Quantification of Spatial Ventilation Defect Sparsity in Hyperpolarized Gas Magnetic Resonance Imaging of Lungs Utilizing a Three‐Dimensional Clustering Algorithm
ABSTRACT Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focal...
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| Vydáno v: | NMR in biomedicine Ročník 38; číslo 3; s. e70005 - n/a |
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| Abstract | ABSTRACT
Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized‐gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean ± sd = 45.1 ± 18.9), 25 COPD subjects (age = 60.6 ± 10.4), and 17 CF subjects (age = 21.8 ± 8.4). The analyzed data are from four different studies: Study 1 used a 3‐T gradient echo (GRE) sequence, Study 2 used a 1.5‐T GRE sequence, Study 3 used a 1.5‐T two‐dimensional spiral sequence, and Study 4 used a 1.5‐T three‐dimensional balanced steady‐state free precession (bSSFP) sequence. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). The algorithm was assessed on synthesized spherical defect clusters and 3D lung volume defects of varying sizes/distributions. CI and whole‐lung VDP were calculated for asthmatic, COPD, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VDP, were performed to evaluate CI among asthma, COPD, and CF. T tests were performed to evaluate CI/VDP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VDP were r = 0.60 (p = 2.21 × 10−2) for asthma, r = 0.79 (p = 3.15 × 10−6) for COPD, and r = 0.84 (p = 2.80 × 10−5) for CF. Pearson correlation coefficients between CI and FEV1pp was r = −0.47 (p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSD) test revealed that the ratio of whole‐lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008), but not among asthma/COPD (p = 0.95). This method of volumetric quantification of defect spatial distribution may provide information regarding defect cluster size in which VDP alone is uninformative.
Hyperpolarized gas magnetic resonance imaging allows for the quantification of pulmonary defects with the ventilation defect percentage, which lacks information regarding the spatial distribution of defects. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). CI and whole‐lung VDP were calculated for asthmatic, COPD, and CF subjects. ANOVA and Tukey's HSD test revealed the ratio of whole‐lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008), but not among asthma/COPD (p = 0.95). |
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| AbstractList | Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized-gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean ± sd = 45.1 ± 18.9), 25 COPD subjects (age = 60.6 ± 10.4), and 17 CF subjects (age = 21.8 ± 8.4). The analyzed data are from four different studies: Study 1 used a 3-T gradient echo (GRE) sequence, Study 2 used a 1.5-T GRE sequence, Study 3 used a 1.5-T two-dimensional spiral sequence, and Study 4 used a 1.5-T three-dimensional balanced steady-state free precession (bSSFP) sequence. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). The algorithm was assessed on synthesized spherical defect clusters and 3D lung volume defects of varying sizes/distributions. CI and whole-lung VDP were calculated for asthmatic, COPD, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VDP, were performed to evaluate CI among asthma, COPD, and CF. T tests were performed to evaluate CI/VDP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VDP were r = 0.60 (p = 2.21 × 10
) for asthma, r = 0.79 (p = 3.15 × 10
) for COPD, and r = 0.84 (p = 2.80 × 10
) for CF. Pearson correlation coefficients between CI and FEV1pp was r = -0.47 (p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSD) test revealed that the ratio of whole-lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008), but not among asthma/COPD (p = 0.95). This method of volumetric quantification of defect spatial distribution may provide information regarding defect cluster size in which VDP alone is uninformative. Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized‐gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean ± sd = 45.1 ± 18.9), 25 COPD subjects (age = 60.6 ± 10.4), and 17 CF subjects (age = 21.8 ± 8.4). The analyzed data are from four different studies: Study 1 used a 3‐T gradient echo (GRE) sequence, Study 2 used a 1.5‐T GRE sequence, Study 3 used a 1.5‐T two‐dimensional spiral sequence, and Study 4 used a 1.5‐T three‐dimensional balanced steady‐state free precession (bSSFP) sequence. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). The algorithm was assessed on synthesized spherical defect clusters and 3D lung volume defects of varying sizes/distributions. CI and whole‐lung VDP were calculated for asthmatic, COPD, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VDP, were performed to evaluate CI among asthma, COPD, and CF. T tests were performed to evaluate CI/VDP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VDP were r = 0.60 ( p = 2.21 × 10 −2 ) for asthma, r = 0.79 ( p = 3.15 × 10 −6 ) for COPD, and r = 0.84 ( p = 2.80 × 10 −5 ) for CF. Pearson correlation coefficients between CI and FEV1pp was r = −0.47 ( p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSD) test revealed that the ratio of whole‐lung CI/VDP was significantly different between asthma/CF ( p = 0.04) and CF/COPD ( p = 0.008), but not among asthma/COPD ( p = 0.95). This method of volumetric quantification of defect spatial distribution may provide information regarding defect cluster size in which VDP alone is uninformative. ABSTRACT Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized‐gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean ± sd = 45.1 ± 18.9), 25 COPD subjects (age = 60.6 ± 10.4), and 17 CF subjects (age = 21.8 ± 8.4). The analyzed data are from four different studies: Study 1 used a 3‐T gradient echo (GRE) sequence, Study 2 used a 1.5‐T GRE sequence, Study 3 used a 1.5‐T two‐dimensional spiral sequence, and Study 4 used a 1.5‐T three‐dimensional balanced steady‐state free precession (bSSFP) sequence. