ArticleFrontiers in medicine2022
Unsupervised clustering algorithms improve the reproducibility of dynamic contrast-enhanced magnetic resonance imaging pulmonary perfusion quantification in muco-obstructive lung diseases.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 5 citations in OpenAlex.
- Evaluating Perfusion Quantification for Phase-Resolved Functional Lung MRI Using Gaussian Filtering.NMR in biomedicine · 2026Article
- Pathology-validated structural and physiologic habitat imaging for differentiating radiation necrosis from tumor recurrence in brain metastases.Journal of neuro-oncology · 2026Article
- Characterizing chronic obstructive pulmonary disease using quantitative MRI biomarkers.The British journal of radiology · 2026Review
- Quantitative MRI detects delayed perfusion and impact of bronchial artery dilatation on pulmonary circulation in patients with cystic fibrosis.European radiology · 2025Article
- Supervised and unsupervised learning for lung perfusion data segmentation in electrical impedance tomography.Biomedical physics & engineering express · 2025Article
- Echo time-dependent observed T1 and quantitative perfusion in chronic obstructive pulmonary disease using magnetic resonance imaging.Frontiers in medicine · 2023Article
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Authors and funding
13 authors at 4 institutions in 1 country.
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Abstract
Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows the assessment of pulmonary perfusion, which may play a key role in the development of muco-obstructive lung disease. One problem with quantifying pulmonary perfusion is the high variability of metrics. Quantifying the extent of abnormalities using unsupervised clustering algorithms in residue function maps leads to intrinsic normalization and could reduce variability. Purpose: We investigated the reproducibility of perfusion defects in percent (QDP) in clinically stable patients with cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD). Methods: 15 CF (29.3 ± 9.3y, FEV1%predicted = 66.6 ± 15.8%) and 20 COPD (66.5 ± 8.9y, FEV1%predicted = 42.0 ± 13.3%) patients underwent DCE-MRI twice 1 month apart. QDP, pulmonary blood flow (PBF), and pulmonary blood volume (PBV) were computed from residue function maps using an in-house quantification pipeline. A previously validated MRI perfusion score was visually assessed by an expert reader. Results: Overall, mean QDP, PBF, and PBV did not change within 1 month, except for QDP in COPD ( Conclusion: In patients with muco-obstructive lung diseases, QDP was more robust and showed a higher correlation with the MRI perfusion score compared to the traditionally used perfusion metrics PBF and PBV.
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