Dynamic oxygen-enhanced magnetic resonance imaging of the lung in asthma—Initial experience

Zhang, Weijuan; Niven, Robert; Young, Simon S.; Liu, Yuzhen; Parker, Geoffrey; Naish, Josephine H.

doi:10.1016/j.ejrad.2014.10.021

Cited by 39 publications

(40 citation statements)

References 27 publications

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“…Several studies have used 2D OE‐MRI with T 1 ‐weighting obtained from inversion‐recovery fast spin echo and Look‐Locker GRE pulse sequences for patients with various pulmonary diseases and have observed differences in relative enhancement associated with disease and disease severity. These studies examined small‐volume measurements or ROIs that often covered only a modest portion of the lungs due to the extended scan times required for 3D coverage using these pulse sequences.…”

Section: Discussionmentioning

confidence: 99%

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Pulmonary ventilation imaging in asthma and cystic fibrosis using oxygen‐enhanced 3D radial ultrashort echo time MRI

Zha

Kruger

Johnson

et al. 2017

Magnetic Resonance Imaging

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Section: Discussionmentioning

confidence: 99%

“…The histogram of the PSE values in the lungs generally has a nonsymmetrical distribution, particularly in the obstructed lungs. Therefore, the median PSE (MPSE) value in the lungs was calculated and used as a global measure from UTE OE‐MRI, as reported in prior OE‐MRI studies …”

Section: Methodsmentioning

confidence: 99%

Pulmonary ventilation imaging in asthma and cystic fibrosis using oxygen‐enhanced 3D radial ultrashort echo time MRI

Zha

Kruger

Johnson

et al. 2017

Magnetic Resonance Imaging

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“…MAGNETIC RESONANCE IMAGING (MRI) of the chest can provide regional assessment of lung ventilation by inhaling oxygen (O 2 ), hyperpolarized noble gases, or fluorinated gas . Oxygen‐enhanced (OE) MRI using a 3D radial ultrashort echo time (UTE) sequence supports quantitative differentiation of diseased vs. healthy lungs using whole lung ventilation defect percent (VDP) .…”

mentioning

confidence: 99%

Deep convolutional neural networks with multiplane consensus labeling for lung function quantification using UTE proton MRI

Zha

Fain

Schiebler

et al. 2019

Magnetic Resonance Imaging

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Background Ultrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development of rapid biomarker extraction and regional quantification has lagged behind due to labor‐intensive lung segmentation. Purpose To evaluate a deep learning (DL) approach for automated lung segmentation to extract image‐based biomarkers from functional lung imaging using 3D radial UTE oxygen‐enhanced (OE) MRI. Study Type Retrospective study aimed to evaluate a technical development. Population Forty‐five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis. Field Strength/Sequence 1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence. Assessment Two 3D radial UTE volumes were acquired sequentially under normoxic (21% O2) and hyperoxic (100% O2) conditions. Automated segmentation of the lungs using 2D convolutional encoder‐decoder based DL method, and the subsequent functional quantification via adaptive K‐means were compared with the results obtained from the reference method, supervised region growing. Statistical Tests Relative to the reference method, the performance of DL on volumetric quantification was assessed using Dice coefficient with 95% confidence interval (CI) for accuracy, two‐sided Wilcoxon signed‐rank test for computation time, and Bland–Altman analysis on the functional measure derived from the OE images. Results The DL method produced strong agreement with supervised region growing for the right (Dice: 0.97; 95% CI = [0.96, 0.97]; P < 0.001) and left lungs (Dice: 0.96; 95% CI = [0.96, 0.97]; P < 0.001). The DL method averaged 46 seconds to generate the automatic segmentations in contrast to 1.93 hours using the reference method (P < 0.001). Bland–Altman analysis showed nonsignificant intermethod differences of volumetric (P ≥ 0.12) and functional measurements (P ≥ 0.34) in the left and right lungs. Data Conclusion DL provides rapid, automated, and robust lung segmentation for quantification of regional lung function using UTE proton MRI. Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1169–1181.

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“…Zhang et al [155] demonstrated that dynamic oxygen-enhanced MRI is sensitive to severity of asthma and enables assessments of regional lung function. In oxygen-weighted HP gas MRI, the gas dose is mixed with pure O 2 immediately prior to inhalation to approximate normoxic concentrations (20% O 2 ).…”

Section: Magnetic Resonance Ventilation/perfusion Imagingmentioning

confidence: 99%

Novel imaging approaches in adult asthma and their clinical potential

Hartley

Baldi

Brightling

et al. 2015

Expert Review of Clinical Immunology

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Currently, imaging in asthma is confined to chest radiography and CT. The emergence of new imaging techniques and tremendous improvement of existing imaging methods, primarily due to technological advancement, has completely changed its research and clinical prospects. In research, imaging in asthma is now being employed to provide quantitative assessment of morphology, function and pathogenic processes at the molecular level. The unique ability of imaging for non-invasive, repeated, quantitative, and in vivo assessment of structure and function in asthma could lead to identification of 'imaging biomarkers' with potential as outcome measures in future clinical trials. Emerging imaging techniques and their utility in the research and clinical setting is discussed in this review.

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Dynamic oxygen-enhanced magnetic resonance imaging of the lung in asthma—Initial experience

Abstract: Dynamic OE-MRI is feasible in asthma and sensitive to the severity of disease. The technique provides indices related to regional oxygen delivery, uptake and washout that show good one month reproducibility and intra-observer agreement.

Cited by 39 publications

References 27 publications

Pulmonary ventilation imaging in asthma and cystic fibrosis using oxygen‐enhanced 3D radial ultrashort echo time MRI

Pulmonary ventilation imaging in asthma and cystic fibrosis using oxygen‐enhanced 3D radial ultrashort echo time MRI

Deep convolutional neural networks with multiplane consensus labeling for lung function quantification using UTE proton MRI

Novel imaging approaches in adult asthma and their clinical potential

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