Quantifying Spatial Distribution of Ventilation Defects in Multiple Pulmonary Diseases With Hyperpolarized 129Xenon MRI

Hyperpolarized 129Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.BACKGROUNDHyperpolarized 129Xe MRI assesses lung ventilation, often using the ventilation defect percentage (V...

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Published in:Journal of magnetic resonance imaging 2024-10
Main Authors: Bdaiwi, Abdullah S, Willmering, Matthew M, Woods, Jason C, Walkup, Laura L, Cleveland, Zackary I
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Willmering, Matthew M
Woods, Jason C
Walkup, Laura L
Cleveland, Zackary I
description Hyperpolarized 129Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.BACKGROUNDHyperpolarized 129Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.To quantify spatial distribution of 129Xe ventilation defects using DDI across pulmonary diseases.PURPOSETo quantify spatial distribution of 129Xe ventilation defects using DDI across pulmonary diseases.Retrospective.STUDY TYPERetrospective.Four hundred twenty-one subjects (age = 23.1 ± 17.1, female = 230), comprising healthy controls (N = 60) and subjects with obstructive conditions (asthma [N = 25], bronchiolitis obliterans syndrome [BOS, N = 18], cystic fibrosis [CF, N = 90], lymphangioleiomyomatosis [LAM, N = 50]), restrictive conditions (bleomycin-treated cancer survivors [BLEO, N = 14]; fibrotic lung diseases [FLD, N = 92]), bone marrow transplantation (BMT, N = 53), and bronchopulmonary dysplasia (BPD, N = 19).SUBJECTSFour hundred twenty-one subjects (age = 23.1 ± 17.1, female = 230), comprising healthy controls (N = 60) and subjects with obstructive conditions (asthma [N = 25], bronchiolitis obliterans syndrome [BOS, N = 18], cystic fibrosis [CF, N = 90], lymphangioleiomyomatosis [LAM, N = 50]), restrictive conditions (bleomycin-treated cancer survivors [BLEO, N = 14]; fibrotic lung diseases [FLD, N = 92]), bone marrow transplantation (BMT, N = 53), and bronchopulmonary dysplasia (BPD, N = 19).3 T, two-dimensional multi-slice gradient echo.FIELD STRENGTH/SEQUENCE3 T, two-dimensional multi-slice gradient echo.Whole-lung mean DDI was extracted from DDI maps; correlated with VDP (percent of pixels
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Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.BACKGROUNDHyperpolarized 129Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.To quantify spatial distribution of 129Xe ventilation defects using DDI across pulmonary diseases.PURPOSETo quantify spatial distribution of 129Xe ventilation defects using DDI across pulmonary diseases.Retrospective.STUDY TYPERetrospective.Four hundred twenty-one subjects (age = 23.1 ± 17.1, female = 230), comprising healthy controls (N = 60) and subjects with obstructive conditions (asthma [N = 25], bronchiolitis obliterans syndrome [BOS, N = 18], cystic fibrosis [CF, N = 90], lymphangioleiomyomatosis [LAM, N = 50]), restrictive conditions (bleomycin-treated cancer survivors [BLEO, N = 14]; fibrotic lung diseases [FLD, N = 92]), bone marrow transplantation (BMT, N = 53), and bronchopulmonary dysplasia (BPD, N = 19).SUBJECTSFour hundred twenty-one subjects (age = 23.1 ± 17.1, female = 230), comprising healthy controls (N = 60) and subjects with obstructive conditions (asthma [N = 25], bronchiolitis obliterans syndrome [BOS, N = 18], cystic fibrosis [CF, N = 90], lymphangioleiomyomatosis [LAM, N = 50]), restrictive conditions (bleomycin-treated cancer survivors [BLEO, N = 14]; fibrotic lung diseases [FLD, N = 92]), bone marrow transplantation (BMT, N = 53), and bronchopulmonary dysplasia (BPD, N = 19).3 T, two-dimensional multi-slice gradient echo.FIELD STRENGTH/SEQUENCE3 T, two-dimensional multi-slice gradient echo.Whole-lung mean DDI was extracted from DDI maps; correlated with VDP (percent of pixels <60% of whole-lung mean signal intensity) and pulmonary function tests (PFTs) including FEV1, FVC, and FEV1/FVC. DDI and DDI/VDP, a marker of defect clustering, were compared across diseases.ASSESSMENTWhole-lung