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). The algorithm was assessed on synthesized spherical defect clusters and 3D lung volume defects of varying sizes/distributions. CI and whole‐lung VDP were calculated for asthmatic, COPD, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VDP, were performed to evaluate CI among asthma, COPD, and CF. T tests were performed to evaluate CI/VDP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VDP were r = 0.60 (p = 2.21 × 10−2) for asthma, r = 0.79 (p = 3.15 × 10−6) for COPD, and r = 0.84 (p = 2.80 × 10−5) for CF. Pearson correlation coefficients between CI and FEV1pp was r = −0.47 (p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSD) test revealed that the ratio of whole‐lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008), but not among asthma/COPD (p = 0.95). This method of volumetric quantification of defect spatial distribution may provide information regarding defect cluster size in which VDP alone is uninformative. Hyperpolarized gas magnetic resonance imaging allows for the quantification of pulmonary defects with the ventilation defect percentage, which lacks information regarding the spatial distribution of defects. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). CI and whole‐lung VDP were calculated for asthmatic, COPD, and CF subjects. ANOVA and Tukey's HSD test revealed the ratio of whole‐lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008), but not among asthma/COPD (p = 0.95). Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized-gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean ± sd = 45.1 ± 18.9), 25 COPD subjects (age = 60.6 ± 10.4), and 17 CF subjects (age = 21.8 ± 8.4). The analyzed data are from four different studies: Study 1 used a 3-T gradient echo (GRE) sequence, Study 2 used a 1.5-T GRE sequence, Study 3 used a 1.5-T two-dimensional spiral sequence, and Study 4 used a 1.5-T three-dimensional balanced steady-state free precession (bSSFP) sequence. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). The algorithm was assessed on synthesized spherical defect clusters and 3D lung volume defects of varying sizes/distributions. CI and whole-lung VDP were calculated for asthmatic, COPD, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VDP, were performed to evaluate CI among asthma, COPD, and CF. T tests were performed to evaluate CI/VDP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VDP were r = 0.60 (p = 2.21 × 10-2) for asthma, r = 0.79 (p = 3.15 × 10-6) for COPD, and r = 0.84 (p = 2.80 × 10-5) for CF. Pearson correlation coefficients between CI and FEV1pp was r = -0.47 (p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSD) test revealed that the ratio of whole-lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008), but not among asthma/COPD (p = 0.95). This method of volumetric quantification of defect spatial distribution may provide information regarding defect cluster size in which VDP alone is uninformative.Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized-gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean ± sd = 45.1 ± 18.9), 25 COPD subjects (age = 60.6 ± 10.4), and 17 CF subjects (age = 21.8 ± 8.4). The analyzed data are from four different studies: Study 1 used a 3-T gradient echo (GRE) sequence, Study 2 used a 1.5-T GRE sequence, Study 3 used a 1.5-T two-dimensional spiral sequence, and Study 4 used a 1.5-T three-dimensional balanced steady-state free precession (bSSFP) sequence. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). The algorithm was assessed on synthesized spherical defect clusters and 3D lung volume defects of varying sizes/distributions. CI and whole-lung VDP were calculated for asthmatic, COPD, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VDP, were performed to evaluate CI among asthma, COPD, and CF. T tests were performed to evaluate CI/VDP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VDP were r = 0.60 (p = 2.21 × 10-2) for asthma, r = 0.79 (p = 3.15 × 10-6) for COPD, and r = 0.84 (p = 2.80 × 10-5) for CF. Pearson correlation coefficients between CI and FEV1pp was r = -0.47 (p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSD) test revealed that the ratio of whole-lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008), but not among asthma/COPD (p = 0.95). This method of volumetric quantification of defect spatial distribution may provide information regarding defect cluster size in which VDP alone is uninformative. Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized‐gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean ± sd = 45.1 ± 18.9), 25 COPD subjects (age = 60.6 ± 10.4), and 17 CF subjects (age = 21.8 ± 8.4). The analyzed data are from four different studies: Study 1 used a 3‐T gradient echo (GRE) sequence, Study 2 used a 1.5‐T GRE sequence, Study 3 used a 1.5‐T two‐dimensional spiral sequence, and Study 4 used a 1.5‐T three‐dimensional balanced steady‐state free precession (bSSFP) sequence. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). The algorithm was assessed on synthesized spherical defect clusters and 3D lung volume defects of varying sizes/distributions. CI and whole‐lung VDP were calculated for asthmatic, COPD, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VDP, were performed to evaluate CI among asthma, COPD, and CF. T tests were performed to evaluate CI/VDP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VDP were r = 0.60 (p = 2.21 × 10−2) for asthma, r = 0.79 (p = 3.15 × 10−6) for COPD, and r = 0.84 (p = 2.80 × 10−5) for CF. Pearson correlation coefficients between CI and FEV1pp was r = −0.47 (p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSD) test revealed that the ratio of whole‐lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008), but not among asthma/COPD (p = 0.95). This method of volumetric quantification of defect spatial distribution may provide information regarding defect cluster size in which VDP alone is uninformative. |
| Author | Shammi, Ummul Afia Qing, Kun Thomen, Robert P. Hersman, F. W. García Delgado, Gabriela María Ruppel, Mia R. Meyer, Craig H. Lange, Eduard E. Altes, Talissa A. Mugler, John P. Ruset, Iulian C. Mata, Jaime |
| Author_xml | – sequence: 1 givenname: Gabriela María orcidid: 0009-0003-6949-6454 surname: García Delgado fullname: García Delgado, Gabriela María email: gmg9r2@umsystem.edu organization: University of Missouri – sequence: 2 givenname: Ummul Afia surname: Shammi fullname: Shammi, Ummul Afia organization: University of Illinois – sequence: 3 givenname: Mia R. surname: Ruppel fullname: Ruppel, Mia R. organization: University of Missouri – sequence: 4 givenname: Talissa A. surname: Altes fullname: Altes, Talissa A. organization: University of Missouri – sequence: 5 givenname: John P. surname: Mugler fullname: Mugler, John P. organization: University of Virginia – sequence: 6 givenname: Craig H. surname: Meyer fullname: Meyer, Craig H. organization: University of Virginia – sequence: 7 givenname: Kun surname: Qing fullname: Qing, Kun organization: City of Hope – sequence: 8 givenname: Eduard E. surname: Lange fullname: Lange, Eduard E. organization: University of Virginia – sequence: 9 givenname: Jaime surname: Mata fullname: Mata, Jaime organization: University of Virginia – sequence: 10 givenname: Iulian C. surname: Ruset fullname: Ruset, Iulian C. organization: Xemed LLC – sequence: 11 givenname: F. W. surname: Hersman fullname: Hersman, F. W. organization: Xemed LLC – sequence: 12 givenname: Robert P. surname: Thomen fullname: Thomen, Robert P. email: thomenr@health.missouri.edu organization: University of Missouri |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39930612$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.jcf.2016.12.004 10.1016/j.jaci.2020.11.010 10.1002/mrm.10173 10.1016/j.acra.2021.01.007 10.1016/j.mri.2012.05.001 10.1016/j.acra.2021.06.017 10.1016/j.jaci.2020.02.029 10.3390/molecules28031038 10.2307/1170601 10.1152/japplphysiol.01206.2012 10.1148/radiol.14140080 10.1038/s41598‐023‐40950‐8 10.1016/S0720‐048X(01)00347‐3 10.1148/radiol.12120485 10.1002/mrm.28947 10.1002/jmri.25992 10.1002/mrm.28985 10.1016/j.jcf.2016.07.008 10.1016/j.jcf.2022.12.012 10.1016/j.mric.2015.01.003 10.1109/TMI.2010.2046908 10.1183/13993003.00821‐2018 |
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| Keywords | hyperpolarized MRI defects ventilation gas lungs clustering algorithm |
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| References | 2015; 23 2002; 47 2023; 13 2021; 86 2012; 265 2023; 22 2023; 28 2010; 29 2017; 16 2021; 147 2015; 274 2013; 114 2017 2018; 52 2020; 146 2001; 40 2018; 48 2022; 29 2012; 30 1998; 68 e_1_2_10_12_1 e_1_2_10_23_1 e_1_2_10_9_1 e_1_2_10_13_1 e_1_2_10_24_1 e_1_2_10_10_1 e_1_2_10_21_1 e_1_2_10_11_1 e_1_2_10_22_1 e_1_2_10_20_1 e_1_2_10_2_1 e_1_2_10_4_1 e_1_2_10_18_1 e_1_2_10_3_1 e_1_2_10_19_1 e_1_2_10_6_1 e_1_2_10_16_1 e_1_2_10_5_1 e_1_2_10_17_1 e_1_2_10_8_1 e_1_2_10_14_1 e_1_2_10_25_1 e_1_2_10_7_1 e_1_2_10_15_1 |
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| Snippet | ABSTRACT
Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage... Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP).... |
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| SubjectTerms | Adult Age Aged algorithm Algorithms Asthma Asthma - diagnostic imaging Asthma - physiopathology Chronic obstructive pulmonary disease Cluster Analysis Clustering Clustering Algorithms Correlation coefficient Correlation coefficients Defects Female gas Helium Humans hyperpolarized Imaging, Three-Dimensional Lung - diagnostic imaging Lung - physiopathology Lung diseases lungs Magnetic Resonance Imaging Male Middle Aged MRI Pulmonary Disease, Chronic Obstructive - diagnostic imaging Pulmonary Disease, Chronic Obstructive - physiopathology Pulmonary Ventilation Regression analysis Regression models Spatial distribution Statistical analysis Variance analysis Ventilation Young Adult |
| Title | Quantification of Spatial Ventilation Defect Sparsity in Hyperpolarized Gas Magnetic Resonance Imaging of Lungs Utilizing a Three‐Dimensional Clustering Algorithm |
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