Accelerated Cine Cardiac MRI Using Deep Learning‐Based Reconstruction: A Systematic Evaluation
Background Breath‐holding (BH) for cine balanced steady state free precession (bSSFP) imaging is challenging for patients with impaired BH capacity. Deep learning‐based reconstruction (DLR) of undersampled k‐space promises to shorten BHs while preserving image quality and accuracy of ventricular ass...
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| Veröffentlicht in: | Journal of magnetic resonance imaging 2024-08, Vol.60 (2), p.640-650 |
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| creator | Pednekar, Amol Kocaoglu, Murat Wang, Hui Tanimoto, Aki Tkach, Jean A. Lang, Sean Taylor, Michael D. |
| description | Background
Breath‐holding (BH) for cine balanced steady state free precession (bSSFP) imaging is challenging for patients with impaired BH capacity. Deep learning‐based reconstruction (DLR) of undersampled k‐space promises to shorten BHs while preserving image quality and accuracy of ventricular assessment.
Purpose
To perform a systematic evaluation of DLR of cine bSSFP images from undersampled k‐space over a range of acceleration factors.
Study Type
Retrospective.
Subjects
Fifteen pectus excavatum patients (mean age 16.8 ± 5.4 years, 20% female) with normal cardiac anatomy and function and 12‐second BH capability.
Field Strength/Sequence
1.5‐T, cine bSSFP.
Assessment
Retrospective DLR was conducted by applying compressed sensitivity encoding (C‐SENSE) acceleration to systematically undersample fully sampled k‐space cine bSSFP acquisition data over an acceleration/undersampling factor (R) considering a range of 2 to 8. Quality imperceptibility (QI) measures, including structural similarity index measure, were calculated using images reconstructed from fully sampled k‐space as a reference. Image quality, including contrast and edge definition, was evaluated for diagnostic adequacy by three readers with varying levels of experience in cardiac MRI (>4 years, >18 years, and 1 year). Automated DL‐based biventricular segmentation was performed commercially available software by cardiac radiologists with more than 4 years of experience.
Statistical Tests
Tukey box plots, linear mixed effects model, analysis of variance (ANOVA), weighted kappa, Kruskal–Wallis test, and Wilcoxon signed‐rank test were employed as appropriate. A P‐value |
| doi_str_mv | 10.1002/jmri.29081 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2879406582</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2879406582</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4591-2463749acca26fc593f86cc3fcb77b4917ae99b01aa99e8f5afc1c1e52cc5dd33</originalsourceid><addsrcrecordid>eNp9kM9uFDEMhyNERUvhwgOgSFwQ0pQkM5kk3Jal0KJFSIWeg9fjQVnNnyWZAe2NR-gz8iRku4UDB0625U8_2R9jT6Q4k0Kol5s-hjPlhJX32InUShVK2_p-7oUuC2mFOWYPU9oIIZyr9AN2XBqrtdLmhH1ZIFJHESZq-DIMxJcQmwDIP1xd8usUhq_8DdGWrwjikKdfP29eQ8rwFeE4pCnOOIVxeMUX_NMuTdTDFJCff4duhv3iETtqoUv0-K6esuu355-XF8Xq47vL5WJVYKWdLFRVl6ZygAiqblG7srU1Ytni2ph15aQBcm4tJIBzZFsNLUqUpBWibpqyPGXPD7nbOH6bKU2-Dym_1sFA45y8ssZVotZWZfTZP-hmnOOQr_OlMNaaSro6Uy8OFMYxpUit38bQQ9x5Kfzeu99797feM_z0LnJe99T8Rf-IzoA8AD9CR7v_RPn3Wfwh9DfT4442</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3078874196</pqid></control><display><type>article</type><title>Accelerated Cine Cardiac MRI Using Deep Learning‐Based Reconstruction: A Systematic Evaluation</title><source>Access via Wiley Online Library</source><source>MEDLINE</source><creator>Pednekar, Amol ; Kocaoglu, Murat ; Wang, Hui ; Tanimoto, Aki ; Tkach, Jean A. ; Lang, Sean ; Taylor, Michael D.</creator><creatorcontrib>Pednekar, Amol ; Kocaoglu, Murat ; Wang, Hui ; Tanimoto, Aki ; Tkach, Jean A. ; Lang, Sean ; Taylor, Michael D.</creatorcontrib><description>Background
Breath‐holding (BH) for cine balanced steady state free precession (bSSFP) imaging is challenging for patients with impaired BH capacity. Deep learning‐based reconstruction (DLR) of undersampled k‐space promises to shorten BHs while preserving image quality and accuracy of ventricular assessment.