mean DDI was extracted from DDI maps; correlated with VDP (percent of pixels <60% of whole-lung mean signal intensity) and pulmonary function tests (PFTs) including FEV1, FVC, and FEV1/FVC. DDI and DDI/VDP, a marker of defect clustering, were compared across diseases.Pearson correlation analysis and Kruskal-Wallis tests. P < 0.0056 for disease groups, P < 0.0125 for categories.STATISTICAL TESTSPearson correlation analysis and Kruskal-Wallis tests. P < 0.0056 for disease groups, P < 0.0125 for categories.DDI was significantly elevated in BMT (8.3 ± 11.5), BOS (30.1 ± 57.5), BPD (16.0 ± 46.8), CF (15.4 ± 27.2), and LAM (12.6 ± 34.2) compared to controls (1.8 ± 3.1). DDI correlated significantly with VDP in all groups (r ≥ 0.56) except BLEO, and with PFTs in CF, FLD, and LAM (r ≥ 0.56). Obstructive groups had significantly higher mean DDI (14.0 ± 32.0) than controls (1.8 ± 3.0) and restrictive groups (4.0 ± 12.0). DDI/VDP was significantly lower in the restrictive group (0.6 ± 0.6) than controls (0.8 ± 0.6) and obstructive group (1.0 ± 1.0).RESULTSDDI was significantly elevated in BMT (8.3 ± 11.5), BOS (30.1 ± 57.5), BPD (16.0 ± 46.8), CF (15.4 ± 27.2), and LAM (12.6 ± 34.2) compared to controls (1.8 ± 3.1). DDI correlated significantly with VDP in all groups (r ≥ 0.56) except BLEO, and with PFTs in CF, FLD, and LAM (r ≥ 0.56). Obstructive groups had significantly higher mean DDI (14.0 ± 32.0) than controls (1.8 ± 3.0) and restrictive groups (4.0 ± 12.0). DDI/VDP was significantly lower in the restrictive group (0.6 ± 0.6) than controls (0.8 ± 0.6) and obstructive group (1.0 ± 1.0).DDI may provide insights into the distribution of ventilation defects across diseases.DATA CONCLUSIONDDI may provide insights into the distribution of ventilation defects across diseases.3 TECHNICAL EFFICACY: Stage 2.EVIDENCE LEVEL3 TECHNICAL EFFICACY: Stage 2.]]></description><identifier>ISSN: 1522-2586</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.29627</identifier><language>eng</language><ispartof>Journal of magnetic resonance imaging, 2024-10</ispartof><rights>2024 The Author(s). Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Bdaiwi, Abdullah S</creatorcontrib><creatorcontrib>Willmering, Matthew M</creatorcontrib><creatorcontrib>Woods, Jason C</creatorcontrib><creatorcontrib>Walkup, Laura L</creatorcontrib><creatorcontrib>Cleveland, Zackary I</creatorcontrib><title>Quantifying Spatial Distribution of Ventilation Defects in Multiple Pulmonary Diseases With Hyperpolarized 129Xenon MRI</title><title>Journal of magnetic resonance imaging</title><description><![CDATA[Hyperpolarized 129Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.BACKGROUNDHyperpolarized 129Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.To quantify spatial distribution of 129Xe ventilation defects using DDI across pulmonary diseases.PURPOSETo quantify spatial distribution of 129Xe ventilation defects using DDI across pulmonary diseases.Retrospective.STUDY TYPERetrospective.Four hundred twenty-one subjects (age = 23.1 ± 17.1, female = 230), comprising healthy controls (N = 60) and subjects with obstructive conditions (asthma [N = 25], bronchiolitis obliterans syndrome [BOS, N = 18], cystic fibrosis [CF, N = 90], lymphangioleiomyomatosis [LAM, N = 50]), restrictive conditions (bleomycin-treated cancer survivors [BLEO, N = 14]; fibrotic lung diseases [FLD, N = 92]), bone marrow transplantation (BMT, N = 53), and bronchopulmonary dysplasia (BPD, N = 19).SUBJECTSFour hundred twenty-one subjects (age = 23.1 ± 17.1, female = 230), comprising healthy controls (N = 60) and subjects with obstructive conditions (asthma [N = 25], bronchiolitis obliterans syndrome [BOS, N = 18], cystic fibrosis [CF, N = 90], lymphangioleiomyomatosis [LAM, N = 50]), restrictive conditions (bleomycin-treated cancer survivors [BLEO, N = 14]; fibrotic lung diseases [FLD, N = 92]), bone marrow transplantation (BMT, N = 53), and bronchopulmonary dysplasia (BPD, N = 19).3 T, two-dimensional multi-slice gradient echo.FIELD STRENGTH/SEQUENCE3 