Estimating ventilation correlation coefficients in the lungs using PREFUL‐MRI in chronic obstructive pulmonary disease patients and healthy adults
Purpose Various parameters of regional lung ventilation can be estimated using phase‐resolved functional lung (PREFUL)‐MRI. The parameter “ventilation correlation coefficient (Vent‐CC)” was shown advantageous because it assesses the dynamics of regional air flow. Calculating Vent‐CC depends on a vox...
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| Veröffentlicht in: | Magnetic resonance in medicine 2024-05, Vol.91 (5), p.2142-2152 |
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| creator | Moher Alsady, Tawfik Ruschepaul, Jakob Voskrebenzev, Andreas Klimes, Filip Poehler, Gesa Helen Vogel‐Claussen, Jens |
| description | Purpose
Various parameters of regional lung ventilation can be estimated using phase‐resolved functional lung (PREFUL)‐MRI. The parameter “ventilation correlation coefficient (Vent‐CC)” was shown advantageous because it assesses the dynamics of regional air flow. Calculating Vent‐CC depends on a voxel‐wise comparison to a healthy reference flow curve. This work examines the effect of placing a reference region of interest (ROI) in various lung quadrants or in different coronal slices. Furthermore, algorithms for automated ROI selection are presented and compared in terms of test–retest repeatability.
Methods
Twenty‐eight healthy subjects and 32 chronic obstructive pulmonary disease (COPD) patients were scanned twice using PREFUL‐MRI. Retrospective analyses examined the homogeneity of air flow curves of various reference ROIs using cross‐correlation. Vent‐CC and ventilation defect percentage (VDP) calculated using various reference ROIs were compared using one‐way analysis of variance (ANOVA). The coefficient of variation was calculated for Vent‐CC and VDP when using different reference selection algorithms.
Results
Flow‐volume curves were highly correlated between ROIs placed at various lung quadrants in the same coronal slice (r > 0.97) with no differences in Vent‐CC and VDP (ANOVA: p > 0.5). However, ROIs placed at different coronal slices showed lower correlation coefficients and resulted in significantly different Vent‐CC and VDP values (ANOVA: p |
| doi_str_mv | 10.1002/mrm.29982 |
| format | Article |
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Various parameters of regional lung ventilation can be estimated using phase‐resolved functional lung (PREFUL)‐MRI. The parameter “ventilation correlation coefficient (Vent‐CC)” was shown advantageous because it assesses the dynamics of regional air flow. Calculating Vent‐CC depends on a voxel‐wise comparison to a healthy reference flow curve. This work examines the effect of placing a reference region of interest (ROI) in various lung quadrants or in different coronal slices. Furthermore, algorithms for automated ROI selection are presented and compared in terms of test–retest repeatability.
Methods
Twenty‐eight healthy subjects and 32 chronic obstructive pulmonary disease (COPD) patients were scanned twice using PREFUL‐MRI. Retrospective analyses examined the homogeneity of air flow curves of various reference ROIs using cross‐correlation. Vent‐CC and ventilation defect percentage (VDP) calculated using various reference ROIs were compared using one‐way analysis of variance (ANOVA). The coefficient of variation was calculated for Vent‐CC and VDP when using different reference selection algorithms.
Results
Flow‐volume curves were highly correlated between ROIs placed at various lung quadrants in the same coronal slice (r > 0.97) with no differences in Vent‐CC and VDP (ANOVA: p > 0.5). However, ROIs placed at different coronal slices showed lower correlation coefficients and resulted in significantly different Vent‐CC and VDP values (ANOVA: p < 0.001). Vent‐CC and VDP showed higher repeatability when calculated using the presented new algorithm.