Purpose
To perform a systematic evaluation of DLR of cine bSSFP images from undersampled k‐space over a range of acceleration factors.
Study Type
Retrospective.
Subjects
Fifteen pectus excavatum patients (mean age 16.8 ± 5.4 years, 20% female) with normal cardiac anatomy and function and 12‐second BH capability.
Field Strength/Sequence
1.5‐T, cine bSSFP.
Assessment
Retrospective DLR was conducted by applying compressed sensitivity encoding (C‐SENSE) acceleration to systematically undersample fully sampled k‐space cine bSSFP acquisition data over an acceleration/undersampling factor (R) considering a range of 2 to 8. Quality imperceptibility (QI) measures, including structural similarity index measure, were calculated using images reconstructed from fully sampled k‐space as a reference. Image quality, including contrast and edge definition, was evaluated for diagnostic adequacy by three readers with varying levels of experience in cardiac MRI (>4 years, >18 years, and 1 year). Automated DL‐based biventricular segmentation was performed commercially available software by cardiac radiologists with more than 4 years of experience.
Statistical Tests
Tukey box plots, linear mixed effects model, analysis of variance (ANOVA), weighted kappa, Kruskal–Wallis test, and Wilcoxon signed‐rank test were employed as appropriate. A P‐value <0.05 was considered statistically significant.
Results
There was a significant decrease in the QI values and edge definition scores as R increased. Diagnostically adequate image quality was observed up to R = 5. The effect of R on all biventricular volumetric indices was non‐significant (P = 0.447).
Data Conclusion
The biventricular volumetric indices obtained from the reconstruction of fully sampled cine bSSFP acquisitions and DLR of the same k‐space data undersampled by C‐SENSE up to R = 5 may be comparable.
Evidence Level
3
Technical Efficacy
Stage 1</description><identifier>ISSN: 1053-1807</identifier><identifier>ISSN: 1522-2586</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.29081</identifier><identifier>PMID: 37855257</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>adaptive intelligence ; Adolescent ; Adult ; biventricular volume ; Breath Holding ; cardiac MRI ; Child ; cine imaging ; compressed SENSE ; Data acquisition ; Deep Learning ; Evaluation ; Female ; Field strength ; Heart ; Heart - diagnostic imaging ; Humans ; Image acquisition ; Image contrast ; Image Interpretation, Computer-Assisted - methods ; Image processing ; Image Processing, Computer-Assisted - methods ; Image quality ; Image reconstruction ; Magnetic resonance imaging ; Magnetic Resonance Imaging, Cine - methods ; Male ; Medical imaging ; Rank tests ; Reproducibility of Results ; Retrospective Studies ; Statistical analysis ; Statistical models ; Statistical tests ; Variance analysis ; Young Adult</subject><ispartof>Journal of magnetic resonance imaging, 2024-08, Vol.60 (2), p.640-650</ispartof><rights>2023 The Authors. published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.</rights><rights>2023 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.</rights><rights>2023. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4591-2463749acca26fc593f86cc3fcb77b4917ae99b01aa99e8f5afc1c1e52cc5dd33</citedby><cites>FETCH-LOGICAL-c4591-2463749acca26fc593f86cc3fcb77b4917ae99b01aa99e8f5afc1c1e52cc5dd33</cites><orcidid>0000-0002-4804-8789 ; 0000-0002-5686-3417 ; 0000-0003-4593-7481</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmri.29081$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.29081$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37855257$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pednekar, Amol</creatorcontrib><creatorcontrib>Kocaoglu, Murat</creatorcontrib><creatorcontrib>Wang, Hui</creatorcontrib><creatorcontrib>Tanimoto, Aki</creatorcontrib><creatorcontrib>Tkach, Jean A.</creatorcontrib><creatorcontrib>Lang, Sean</creatorcontrib><creatorcontrib>Taylor, Michael D.</creatorcontrib><title>Accelerated Cine Cardiac MRI Using Deep Learning‐Based Reconstruction: A Systematic Evaluation</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Background
Breath‐holding (BH) for cine balanced steady state free precession (bSSFP) imaging is challenging for patients with impaired BH capacity. Deep learning‐based reconstruction (DLR) of undersampled k‐space promises to shorten BHs while preserving image quality and accuracy of ventricular assessment.
Purpose
To perform a systematic evaluation of DLR of cine bSSFP images from undersampled k‐space over a range of acceleration factors.
Study Type
Retrospective.
Subjects
Fifteen pectus excavatum patients (mean age 16.8 ± 5.4 years, 20% female) with normal cardiac anatomy and function and 12‐second BH capability.
