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27.08.2018 | Chest

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

verfasst von: Jan Mueller, Stefan Karrasch, Roberto Lorbeer, Tatyana Ivanovska, Andreas Pomschar, Wolfgang G. Kunz, Ricarda von Krüchten, Annette Peters, Fabian Bamberg, Holger Schulz, Christopher L. Schlett

Erschienen in: European Radiology | Ausgabe 3/2019

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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

Nur mit Berechtigung zugänglich

GBD 2015 Chronic Respiratory Disease Collaborators (2017) Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Respir Med 5:691–706CrossRef

Ford ES, Murphy LB, Khavjou O, Giles WH, Holt JB, Croft JB (2015) Total and state-specific medical and absenteeism costs of COPD among adults aged >/= 18 years in the United States for 2010 and projections through 2020. Chest 147:31–45CrossRefPubMed

Jenkins C (2008) COPD management. Part I. Strategies for managing the burden of established COPD. Int J Tuberc Lung Dis 12:586–594PubMed

Sundblad BM, Larsson K, Nathell L (2008) Low awareness of COPD among physicians. Clin Respir J 2:11–16CrossRefPubMed

Sims EJ, Price D (2012) Spirometry: an essential tool for screening, case-finding, and diagnosis of COPD. Prim Care Respir J 21:128–130CrossRefPubMedPubMedCentral

Vogelmeier CF, Criner GJ, Martinez FJ et al (2017) Global strategy for the diagnosis, management and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary. Respirology 22:575–601CrossRefPubMed

Mall MA, Stahl M, Graeber SY, Sommerburg O, Kauczor HU, Wielpütz MO (2016) Early detection and sensitive monitoring of CF lung disease: prospects of improved and safer imaging. Pediatr Pulmonol 51:S49–S60CrossRefPubMed

Ekinci A, Yücel Uçarkuş T, Okur A, Öztürk M, Doğan S (2017) MRI of pneumonia in immunocompromised patients: comparison with CT. Diagn Interv Radiol 23:22–28

Gierada DS, Hakimian S, Slone RM, Yusen RD (1998) MR analysis of lung volume and thoracic dimensions in patients with emphysema before and after lung volume reduction surgery. AJR Am J Roentgenol 170:707–714CrossRefPubMed

10.

Iwasawa T, Kagei S, Gotoh T et al (2002) Magnetic resonance analysis of abnormal diaphragmatic motion in patients with emphysema. Eur Respir J 19:225–231CrossRefPubMed

11.

Suga K, Motoyama K, Hara A et al (1999) Respiratory failure and pulmonary hypertension associated with Klippel-Feil syndrome. Ann Nucl Med 13:441–446CrossRefPubMed

12.

Suga K, Tsukuda T, Awaya H, Matsunaga N, Sugi K, Esato K (2000) Interactions of regional respiratory mechanics and pulmonary ventilatory impairment in pulmonary emphysema: assessment with dynamic MRI and xenon-133 single-photon emission CT. Chest 117:1646–1655CrossRefPubMed

13.

Matin TN, Rahman N, Nickol AH et al (2017) Chronic obstructive pulmonary disease: lobar analysis with hyperpolarized (129)Xe MR imaging. Radiology 282:857–868CrossRefPubMed

14.

Lecouvet FE, Michoux N, Nzeusseu Toukap A et al (2015) The increasing spectrum of indications of whole-body MRI beyond oncology: imaging answers to clinical needs. Semin Musculoskelet Radiol 19:348–362CrossRefPubMed

15.

Adams HJ, Kwee TC, Vermoolen MA et al (2013) Whole-body MRI for the detection of bone marrow involvement in lymphoma: prospective study in 116 patients and comparison with FDG-PET. Eur Radiol 23:2271–2278CrossRefPubMed

16.

Hillengass J, Weber MA, Kilk K et al (2014) Prognostic significance of whole-body MRI in patients with monoclonal gammopathy of undetermined significance. Leukemia 28:174–178CrossRefPubMed

17.

Lecouvet FE (2016) Whole-body MR imaging: musculoskeletal applications. Radiology 279:345–365CrossRefPubMed

18.

Guimarães MD, Noschang J, Teixeira SR et al (2017) Whole-body MRI in pediatric patients with cancer. Cancer Imaging 17:6CrossRefPubMedPubMedCentral

19.

Ahlawat S, Fayad LM, Khan MS et al (2016) Current whole-body MRI applications in the neurofibromatoses: NF1, NF2, and schwannomatosis. Neurology 87:S31–S39CrossRefPubMedPubMedCentral

20.

Ladd SC (2009) Whole-body MRI as a screening tool? Eur J Radiol 70:452–462CrossRefPubMed

21.

Schlett CL, Hendel T, Weckbach S et al (2016) Population-based imaging and radiomics: rationale and perspective of the german national cohort MRI study. Rofo 188:652–661CrossRefPubMed

22.

Bamberg F, Kauczor HU, Weckbach S et al (2015) Whole-body MR imaging in the German national cohort: rationale, design, and technical background. Radiology 277:206–220CrossRefPubMed

23.

Sudlow C, Gallacher J, Allen N et al (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12:e1001779CrossRefPubMedPubMedCentral

24.

Zhang G, Fu J, Zhang Y et al (2015) Lung volume change after pedicle subtraction osteotomy in patients with ankylosing spondylitis with thoracolumbar kyphosis. Spine (Phila Pa 1976) 40:233–237CrossRef

25.

