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% O 2 ) and hyperoxic (100% O 2 ) 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.
Bibliographical noteFunding Information:
Contract grant sponsor: NIH/NCATS; Contract grant number: UL1TR000427; Contract grant sponsor: NIH/NHLBI; Contract grant number: U10 HL109168; Contract grant sponsor: ICTR; Contract grant numbers: NIH R01AR068373, NIH/NHLBI, R01 HL126771; Contract grant sponsor: Research and Development Fund from the Departments of Radiology and Medical Physics, University of Wisconsin-Madison. The authors thank Robert V. Cadman, PhD, who administered the gas and facemask supplies and advised on the experimental setup; the nurses and recruiters who supported this work, especially Jan Yakey, RN, and Gina Crisafi, BS; and the research technologists at University of Wisconsin-Madison, including Kelli HellenBrand, RT, Jenelle Fuller, RT, and Sara John, RT, who supported the MRI scanning.
© 2019 International Society for Magnetic Resonance in Medicine
- cystic fibrosis
- deep learning
- magnetic resonance imaging
PubMed: MeSH publication types
- Journal Article