T, two-dimensional multi-slice gradient echo.Whole-lung mean DDI was extracted from DDI maps; correlated with VDP (percent of pixels <60% of whole-lung mean signal intensity) and pulmonary function tests (PFTs) including FEV1, FVC, and FEV1/FVC. DDI and DDI/VDP, a marker of defect clustering, were compared across diseases.ASSESSMENTWhole-lung mean DDI was extracted from DDI maps; correlated with VDP (percent of pixels <60% of whole-lung mean signal intensity) and pulmonary function tests (PFTs) including FEV1, FVC, and FEV1/FVC. DDI and DDI/VDP, a marker of defect clustering, were compared across diseases.Pearson correlation analysis and Kruskal-Wallis tests. P < 0.0056 for disease groups, P < 0.0125 for categories.STATISTICAL TESTSPearson correlation analysis and Kruskal-Wallis tests. P < 0.0056 for disease groups, P < 0.0125 for categories.DDI was significantly elevated in BMT (8.3 ± 11.5), BOS (30.1 ± 57.5), BPD (16.0 ± 46.8), CF (15.4 ± 27.2), and LAM (12.6 ± 34.2) compared to controls (1.8 ± 3.1). DDI correlated significantly with VDP in all groups (r ≥ 0.56) except BLEO, and with PFTs in CF, FLD, and LAM (r ≥ 0.56). Obstructive groups had significantly higher mean DDI (14.0 ± 32.0) than controls (1.8 ± 3.0) and restrictive groups (4.0 ± 12.0). DDI/VDP was significantly lower in the restrictive group (0.6 ± 0.6) than controls (0.8 ± 0.6) and obstructive group (1.0 ± 1.0).RESULTSDDI was significantly elevated in BMT (8.3 ± 11.5), BOS (30.1 ± 57.5), BPD (16.0 ± 46.8), CF (15.4 ± 27.2), and LAM (12.6 ± 34.2) compared to controls (1.8 ± 3.1). DDI correlated significantly with VDP in all groups (r ≥ 0.56) except BLEO, and with PFTs in CF, FLD, and LAM (r ≥ 0.56). Obstructive groups had significantly higher mean DDI (14.0 ± 32.0) than controls (1.8 ± 3.0) and restrictive groups (4.0 ± 12.0). DDI/VDP was significantly lower in the restrictive group (0.6 ± 0.6) than controls (0.8 ± 0.6) and obstructive group (1.0 ± 1.0).DDI may provide insights into the distribution of ventilation defects across diseases.DATA CONCLUSIONDDI may provide insights into the distribution of ventilation defects across diseases.3 TECHNICAL EFFICACY: Stage 2.EVIDENCE LEVEL3 TECHNICAL EFFICACY: Stage 2.]]></description><issn>1522-2586</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNpNjElPwzAYRC0EEqVw4Rf4yCXFSxzHR9RCW6kV-3KrXOczuHKdEDtC4ddTlgOnmZHmPYROKRlRQtj5Ztu6EVMFk3toQAVjGRNlsf-vH6KjGDeEEKVyMUAft50OydnehVd83-jktMcTF1Pr1l1ydcC1xU-wu3j9MydgwaSIXcDLzifXeMA3nd_WQbf9Nwk6QsTPLr3hWd9A29Ret-4TKkyZeoGwcyzv5sfowGof4eQvh-jx6vJhPMsW19P5-GKRNVTkKTOcS5BcCAaCa2PUuipyarQEy6gQqtSqqAS3sioF1baSZa6UMmtD5A4lJR-is19v09bvHcS02rpowHsdoO7iilOqqGIyL_gXsF9hEQ</recordid><startdate>20241022</startdate><enddate>20241022</enddate><creator>Bdaiwi, Abdullah S</creator><creator>Willmering, Matthew M</creator><creator>Woods, Jason C</creator><creator>Walkup, Laura L</creator><creator>Cleveland, Zackary I</creator><scope>7X8</scope></search><sort><creationdate>20241022</creationdate><title>Quantifying Spatial Distribution of Ventilation Defects in Multiple Pulmonary Diseases With Hyperpolarized 129Xenon MRI</title><author>Bdaiwi, Abdullah S ; Willmering, Matthew M ; Woods, Jason C ; Walkup, Laura L ; Cleveland, Zackary I</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p154t-c337e73552e53acc9bd641ca7ef215598a96d53f7d851afd784999cbc07c33083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bdaiwi, Abdullah S</creatorcontrib><creatorcontrib>Willmering, Matthew M</creatorcontrib><creatorcontrib>Woods, Jason C</creatorcontrib><creatorcontrib>Walkup, Laura L</creatorcontrib><creatorcontrib>Cleveland, Zackary I</creatorcontrib><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bdaiwi, Abdullah S</au><au>Willmering, Matthew M</au><au>Woods, Jason C</au><au>Walkup, Laura L</au><au>Cleveland, Zackary I</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantifying Spatial Distribution of Ventilation Defects in Multiple Pulmonary Diseases With Hyperpolarized 129Xenon