Quantification of Spatial Ventilation Defect Sparsity in Hyperpolarized Gas Magnetic Resonance Imaging of Lungs Utilizing a Three‐Dimensional Clustering Algorithm

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<sup>-2</sup>) for asthma, r = 0.79 (p = 3.15 × 10<sup>-6</sup>) for COPD, and r = 0.84 (p = 2.80 × 10<sup>-5</sup>) 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.

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Journal
NMR in Biomedicine
Published
2025-02-10
DOI
https://doi.org/10.1002/nbm.70005
Primary Topic
Atomic and Subatomic Physics Research
Type
article
Field-Weighted Citation Impact
0.00

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article

Quantification of Spatial Ventilation Defect Sparsity in Hyperpolarized Gas Magnetic Resonance Imaging of Lungs Utilizing a Three‐Dimensional Clustering Algorithm

Mia Ruppel, Talissa A. Altes, Jaime F. Mata, John P. Mugler et al.
NMR in Biomedicine
Atomic and Subatomic Physics Research
article

Quantification of Spatial Ventilation Defect Sparsity in Hyperpolarized Gas Magnetic Resonance Imaging of Lungs Utilizing a Three‐Dimensional Clustering Algorithm

Mia Ruppel, Talissa A. Altes, Jaime F. Mata, John P. Mugler, Iulian C. Ruset, Robert P. Thomen, F. W. Hersman, Eduard E. de Lange, Ummul Afia Shammi, Craig H. Meyer, Kun Qing
article en

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<sup>-2</sup>) for asthma, r = 0.79 (p = 3.15 × 10<sup>-6</sup>) for COPD, and r = 0.84 (p = 2.80 × 10<sup>-5</sup>) 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.

NMR in BiomedicineVol. 38(3)
City Of Hope National Medical Center (US), University of Illinois Chicago (US), City of Hope (US), Xemed (United States) (US), University of Virginia (US), University of Missouri (US)
GlaxoSmithKline, Vertex Pharmaceuticals, National Institutes of Health
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Openalex Percentile: Top 98%
Atomic and Subatomic Physics Research
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