Field Strength/Sequence
1.5‐T, cine bSSFP.
Assessment
Retrospective DLR was conducted by applying compressed sensitivity encoding (C‐SENSE) acceleration to systematically undersample fully sampled k‐space cine bSSFP acquisition data over an acceleration/undersampling factor (R) considering a range of 2 to 8. Quality imperceptibility (QI) measures, including structural similarity index measure, were calculated using images reconstructed from fully sampled k‐space as a reference. Image quality, including contrast and edge definition, was evaluated for diagnostic adequacy by three readers with varying levels of experience in cardiac MRI (>4 years, >18 years, and 1 year). Automated DL‐based biventricular segmentation was performed commercially available software by cardiac radiologists with more than 4 years of experience.
Statistical Tests
Tukey box plots, linear mixed effects model, analysis of variance (ANOVA), weighted kappa, Kruskal–Wallis test, and Wilcoxon signed‐rank test were employed as appropriate. A P‐value <0.05 was considered statistically significant.
Results
There was a significant decrease in the QI values and edge definition scores as R increased. Diagnostically adequate image quality was observed up to R = 5. The effect of R on all biventricular volumetric indices was non‐significant (P = 0.447).
Data Conclusion
The biventricular volumetric indices obtained from the reconstruction of fully sampled cine bSSFP acquisitions and DLR of the same k‐space data undersampled by C‐SENSE up to R = 5 may be comparable.
Evidence Level
3
Technical Efficacy
Stage 1</description><subject>adaptive intelligence</subject><subject>Adolescent</subject><subject>Adult</subject><subject>biventricular volume</subject><subject>Breath Holding</subject><subject>cardiac MRI</subject><subject>Child</subject><subject>cine imaging</subject><subject>compressed SENSE</subject><subject>Data acquisition</subject><subject>Deep Learning</subject><subject>Evaluation</subject><subject>Female</subject><subject>Field strength</subject><subject>Heart</subject><subject>Heart - diagnostic imaging</subject><subject>Humans</subject><subject>Image acquisition</subject><subject>Image contrast</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image quality</subject><subject>Image reconstruction</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging, Cine - methods</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Rank tests</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Statistical tests</subject><subject>Variance analysis</subject><subject>Young Adult</subject><issn>1053-1807</issn><issn>1522-2586</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><recordid>eNp9kM9uFDEMhyNERUvhwgOgSFwQ0pQkM5kk3Jal0KJFSIWeg9fjQVnNnyWZAe2NR-gz8iRku4UDB0625U8_2R9jT6Q4k0Kol5s-hjPlhJX32InUShVK2_p-7oUuC2mFOWYPU9oIIZyr9AN2XBqrtdLmhH1ZIFJHESZq-DIMxJcQmwDIP1xd8usUhq_8DdGWrwjikKdfP29eQ8rwFeE4pCnOOIVxeMUX_NMuTdTDFJCff4duhv3iETtqoUv0-K6esuu355-XF8Xq47vL5WJVYKWdLFRVl6ZygAiqblG7srU1Ytni2ph15aQBcm4tJIBzZFsNLUqUpBWibpqyPGXPD7nbOH6bKU2-Dym_1sFA45y8ssZVotZWZfTZP-hmnOOQr_OlMNaaSro6Uy8OFMYxpUit38bQQ9x5Kfzeu99797feM_z0LnJe99T8Rf-IzoA8AD9CR7v_RPn3Wfwh9DfT4442</recordid><startdate>202408</startdate><enddate>202408</enddate><creator>Pednekar, Amol</creator><creator>Kocaoglu, Murat</creator><creator>Wang, Hui</creator><creator>Tanimoto, Aki</creator><creator>Tkach, Jean A.</creator><creator>Lang, Sean</creator><creator>Taylor, Michael D.</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>WIN</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-4804-8789</orcidid><orcidid>https://orcid.org/0000-0002-5686-3417</orcidid><orcidid>https://orcid.org/0000-0003-4593-7481</orcidid></search><sort><creationdate>202408</creationdate><title>Accelerated Cine Cardiac MRI Using Deep Learning‐Based Reconstruction: A Systematic Evaluation</title><author>Pednekar, Amol ; Kocaoglu, Murat ; Wang, Hui ; Tanimoto, Aki ; Tkach, Jean A. ; Lang, Sean ; Taylor, Michael D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4591-2463749acca26fc593f86cc3fcb77b4917ae99b01aa99e8f5afc1c1e52cc5dd33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>adaptive intelligence</topic><topic>Adolescent</topic><topic>Adult</topic><topic>biventricular volume</topic><topic>Breath Holding</topic><topic>cardiac MRI</topic><topic>Child</topic><topic>cine imaging</topic><topic>compressed SENSE</topic><topic>Data acquisition</topic><topic>Deep Learning</topic><topic>Evaluation</topic><topic>Female</topic><topic>Field strength</topic><topic>Heart</topic><topic>Heart - diagnostic imaging</topic><topic>Humans</topic><topic>Image acquisition</topic><topic>Image contrast</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Image quality</topic><topic>Image reconstruction</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging, Cine - methods</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Rank tests</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Statistical tests</topic><topic>Variance analysis</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pednekar, Amol</creatorcontrib><creatorcontrib>Kocaoglu, Murat</creatorcontrib><creatorcontrib>Wang, Hui</creatorcontrib><creatorcontrib>Tanimoto, Aki</creatorcontrib><creatorcontrib>Tkach, Jean A.