Conclusion
In COPD and healthy cohorts, assessing regional ventilation dynamics using PREFUL‐MRI in terms of the Vent‐CC metric showed higher repeatability using a new algorithm for selecting a homogenous reference ROI from the same slice.</description><identifier>ISSN: 0740-3194</identifier><identifier>EISSN: 1522-2594</identifier><identifier>DOI: 10.1002/mrm.29982</identifier><identifier>PMID: 38217450</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Adult ; Air flow ; Algorithms ; Chronic obstructive pulmonary disease ; Coefficient of variation ; Correlation coefficient ; Correlation coefficients ; fMRI ; Functional magnetic resonance imaging ; Homogeneity ; Humans ; Lung - diagnostic imaging ; Lung diseases ; Lungs ; Magnetic Resonance Imaging - methods ; Mathematical analysis ; Obstructive lung disease ; Parameters ; PREFUL ; Pulmonary Disease, Chronic Obstructive - diagnostic imaging ; Pulmonary Ventilation ; Quadrants ; Regional analysis ; Reproducibility ; Respiration ; Retrospective Studies ; Variance analysis ; Ventilation</subject><ispartof>Magnetic resonance in medicine, 2024-05, Vol.91 (5), p.2142-2152</ispartof><rights>2024 The Authors. published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.</rights><rights>2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.</rights><rights>2024. This article is published under http://creativecommons.org/licenses/by/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-c3882-20e125696c6738b7f5a7e8fec358934fff6ec7549f681eb100e58c29b73d937d3</citedby><cites>FETCH-LOGICAL-c3882-20e125696c6738b7f5a7e8fec358934fff6ec7549f681eb100e58c29b73d937d3</cites><orcidid>0000-0001-5699-1841 ; 0000-0002-0779-0688 ; 0000-0003-2715-0757</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%2Fmrm.29982$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fmrm.29982$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1416,27923,27924,45573,45574</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38217450$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Moher Alsady, Tawfik</creatorcontrib><creatorcontrib>Ruschepaul, Jakob</creatorcontrib><creatorcontrib>Voskrebenzev, Andreas</creatorcontrib><creatorcontrib>Klimes, Filip</creatorcontrib><creatorcontrib>Poehler, Gesa Helen</creatorcontrib><creatorcontrib>Vogel‐Claussen, Jens</creatorcontrib><title>Estimating ventilation correlation coefficients in the lungs using PREFUL‐MRI in chronic obstructive pulmonary disease patients and healthy adults</title><title>Magnetic resonance in medicine</title><addtitle>Magn Reson Med</addtitle><description>Purpose
Various parameters of regional lung ventilation can be estimated using phase‐resolved functional lung (PREFUL)‐MRI. The parameter “ventilation correlation coefficient (Vent‐CC)” was shown advantageous because it assesses the dynamics of regional air flow. Calculating Vent‐CC depends on a voxel‐wise comparison to a healthy reference flow curve. This work examines the effect of placing a reference region of interest (ROI) in various lung quadrants or in different coronal slices. Furthermore, algorithms for automated ROI selection are presented and compared in terms of test–retest repeatability.
Methods
Twenty‐eight healthy subjects and 32 chronic obstructive pulmonary disease (COPD) patients were scanned twice using PREFUL‐MRI. Retrospective analyses examined the homogeneity of air flow curves of various reference ROIs using cross‐correlation. Vent‐CC and ventilation defect percentage (VDP) calculated using various reference ROIs were compared using one‐way analysis of variance (ANOVA). The coefficient of variation was calculated for Vent‐CC and VDP when using different reference selection algorithms.
Results
Flow‐volume curves were highly correlated between ROIs placed at various lung quadrants in the same coronal slice (r > 0.97) with no differences in Vent‐CC and VDP (ANOVA: p > 0.5). However, ROIs placed at different coronal slices showed lower correlation coefficients and resulted in significantly different Vent‐CC and VDP values (ANOVA: p < 0.001). Vent‐CC and VDP showed higher repeatability when calculated using the presented new algorithm.
