Abdullah S Bdaiwi, Matthew M Willmering, Jason C Woods, Laura L Walkup, Zackary I Cleveland
{"title":"用超极化 129Xenon 磁共振成像量化多种肺部疾病中通气缺陷的空间分布。","authors":"Abdullah S Bdaiwi, Matthew M Willmering, Jason C Woods, Laura L Walkup, Zackary I Cleveland","doi":"10.1002/jmri.29627","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Hyperpolarized <sup>129</sup>Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.</p><p><strong>Purpose: </strong>To quantify spatial distribution of <sup>129</sup>Xe ventilation defects using DDI across pulmonary diseases.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Subjects: </strong>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).</p><p><strong>Field strength/sequence: </strong>3 T, two-dimensional multi-slice gradient echo.</p><p><strong>Assessment: </strong>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 FEV<sub>1</sub>, FVC, and FEV<sub>1</sub>/FVC. DDI and DDI/VDP, a marker of defect clustering, were compared across diseases.</p><p><strong>Statistical tests: </strong>Pearson correlation analysis and Kruskal-Wallis tests. P < 0.0056 for disease groups, P < 0.0125 for categories.</p><p><strong>Results: </strong>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).</p><p><strong>Data conclusion: </strong>DDI may provide insights into the distribution of ventilation defects across diseases.</p><p><strong>Evidence level: </strong>3 TECHNICAL EFFICACY: Stage 2.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantifying Spatial Distribution of Ventilation Defects in Multiple Pulmonary Diseases With Hyperpolarized <sup>129</sup>Xenon MRI.\",\"authors\":\"Abdullah S Bdaiwi, Matthew M Willmering, Jason C Woods, Laura L Walkup, Zackary I Cleveland\",\"doi\":\"10.1002/jmri.29627\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Hyperpolarized <sup>129</sup>Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.</p><p><strong>Purpose: </strong>To quantify spatial distribution of <sup>129</sup>Xe ventilation defects using DDI across pulmonary diseases.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Subjects: </strong>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).</p><p><strong>Field strength/sequence: </strong>3 T, two-dimensional multi-slice gradient echo.</p><p><strong>Assessment: </strong>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 FEV<sub>1</sub>, FVC, and FEV<sub>1</sub>/FVC. DDI and DDI/VDP, a marker of defect clustering, were compared across diseases.</p><p><strong>Statistical tests: </strong>Pearson correlation analysis and Kruskal-Wallis tests. P < 0.0056 for disease groups, P < 0.0125 for categories.</p><p><strong>Results: </strong>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).</p><p><strong>Data conclusion: </strong>DDI may provide insights into the distribution of ventilation defects across diseases.</p><p><strong>Evidence level: </strong>3 TECHNICAL EFFICACY: Stage 2.</p>\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/jmri.29627\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/jmri.29627","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Quantifying Spatial Distribution of Ventilation Defects in Multiple Pulmonary Diseases With Hyperpolarized 129Xenon MRI.
Background: Hyperpolarized 129Xe MRI assesses lung ventilation, often using the ventilation defect percentage (VDP). Unlike VDP, defect distribution index (DDI) quantifies spatial clustering of defects.
Purpose: To quantify spatial distribution of 129Xe ventilation defects using DDI across pulmonary diseases.
Study type: Retrospective.
Subjects: 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).
Field strength/sequence: 3 T, two-dimensional multi-slice gradient echo.
Assessment: 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.
Statistical tests: Pearson correlation analysis and Kruskal-Wallis tests. P < 0.0056 for disease groups, P < 0.0125 for categories.
Results: 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).
Data conclusion: DDI may provide insights into the distribution of ventilation defects across diseases.
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