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European Radiology

, Volume 29, Issue 3, pp 1595–1606 | Cite as

Automated MR-based lung volume segmentation in population-based whole-body MR imaging: correlation with clinical characteristics, pulmonary function testing and obstructive lung disease

  • Jan Mueller
  • Stefan Karrasch
  • Roberto Lorbeer
  • Tatyana Ivanovska
  • Andreas Pomschar
  • Wolfgang G. Kunz
  • Ricarda von Krüchten
  • Annette Peters
  • Fabian Bamberg
  • Holger Schulz
  • Christopher L. SchlettEmail author
Chest

Abstract

Objectives

Whole-body MR imaging is increasingly utilised; although for lung dedicated sequences are often not included, the chest is typically imaged. Our objective was to determine the clinical utility of lung volumes derived from non-dedicated MRI sequences in the population-based KORA-FF4 cohort study.

Methods

400 subjects (56.4 ± 9.2 years, 57.6% males) underwent whole-body MRI including a coronal T1-DIXON-VIBE sequence in inspiration breath-hold, originally acquired for fat quantification. Based on MRI, lung volumes were derived using an automated framework and related to common predictors, pulmonary function tests (PFT; spirometry and pulmonary gas exchange, n = 214) and obstructive lung disease.

Results

MRI-based lung volume was 4.0 ± 1.1 L, which was 64.8 ± 14.9% of predicted total lung capacity (TLC) and 124.4 ± 27.9% of functional residual capacity. In multivariate analysis, it was positively associated with age, male, current smoking and height. Among PFT indices, MRI-based lung volume correlated best with TLC, alveolar volume and residual volume (RV; r = 0.57 each), while it was negatively correlated to FEV1/FVC (r = 0.36) and transfer factor for carbon monoxide (r = 0.16). Combining the strongest PFT parameters, RV and FEV1/FVC remained independently and incrementally associated with MRI-based lung volume (β = 0.50, p = 0.04 and β = – 0.02, p = 0.02, respectively) explaining 32% of the variability. For the identification of subjects with obstructive lung disease, height-indexed MRI-based lung volume yielded an AUC of 0.673–0.654.

Conclusion

Lung volume derived from non-dedicated whole-body MRI is independently associated with RV and FEV1/FVC. Furthermore, its moderate accuracy for obstructive lung disease indicates that it may be a promising tool to assess pulmonary health in whole-body imaging when PFT is not available.

Key Points

• Although whole-body MRI often does not include dedicated lung sequences, lung volume can be automatically derived using dedicated segmentation algorithms

• Lung volume derived from whole-body MRI correlates with typical predictors and risk factors of respiratory function including smoking and represents about 65% of total lung capacity and 125% of the functional residual capacity

• Lung volume derived from whole-body MRI is independently associated with residual volume and the ratio of forced expiratory volume in 1 s to forced vital capacity and may allow detection of obstructive lung disease

Keywords

Magnetic resonance imaging Whole-body imaging Computer-assisted image analysis Pulmonary function test Obstructive lung disease 

Abbreviations

AUC

Area under the curve

BMI

Body mass index

BSA

Body surface area

CAT

COPD assessment tool

COPD

Chronic obstructive lung disease

CT

Computed tomography

FEF25–75

Forced expiratory flow 25–75%

FEV1

Forced expiratory volume in 1 s

FRC

Functional reserve capacity

FVC

Forced vital capacity

MRI

Magnetic resonance imaging

PFT

Pulmonary function testing

ROC

Receiver operating characteristic

RV

Residual volume

TLC

Total lung capacity

TLCO

Transfer factor of the lung for carbon monoxide

VA

Alveolar volume

Notes

Funding

This study was funded by German Research Foundation (Bonn, Germany) grant BA 4233/4 1 and SCHL 2174/1-2, and German Centre for Cardiovascular Disease Research (Berlin, Germany) grants 81X2600209 and 81X2600214.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Christopher L. Schlett, MD MPH.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional review board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Bamberg et al Subclinical disease burden as assessed by whole-body MRI in subjects with prediabetes, subjects with diabetes, and normal control subjects from the general population: the KORA-MRI study. Diabetes. 2017 Jan;66(1):158–169.

Methodology

• prospective

• cross-sectional study/observational

• performed at one institution

Supplementary material

330_2018_5659_MOESM1_ESM.docx (241 kb)
ESM 1 (DOCX 240 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Jan Mueller
    • 1
  • Stefan Karrasch
    • 2
    • 3
    • 4
  • Roberto Lorbeer
    • 5
  • Tatyana Ivanovska
    • 6
  • Andreas Pomschar
    • 5
  • Wolfgang G. Kunz
    • 5
  • Ricarda von Krüchten
    • 1
    • 7
  • Annette Peters
    • 2
    • 8
    • 9
  • Fabian Bamberg
    • 10
  • Holger Schulz
    • 2
    • 3
  • Christopher L. Schlett
    • 1
    • 7
    Email author
  1. 1.Department of Diagnostic and Interventional RadiologyUniversity Hospital HeidelbergHeidelbergGermany
  2. 2.Institute of Epidemiology, Helmholtz Zentrum MünchenGerman Research Center for Environmental HealthNeuherbergGermany
  3. 3.Comprehensive Pneumology Center Munich (CPC-M)Member of the German Center for Lung ResearchMunichGermany
  4. 4.Institute and Outpatient Clinic for Occupational, Social and Environmental MedicineLudwig-Maximilians-UniversitätMunichGermany
  5. 5.Department of RadiologyUniversity Hospital, LMU MunichMunichGermany
  6. 6.Department of Computational Neuroscience, Computer VisionGeorg-August-UniversityGottingenGermany
  7. 7.Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL)HeidelbergGermany
  8. 8.Institute for Cardiovascular PreventionLudwig-Maximilian-University-HospitalMunichGermany
  9. 9.German Center for Cardiovascular Disease Research (DZHK e.V.)Partnersite MunichMunichGermany
  10. 10.Department of Diagnostic and Interventional RadiologyUniversity of TuebingenTuebingenGermany

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