Journal article
Quantification of lung ventilation defects on hyperpolarized MRI: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD study
Magnetic resonance imaging, Vol.92, pp.140-149
10/01/2022
DOI: 10.1016/j.mri.2022.06.016
PMCID: PMC9957614
PMID: 35777684
Abstract
Purpose: To develop an end-to-end deep learning (DL) framework to segment ventilation defects on pulmonary hyperpolarized MRI. Materials and methods: The Multi-Ethnic Study of Atherosclerosis Chronic Obstructive Pulmonary Disease (COPD) study is a nested longitudinal case-control study in older smokers. Between February 2016 and July 2017, 56 participants (age, mean +/- SD, 74 +/- 8 years; 34 men) underwent same breath-hold proton (1H) and helium (3He) MRI, which were annotated for non-ventilated, hypo-ventilated, and normal-ventilated lungs. In this retrospective DL study, 820 1H and 3He slices from 42/56 (75%) participants were randomly selected for training, with the remaining 14/56 (25%) for test. Full lung masks were segmented using a traditional U-Net on 1H MRI and were imported into a cascaded U-Net, which were used to segment ventilation defects on 3He MRI. Models were trained with conventional data augmentation (DA) and generative adversarial networks (GAN)-DA. Results: Conventional-DA improved 1H and 3He MRI segmentation over the non-DA model (P = 0.007 to 0.03) but GAN-DA did not yield further improvement. The cascaded U-Net improved non-ventilated lung segmentation (P < 0.005). Dice similarity coefficients (DSC) between manually and DL-segmented full lung, non-ventilated, hypo-ventilated, and normal-ventilated regions were 0.965 +/- 0.010, 0.840 +/- 0.057, 0.715 +/- 0.175, and 0.883 +/- 0.060, respectively. We observed no statistically significant difference in DCSs between participants with and without COPD (P = 0.41, 0.06, and 0.18 for non-ventilated, hypo-ventilated, and normal-ventilated regions, respectively).
Details
- Title: Subtitle
- Quantification of lung ventilation defects on hyperpolarized MRI: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD study
- Creators
- Xuzhe Zhang - Columbia UniversityElsa D. Angelini - Department of Biomedical Engineering, Columbia University, New York, NY, USA; NIHR Imperial BRC, ITMAT Data Science Group, Department of Metabolism, Digestion and Reproduction, Imperial College, London, UK.Fateme S. Haghpanah - University of TorontoAndrew F. Laine - Columbia UniversityYanping Sun - Columbia University Medical CenterGrant T. Hiura - Columbia University Medical CenterStephen M. Dashnaw - Columbia University Medical CenterMartin R. Prince - Cornell UniversityEric A. Hoffman - University of IowaBharath Ambale-Venkatesh - Johns Hopkins MedicineJoao A. Lima - Johns Hopkins MedicineJim M. Wild - University of SheffieldEmlyn W. Hughes - Columbia UniversityR. Graham Barr - Columbia University Irving Medical CenterWei Shen - Columbia University
- Resource Type
- Journal article
- Publication Details
- Magnetic resonance imaging, Vol.92, pp.140-149
- DOI
- 10.1016/j.mri.2022.06.016
- PMID
- 35777684
- PMCID
- PMC9957614
- NLM abbreviation
- Magn Reson Imaging
- ISSN
- 0730-725X
- eISSN
- 1873-5894
- Publisher
- Elsevier
- Number of pages
- 10
- Grant note
- 75N92020D00001; HHSN268201500003I; N01-HC-95169 / National Center for Advancing Translational Sciences (NCATS); United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Center for Advancing Translational Sciences (NCATS) R01-HL093081; R01-HL077612; R01-HL121270 / National Heart, Lung, and Blood Institute; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI) R01-HL093081 / Medical Research Council; UK Research & Innovation (UKRI); Medical Research Council UK (MRC) UL1-TR-000040; UL1-TR-001079; UL1-TR-001420 / National Institute of Diabetes and Digestive and Kidney Diseases; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Diabetes & Digestive & Kidney Diseases (NIDDK)
- Language
- English
- Date published
- 10/01/2022
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
- Record Identifier
- 9984318822602771
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