Automated MR-based lung volume segmentation in population-based whole-body MR imaging

• Verfasst von: Müller, Jan [VerfasserIn]
• Verfasst von: Krüchten, Ricarda von [VerfasserIn]
• Verfasst von: Schlett, Christopher L. [VerfasserIn]
• Titel: Automated MR-based lung volume segmentation in population-based whole-body MR imaging
• Titelzusatz: correlation with clinical characteristics, pulmonary function testing and obstructive lung disease
• Verf.angabe: 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
• Jahr: 2019
• Jahr des Originals: 2018
• Umfang: 12 S.
• Fussnoten: Published online: 27 August 2018 ; Gesehen am 09.05.2019
• Titel Quelle: Enthalten in: European radiology
• Ort Quelle: Berlin : Springer, 1991
• Jahr Quelle: 2019
• Band/Heft Quelle: 29(2019), 3, Seite 1595-1606
• ISSN Quelle: 1432-1084
• ISSN Quelle: 1613-3757
• DOI: doi:10.1007/s00330-018-5659-9
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Computer-assisted image analysis
• Sach-SW: Magnetic resonance imaging
• Sach-SW: Obstructive lung disease
• Sach-SW: Pulmonary function test
• Sach-SW: Whole-body imaging
• K10plus-PPN: 1665077484

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