ArticleJournal of magnetic resonance imaging : JMRI2019
Deep convolutional neural networks with multiplane consensus labeling for lung function quantification using UTE proton MRI.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- 3D Automated Segmentation of Bronchial Abnormalities on Ultrashort Echo Time MRI: A Quantitative MR Outcome in Cystic Fibrosis.Journal of magnetic resonance imaging : JMRI · 2026Article
- Automated lung segmentation on chest MRI in children with cystic fibrosis.Frontiers in medicine · 2024Article
- Automatic lung segmentation of magnetic resonance images: A new approach applied to healthy volunteers undergoing enhanced Deep-Inspiration-Breath-Hold for motion-mitigated 4D proton therapy of lung tumors.Physics and imaging in radiation oncology · 2024Article
- Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease.Radiology. Artificial intelligence · 2023Article
- Combining neural networks and image synthesis to enable automatic thoracic cavity segmentation of hyperpolarizedMagnetic resonance imaging · 2023Article
- Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.Journal of magnetic resonance imaging : JMRI · 2023Article
- A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI.Journal of magnetic resonance imaging : JMRI · 2023Article
- Quantification of lung ventilation defects on hyperpolarized MRI: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD study.Magnetic resonance imaging · 2022Article
- MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets.Magnetic resonance in medicine · 2022Article
- Deep learning in structural and functional lung image analysis.The British journal of radiology · 2022Review
- Artificial intelligence in functional imaging of the lung.The British journal of radiology · 2022Review
- Deep Learning for the Automatic Quantification of Pleural Plaques in Asbestos-Exposed Subjects.International journal of environmental research and public health · 2022Article
- Semiautomatic assessment of respiratory dynamics using cine MRI in chronic obstructive pulmonary disease.European journal of radiology open · 2022Article
- Preclinical MRI to Quantify Pulmonary Disease Severity and Trajectories in Poorly Characterized Mouse Models: A Pedagogical Example Using Data from Novel Transgenic Models of Lung Fibrosis.Journal of magnetic resonance open · 2021Article
- Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times.BMC medical imaging · 2021Article
- Novel imaging techniques for cystic fibrosis lung disease.Pediatric pulmonology · 2021Review
- SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction.Magnetic resonance in medicine · 2019Article
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6 authors.
Funding
Abstract
backgroundUltrashort 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% O 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.
resultsThe 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
conclusionDL 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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