Holle R, Happich M, Löwel H, Wichmann HE; MONICA/KORA Study Group (2005) KORA–a research platform for population based health research. Gesundheitswesen 67(Suppl 1):S19–S25

26.

Bamberg F, Hetterich H, Rospleszcz S et al (2017) 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 66:158–169CrossRefPubMed

27.

Jones PW, Harding G, Berry P, Wiklund I, Chen WH, Kline Leidy N (2009) Development and first validation of the COPD assessment test. Eur Respir J 34:648–654CrossRefPubMed

28.

Ivanovska T, Hegenscheid K, Laqua R et al (2012) A fast and accurate automatic lung segmentation and volumetry method for MR data used in epidemiological studies. Comput Med Imaging Graph 36:281–293CrossRefPubMed

29.

Miller MR, Hankinson J, Brusasco V et al (2005) Standardisation of spirometry. Eur Respir J 26:319–338CrossRefPubMed

30.

Macintyre N, Crapo RO, Viegi G et al (2005) Standardisation of the single-breath determination of carbon monoxide uptake in the lung. Eur Respir J 26:720–735CrossRefPubMed

31.

Quanjer PH, Tammeling GJ, Cotes JE, Pedersen OF, Peslin R, Yernault JC (1993) Lung volumes and forced ventilatory flows. Eur Respir J 6(Suppl 16):5–40CrossRefPubMed

32.

Miller MR, Crapo R, Hankinson J et al (2005) General considerations for lung function testing. Eur Respir J 26:153–161CrossRefPubMed

33.

Graham BL, Brusasco V, Burgos F et al (2017) 2017 ERS/ATS standards for single-breath carbon monoxide uptake in the lung. Eur Respir J 49

34.

Quanjer PH, Stanojevic S, Cole TJ et al (2012) Multi-ethnic reference values for spirometry for the 3–95-yr age range: the global lung function 2012 equations. Eur Respir J 40:1324–1343CrossRefPubMedPubMedCentral

35.

Rennard SI, Farmer SG (2002) COPD in 2001: a major challenge for medicine, the pharmaceutical industry, and society. Chest 121:113S–115SCrossRefPubMed

36.

May SM, Li JT (2015) Burden of chronic obstructive pulmonary disease: healthcare costs and beyond. Allergy Asthma Proc 36:4–10CrossRefPubMedPubMedCentral

37.

Ferguson GT, Petty TL (1998) Screening and early intervention for COPD. Hosp Pract (1995) 33:67–72 79-80, 83; discussion 83-64CrossRef

38.

Rabe KF, Watz H (2017) Chronic obstructive pulmonary disease. Lancet 389:1931–1940CrossRefPubMed

39.

Wild JM, Marshall H, Bock M et al (2012) MRI of the lung (1/3): methods. Insights Imaging 3:345–353CrossRefPubMedPubMedCentral

40.

Biederer J, Beer M, Hirsch W et al (2012) MRI of the lung (2/3). Why ... when ... how? Insights Imaging 3:355–371CrossRefPubMedPubMedCentral

41.

Kauczor HU, Heussel CP, Fischer B, Klamm R, Mildenberger P, Thelen M (1998) Assessment of lung volumes using helical CT at inspiration and expiration: comparison with pulmonary function tests. AJR Am J Roentgenol 171:1091–1095CrossRefPubMed

42.

Haas M, Hamm B, Niehues SM (2014) Automated lung volumetry from routine thoracic CT scans: how reliable is the result? Acad Radiol 21:633–638CrossRefPubMed

43.

Shin TR, Oh YM, Park JH et al (2015) The prognostic value of residual volume/total lung capacity in patients with chronic obstructive pulmonary disease. J Korean Med Sci 30:1459–1465CrossRefPubMedPubMedCentral

44.

Diaz S, Casselbrant I, Piitulainen E et al (2009) Validity of apparent diffusion coefficient hyperpolarized 3He-MRI using MSCT and pulmonary function tests as references. Eur J Radiol 71:257–263CrossRefPubMed

45.

Hogg JC, Timens W (2009) The pathology of chronic obstructive pulmonary disease. Annu Rev Pathol 4:435–459CrossRefPubMed

46.

Miniati M, Bottai M, Pavlickova I, Monti S (2016) Body height as risk factor for emphysema in COPD. Sci Rep 6:36896CrossRefPubMedPubMedCentral

47.

Wei YF, Tsai YH, Wang CC, Kuo PH (2017) Impact of overweight and obesity on acute exacerbations of COPD - subgroup analysis of the Taiwan obstructive lung disease cohort. Int J Chron Obstruct Pulmon Dis 12:2723–2729CrossRefPubMedPubMedCentral

48.

Guerra S, Sherrill DL, Bobadilla A, Martinez FD, Barbee RA (2002) The relation of body mass index to asthma, chronic bronchitis, and emphysema. Chest 122:1256–1263CrossRefPubMed

Titel: Automated MR-based lung volume segmentation in population-based whole-body MR imaging: correlation with clinical characteristics, pulmonary function testing and obstructive lung disease
verfasst von: Jan Mueller
Stefan Karrasch
Roberto Lorbeer
Tatyana Ivanovska
Andreas Pomschar
Wolfgang G. Kunz
Ricarda von Krüchten
Annette Peters
Fabian Bamberg
Holger Schulz
Christopher L. Schlett
Publikationsdatum: 27.08.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 3/2019
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5659-9

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