MRI</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><date>2024-10-22</date><risdate>2024</risdate><issn>1522-2586</issn><eissn>1522-2586</eissn><abstract><![CDATA[Hyperpolarized 129Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.BACKGROUNDHyperpolarized 129Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.To quantify spatial distribution of 129Xe ventilation defects using DDI across pulmonary diseases.PURPOSETo quantify spatial distribution of 129Xe ventilation defects using DDI across pulmonary diseases.Retrospective.STUDY TYPERetrospective.Four hundred twenty-one subjects (age = 23.1 ± 17.1, female = 230), comprising healthy controls (N = 60) and subjects with obstructive conditions (asthma [N = 25], bronchiolitis obliterans syndrome [BOS, N = 18], cystic fibrosis [CF, N = 90], lymphangioleiomyomatosis [LAM, N = 50]), restrictive conditions (bleomycin-treated cancer survivors [BLEO, N = 14]; fibrotic lung diseases [FLD, N = 92]), bone marrow transplantation (BMT, N = 53), and bronchopulmonary dysplasia (BPD, N = 19).SUBJECTSFour hundred twenty-one subjects (age = 23.1 ± 17.1, female = 230), comprising healthy controls (N = 60) and subjects with obstructive conditions (asthma [N = 25], bronchiolitis obliterans syndrome [BOS, N = 18], cystic fibrosis [CF, N = 90], lymphangioleiomyomatosis [LAM, N = 50]), restrictive conditions (bleomycin-treated cancer survivors [BLEO, N = 14]; fibrotic lung diseases [FLD, N = 92]), bone marrow transplantation (BMT, N = 53), and bronchopulmonary dysplasia (BPD, N = 19).3 T, two-dimensional multi-slice gradient echo.FIELD STRENGTH/SEQUENCE3 T, two-dimensional multi-slice gradient echo.Whole-lung mean DDI was extracted from DDI maps; correlated with VDP (percent of pixels <60% of whole-lung mean signal intensity) and pulmonary function tests (PFTs) including FEV1, FVC, and FEV1/FVC. DDI and DDI/VDP, a marker of defect clustering, were compared across diseases.ASSESSMENTWhole-lung mean DDI was extracted from DDI maps; correlated with VDP (percent of pixels <60% of whole-lung mean signal intensity) and pulmonary function tests (PFTs) including FEV1, FVC, and FEV1/FVC. DDI and DDI/VDP, a marker of defect clustering, were compared across diseases.Pearson correlation analysis and Kruskal-Wallis tests. P < 0.0056 for disease groups, P < 0.0125 for categories.STATISTICAL TESTSPearson correlation analysis and Kruskal-Wallis tests. P < 0.0056 for disease groups, P < 0.0125 for categories.DDI was significantly elevated in BMT (8.3 ± 11.5), BOS (30.1 ± 57.5), BPD (16.0 ± 46.8), CF (15.4 ± 27.2), and LAM (12.6 ± 34.2) compared to controls (1.8 ± 3.1). DDI correlated significantly with VDP in all groups (r ≥ 0.56) except BLEO, and with PFTs in CF, FLD, and LAM (r ≥ 0.56). Obstructive groups had significantly higher mean DDI (14.0 ± 32.0) than controls (1.8 ± 3.0) and restrictive groups (4.0 ± 12.0). DDI/VDP was significantly lower in the restrictive group (0.6 ± 0.6) than controls (0.8 ± 0.6) and obstructive group (1.0 ± 1.0).RESULTSDDI was significantly elevated in BMT (8.3 ± 11.5), BOS (30.1 ± 57.5), BPD (16.0 ± 46.8), CF (15.4 ± 27.2), and LAM (12.6 ± 34.2) compared to controls (1.8 ± 3.1). DDI correlated significantly with VDP in all groups (r ≥ 0.56) except BLEO, and with PFTs in CF, FLD, and LAM (r ≥ 0.56). Obstructive groups had significantly higher mean DDI (14.0 ± 32.0) than controls (1.8 ± 3.0) and restrictive groups (4.0 ± 12.0). DDI/VDP was significantly lower in the restrictive group (0.6 ± 0.6) than controls (0.8 ± 0.6) and obstructive group (1.0 ± 1.0).DDI may provide insights into the distribution of ventilation defects across diseases.DATA CONCLUSIONDDI may provide insights into the distribution of ventilation defects across diseases.3 TECHNICAL EFFICACY: Stage 2.EVIDENCE LEVEL3 TECHNICAL EFFICACY: Stage 2.]]></abstract><doi>10.1002/jmri.29627</doi><oa>free_for_read</oa></addata></record>
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title Quantifying Spatial Distribution of Ventilation Defects in Multiple Pulmonary Diseases With Hyperpolarized 129Xenon MRI
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