2$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37294423$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Maillot, Aurélien</creatorcontrib><creatorcontrib>Sridi, Soumaya</creatorcontrib><creatorcontrib>Pineau, Xavier</creatorcontrib><creatorcontrib>André-Billeau, Amandine</creatorcontrib><creatorcontrib>Hosteins, Stéphanie</creatorcontrib><creatorcontrib>Maes, Jean-David</creatorcontrib><creatorcontrib>Montier, Géraldine</creatorcontrib><creatorcontrib>Nuñez-Garcia, Marta</creatorcontrib><creatorcontrib>Quesson, Bruno</creatorcontrib><creatorcontrib>Sermesant, Maxime</creatorcontrib><creatorcontrib>Cochet, Hubert</creatorcontrib><creatorcontrib>Stuber, Matthias</creatorcontrib><creatorcontrib>Bustin, Aurélien</creatorcontrib><title>Automated inversion time selection for black-blood late gadolinium enhancement cardiac imaging in clinical practice</title><title>Magma (New York, N.Y.)</title><addtitle>Magn Reson Mater Phy</addtitle><addtitle>MAGMA</addtitle><description>Objective
To simplify black-blood late gadolinium enhancement (BL-LGE) cardiac imaging in clinical practice using an image-based algorithm for automated inversion time (TI) selection.
Materials and methods
The algorithm selects from BL-LGE TI scout images, the TI corresponding to the image with the highest number of sub-threshold pixels within a region of interest (ROI) encompassing the blood-pool and myocardium. The threshold value corresponds to the most recurrent pixel intensity of all scout images within the ROI. ROI dimensions were optimized in 40 patients’ scans. The algorithm was validated retrospectively (80 patients) versus two experts and tested prospectively (5 patients) on a 1.5 T clinical scanner.
Results
Automated TI selection took ~ 40 ms per dataset (manual: ~ 17 s). Fleiss’ kappa coefficient for automated-manual, intra-observer and inter-observer agreements were
κ
¯
= 0.73,
κ
¯
= 0.70 and
κ
¯
= 0.63, respectively. The agreement between the algorithm and any expert was better than the agreement between the two experts or between two selections of one expert.
Discussion
Thanks to its good performance and simplicity of implementation, the proposed algorithm is a good candidate for automated BL-LGE imaging in clinical practice.</description><subject>Biomedical Engineering and Bioengineering</subject><subject>Clinical Applications - Cardiac</subject><subject>Computer Appl. in Life Sciences</subject><subject>Health Informatics</subject><subject>Imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Radiology</subject><subject>Research Article</subject><subject>Solid State Physics</subject><issn>1352-8661</issn><issn>1352-8661</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><recordid>eNp9kE1vFSEUQInR2Fr9A10Ylm5GLzAMM8umaW2TJt3omtzh40nLwBNmTPz38nzVdNUVEM49yT2EnDP4zADUl8pAiL4DLjpgDFjHX5FTJiTvxmFgr5_dT8i7Wh8AOJMg3pITofjU91ycknqxrXnB1Vka0i9XasiJrmFxtLrozHp4-lzoHNE8dnPM2dLYcLpDm2NIYVuoSz8wGbe4tFKDxQY0NCy4C2nXpNQcMIOR7gs2oXHvyRuPsboPT-cZ-X599e3ypru7_3p7eXHXGTGxtZs8zAPIYe4leoRJSa7mcRonCV4aBrLn0ktkSvjBOmmZUsZ6BcbyCVGhOCOfjt59yT83V1e9hGpcjJhc3qrmI--HUapJNpQfUVNyrcV5vS9thfJbM9CH2PoYW7fY-m9szdvQxyf_Ni_O_h_5V7cB4gjU9pV2ruiHvJXUdn5J-wf8PIur</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Maillot, Aurélien</creator><creator>Sridi, Soumaya</creator><creator>Pineau, Xavier</creator><creator>André-Billeau, Amandine</creator><creator>Hosteins, Stéphanie</creator><creator>Maes, Jean-David</creator><creator>Montier, Géraldine</creator><creator>Nuñez-Garcia, Marta</creator><creator>Quesson, Bruno</creator><creator>Sermesant, Maxime</creator><creator>Cochet, Hubert</creator><creator>Stuber, Matthias</creator><creator>Bustin, Aurélien</creator><general>Springer International Publishing</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-2845-8617</orcidid></search><sort><creationdate>20231201</creationdate><title>Automated inversion time selection for black-blood late gadolinium enhancement cardiac imaging in clinical practice</title><author>Maillot, Aurélien ; Sridi, Soumaya ; Pineau, Xavier ; André-Billeau, Amandine ; Hosteins, Stéphanie ; Maes, Jean-David ; Montier, Géraldine ; Nuñez-Garcia, Marta ; Quesson, Bruno ; Sermesant, Maxime ; Cochet, Hubert ; Stuber, Matthias ; Bustin, Aurélien</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c391t-9f0b6056b45afa097527b898950f5c105425f5a173f6de5d177cdf70cd29aa7a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Biomedical Engineering and Bioengineering</topic><topic>Clinical Applications - Cardiac</topic><topic>Computer Appl. in Life Sciences</topic><topic>Health Informatics</topic><topic>Imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Radiology</topic><topic>Research Article</topic><topic>Solid State Physics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Maillot, Aurélien</creatorcontrib><creatorcontrib>Sridi, Soumaya</creatorcontrib><creatorcontrib>Pineau, Xavier</creatorcontrib><creatorcontrib>André-Billeau, Amandine</creatorcontrib><creatorcontrib>Hosteins, Stéphanie</creatorcontrib><creatorcontrib>Maes, Jean-David</creatorcontrib><creatorcontrib>Montier, Géraldine</creatorcontrib><creatorcontrib>Nuñez-Garcia, Marta</creatorcontrib><creatorcontrib>Quesson, Bruno</creatorcontrib><creatorcontrib>Sermesant, Maxime</creatorcontrib><creatorcontrib>Cochet, Hubert</creatorcontrib><creatorcontrib>Stuber, Matthias</creatorcontrib><creatorcontrib>Bustin, Aurélien</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Magma (New York, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Maillot, Aurélien</au><au>Sridi, Soumaya</au><au>Pineau, Xavier</au><au>André-Billeau, Amandine</au><au>Hosteins, Stéphanie</au><au>Maes, Jean-David</au><au>Montier, Géraldine</au><au>Nuñez-Garcia, Marta</au><au>Quesson, Bruno</au><au>Sermesant, Maxime</au><au>Cochet, Hubert</au><au>Stuber, Matthias</au><au>Bustin, Aurélien</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automated inversion time selection for black-blood late gadolinium enhancement cardiac imaging in clinical practice</atitle><jtitle>Magma (New York, N.Y.)</jtitle><stitle>Magn Reson Mater Phy</stitle><addtitle>MAGMA</addtitle><date>2023-12-01</date><risdate>2023</risdate><volume>36</volume><issue>6</issue><spage>877</spage><epage>885</epage><pages>877-885</pages><issn>1352-8661</issn><eissn>1352-8661</eissn><abstract>Objective
To simplify black-blood late gadolinium enhancement (BL-LGE) cardiac imaging in clinical practice using an image-based algorithm for automated inversion time (TI) selection.
Materials and methods
The algorithm selects from BL-LGE TI scout images, the TI corresponding to the image with the highest number of sub-threshold pixels within a region of interest (ROI) encompassing the blood-pool and myocardium. The threshold value corresponds to the most recurrent pixel intensity of all scout images within the ROI. ROI dimensions were optimized in 40 patients’ scans. The algorithm was validated retrospectively (80 patients) versus two experts and tested prospectively (5 patients) on a 1.5 T clinical scanner.
Results
Automated TI selection took ~ 40 ms per dataset (manual: ~ 17 s). Fleiss’ kappa coefficient for automated-manual, intra-observer and inter-observer agreements were
κ
¯
= 0.73,
κ
¯
= 0.70 and
κ
¯
= 0.63, respectively. The agreement between the algorithm and any expert was better than the agreement between the two experts or between two selections of one expert.
Discussion
Thanks to its good performance and simplicity of implementation, the proposed algorithm is a good candidate for automated BL-LGE imaging in clinical practice.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>37294423</pmid><doi>10.1007/s10334-023-01101-2</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-2845-8617</orcidid><oa>free_for_read</oa></addata></record> |
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| issn | 1352-8661 1352-8661 |
| language | eng |
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| subjects | Biomedical Engineering and Bioengineering Clinical Applications - Cardiac Computer Appl. in Life Sciences Health Informatics Imaging Medicine Medicine & Public Health Radiology Research Article Solid State Physics |
| title | Automated inversion time selection for black-blood late gadolinium enhancement cardiac imaging in clinical practice |
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