Conclusion
In COPD and healthy cohorts, assessing regional ventilation dynamics using PREFUL‐MRI in terms of the Vent‐CC metric showed higher repeatability using a new algorithm for selecting a homogenous reference ROI from the same slice.</description><subject>Adult</subject><subject>Air flow</subject><subject>Algorithms</subject><subject>Chronic obstructive pulmonary disease</subject><subject>Coefficient of variation</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>fMRI</subject><subject>Functional magnetic resonance imaging</subject><subject>Homogeneity</subject><subject>Humans</subject><subject>Lung - diagnostic imaging</subject><subject>Lung diseases</subject><subject>Lungs</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Mathematical analysis</subject><subject>Obstructive lung disease</subject><subject>Parameters</subject><subject>PREFUL</subject><subject>Pulmonary Disease, Chronic Obstructive - diagnostic imaging</subject><subject>Pulmonary Ventilation</subject><subject>Quadrants</subject><subject>Regional analysis</subject><subject>Reproducibility</subject><subject>Respiration</subject><subject>Retrospective Studies</subject><subject>Variance analysis</subject><subject>Ventilation</subject><issn>0740-3194</issn><issn>1522-2594</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><recordid>eNp1kctuEzEUhi1ERUNhwQsgS2xgkdaX8dheoiqFSomKIroezXiOG1cznuBLUXZ9hC54Qp4EpyldILHykc_nT8fnR-gdJaeUEHY2hvGUaa3YCzSjgrE5E7p6iWZEVmTOqa6O0esYbwkhWsvqFTrmilFZCTJDvxYxubFNzt_gO_DJDaWePDZTCPBcg7XOuNKO2HmcNoCH7G8iznH_7tt6cXG9_H3_sFpf7vtmEybvDJ66mEI2yd0B3uZhnHwbdrh3EdpYbor80dj6Hm-gHdJmh9s-Dym-QUe2HSK8fTpP0PXF4vv51_ny6svl-efl3HClyi8JUCZqXZtactVJK1oJyoLhQmleWWtrMFJU2taKQldWBUIZpjvJe81lz0_Qx4N3G6YfGWJqRhcNDEPrYcqxYZppwpmoVEE__IPeTjn4Ml2hakmZkkQU6tOBMmGKMYBttqFsN-waSpp9VE2JqnmMqrDvn4y5G6F_Jv9mU4CzA_DTDbD7v6lZrVcH5R_xfKDl</recordid><startdate>202405</startdate><enddate>202405</enddate><creator>Moher Alsady, Tawfik</creator><creator>Ruschepaul, Jakob</creator><creator>Voskrebenzev, Andreas</creator><creator>Klimes, Filip</creator><creator>Poehler, Gesa Helen</creator><creator>Vogel‐Claussen, Jens</creator><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>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>M7Z</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-5699-1841</orcidid><orcidid>https://orcid.org/0000-0002-0779-0688</orcidid><orcidid>https://orcid.org/0000-0003-2715-0757</orcidid></search><sort><creationdate>202405</creationdate><title>Estimating ventilation correlation coefficients in the lungs using PREFUL‐MRI in chronic obstructive pulmonary disease patients and healthy adults</title><author>Moher Alsady, Tawfik ; Ruschepaul, Jakob ; Voskrebenzev, Andreas ; Klimes, Filip ; Poehler, Gesa Helen ; Vogel‐Claussen, Jens</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3882-20e125696c6738b7f5a7e8fec358934fff6ec7549f681eb100e58c29b73d937d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Adult</topic><topic>Air flow</topic><topic>Algorithms</topic><topic>Chronic obstructive pulmonary disease</topic><topic>Coefficient of variation</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>fMRI</topic><topic>Functional magnetic resonance imaging</topic><topic>Homogeneity</topic><topic>Humans</topic><topic>Lung - diagnostic imaging</topic><topic>Lung diseases</topic><topic>Lungs</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Mathematical analysis</topic><topic>Obstructive lung disease</topic><topic>Parameters</topic><topic>PREFUL</topic><topic>Pulmonary Disease, Chronic Obstructive - diagnostic imaging</topic><topic>Pulmonary Ventilation</topic><topic>Quadrants</topic><topic>Regional analysis</topic><topic>Reproducibility</topic><topic>Respiration</topic><topic>Retrospective Studies</topic><topic>Variance analysis</topic><topic>Ventilation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Moher Alsady, Tawfik</creatorcontrib><creatorcontrib>Ruschepaul, Jakob</creatorcontrib><creatorcontrib>Voskrebenzev, Andreas</creatorcontrib><creatorcontrib>Klimes, Filip</creatorcontrib><creatorcontrib>Poehler, Gesa