</creatorcontrib><creatorcontrib>Lang, Sean</creatorcontrib><creatorcontrib>Taylor, Michael D.</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pednekar, Amol</au><au>Kocaoglu, Murat</au><au>Wang, Hui</au><au>Tanimoto, Aki</au><au>Tkach, Jean A.</au><au>Lang, Sean</au><au>Taylor, Michael D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accelerated Cine Cardiac MRI Using Deep Learning‐Based Reconstruction: A Systematic Evaluation</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2024-08</date><risdate>2024</risdate><volume>60</volume><issue>2</issue><spage>640</spage><epage>650</epage><pages>640-650</pages><issn>1053-1807</issn><issn>1522-2586</issn><eissn>1522-2586</eissn><abstract>Background
Breath‐holding (BH) for cine balanced steady state free precession (bSSFP) imaging is challenging for patients with impaired BH capacity. Deep learning‐based reconstruction (DLR) of undersampled k‐space promises to shorten BHs while preserving image quality and accuracy of ventricular assessment.
Purpose
To perform a systematic evaluation of DLR of cine bSSFP images from undersampled k‐space over a range of acceleration factors.
Study Type
Retrospective.
Subjects
Fifteen pectus excavatum patients (mean age 16.8 ± 5.4 years, 20% female) with normal cardiac anatomy and function and 12‐second BH capability.
Field Strength/Sequence
1.5‐T, cine bSSFP.
Assessment
Retrospective DLR was conducted by applying compressed sensitivity encoding (C‐SENSE) acceleration to systematically undersample fully sampled k‐space cine bSSFP acquisition data over an acceleration/undersampling factor (R) considering a range of 2 to 8. Quality imperceptibility (QI) measures, including structural similarity index measure, were calculated using images reconstructed from fully sampled k‐space as a reference. Image quality, including contrast and edge definition, was evaluated for diagnostic adequacy by three readers with varying levels of experience in cardiac MRI (>4 years, >18 years, and 1 year). Automated DL‐based biventricular segmentation was performed commercially available software by cardiac radiologists with more than 4 years of experience.
Statistical Tests
Tukey box plots, linear mixed effects model, analysis of variance (ANOVA), weighted kappa, Kruskal–Wallis test, and Wilcoxon signed‐rank test were employed as appropriate. A P‐value <0.05 was considered statistically significant.
Results
There was a significant decrease in the QI values and edge definition scores as R increased. Diagnostically adequate image quality was observed up to R = 5. The effect of R on all biventricular volumetric indices was non‐significant (P = 0.447).
Data Conclusion
The biventricular volumetric indices obtained from the reconstruction of fully sampled cine bSSFP acquisitions and DLR of the same k‐space data undersampled by C‐SENSE up to R = 5 may be comparable.
Evidence Level
3
Technical Efficacy
Stage 1</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>37855257</pmid><doi>10.1002/jmri.29081</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-4804-8789</orcidid><orcidid>https://orcid.org/0000-0002-5686-3417</orcidid><orcidid>https://orcid.org/0000-0003-4593-7481</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | adaptive intelligence Adolescent Adult biventricular volume Breath Holding cardiac MRI Child cine imaging compressed SENSE Data acquisition Deep Learning Evaluation Female Field strength Heart Heart - diagnostic imaging Humans Image acquisition Image contrast Image Interpretation, Computer-Assisted - methods Image processing Image Processing, Computer-Assisted - methods Image quality Image reconstruction Magnetic resonance imaging Magnetic Resonance Imaging, Cine - methods Male Medical imaging Rank tests Reproducibility of Results Retrospective Studies Statistical analysis Statistical models Statistical tests Variance analysis Young Adult |
| title | Accelerated Cine Cardiac MRI Using Deep Learning‐Based Reconstruction: A Systematic Evaluation |
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