Helen</creatorcontrib><creatorcontrib>Vogel‐Claussen, Jens</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley Free Content</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biochemistry Abstracts 1</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Magnetic resonance in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Moher Alsady, Tawfik</au><au>Ruschepaul, Jakob</au><au>Voskrebenzev, Andreas</au><au>Klimes, Filip</au><au>Poehler, Gesa Helen</au><au>Vogel‐Claussen, Jens</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating ventilation correlation coefficients in the lungs using PREFUL‐MRI in chronic obstructive pulmonary disease patients and healthy adults</atitle><jtitle>Magnetic resonance in medicine</jtitle><addtitle>Magn Reson Med</addtitle><date>2024-05</date><risdate>2024</risdate><volume>91</volume><issue>5</issue><spage>2142</spage><epage>2152</epage><pages>2142-2152</pages><issn>0740-3194</issn><eissn>1522-2594</eissn><abstract>Purpose
Various parameters of regional lung ventilation can be estimated using phase‐resolved functional lung (PREFUL)‐MRI. The parameter “ventilation correlation coefficient (Vent‐CC)” was shown advantageous because it assesses the dynamics of regional air flow. Calculating Vent‐CC depends on a voxel‐wise comparison to a healthy reference flow curve. This work examines the effect of placing a reference region of interest (ROI) in various lung quadrants or in different coronal slices. Furthermore, algorithms for automated ROI selection are presented and compared in terms of test–retest repeatability.
Methods
Twenty‐eight healthy subjects and 32 chronic obstructive pulmonary disease (COPD) patients were scanned twice using PREFUL‐MRI. Retrospective analyses examined the homogeneity of air flow curves of various reference ROIs using cross‐correlation. Vent‐CC and ventilation defect percentage (VDP) calculated using various reference ROIs were compared using one‐way analysis of variance (ANOVA). The coefficient of variation was calculated for Vent‐CC and VDP when using different reference selection algorithms.
Results
Flow‐volume curves were highly correlated between ROIs placed at various lung quadrants in the same coronal slice (r > 0.97) with no differences in Vent‐CC and VDP (ANOVA: p > 0.5). However, ROIs placed at different coronal slices showed lower correlation coefficients and resulted in significantly different Vent‐CC and VDP values (ANOVA: p < 0.001). Vent‐CC and VDP showed higher repeatability when calculated using the presented new algorithm.
Conclusion
In COPD and healthy cohorts, assessing regional ventilation dynamics using PREFUL‐MRI in terms of the Vent‐CC metric showed higher repeatability using a new algorithm for selecting a homogenous reference ROI from the same slice.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>38217450</pmid><doi>10.1002/mrm.29982</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-5699-1841</orcidid><orcidid>https://orcid.org/0000-0002-0779-0688</orcidid><orcidid>https://orcid.org/0000-0003-2715-0757</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Wiley Online Library All Journals |
| subjects | Adult Air flow Algorithms Chronic obstructive pulmonary disease Coefficient of variation Correlation coefficient Correlation coefficients fMRI Functional magnetic resonance imaging Homogeneity Humans Lung - diagnostic imaging Lung diseases Lungs Magnetic Resonance Imaging - methods Mathematical analysis Obstructive lung disease Parameters PREFUL Pulmonary Disease, Chronic Obstructive - diagnostic imaging Pulmonary Ventilation Quadrants Regional analysis Reproducibility Respiration Retrospective Studies Variance analysis Ventilation |
| title | Estimating ventilation correlation coefficients in the lungs using PREFUL‐MRI in chronic obstructive pulmonary disease patients and healthy adults |
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