Workflow-centred open-source fully automated lung volumetry in chest CT. Issue 1 (January 2020)

• Record Type: Journal Article
• Authors: Jungmann, F.; Brodehl, S.; Buhl, R.; Mildenberger, P.; Schömer, E.; Düber, C.; Pinto dos Santos, D.
• Is Part Of: Clinical radiology. Volume 75:Issue 1(2020)
• Page Start: 78.e1
• Page End: 78.e7
• Publication Date: 2020-01
• Subjects: Medical radiology -- Periodicals; Radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiology -- Periodicals; Societies, Medical -- Periodicals; Medical radiology; Radiotherapy; Electronic journals; Periodicals; 616.0757
• DOI: 10.1016/j.crad.2019.08.010
• Languages: English
• ISSNs: 0009-9260

Abstract

Abstract : Aim: To develop a robust open-source method for fully automated extraction of total lung capacity (TLC) from computed tomography (CT) images and to demonstrate its integration into the clinical workflow. Materials and methods: Using only open-source software, an algorithm was developed based on a region-growing method that does not require manual interaction. Lung volumes calculated from reconstructions with different kernels (TLCCT ) were assessed. To validate the algorithm calculations, the results were correlated to TLC measured by pulmonary function testing (TLCPFT ) in a subgroup of patients for which this information was available within 3 days of the CT examination. Results: A total of 288 patients were analysed retrospectively. Manual review revealed poor segmentation results in 13 (4.5%) patients. In the validation subgroup, the correlation between TLCCT and TLCPFT was r =0.87 ( p <0.001). Measurements showed excellent agreement between the two reconstruction kernels with an intraclass correlation coefficient (ICC) of 0.99. Calculation of the volumes took an average of 5 seconds (standard deviation: 3.72 seconds). Integration of the algorithm into the departments of the PACS environment was successful. A DICOM-encapsulated PDF document with measurements and an overlay of the segmentation results was sent to the PACS to allow the radiologists to detect false measurements. Conclusions: The algorithm developed allows fast and fully automated calculation of lung volume without any additional input from the radiologist. The algorithm delivers excellent segmentation in >95% of cases with significant positive correlations between lung volume on CT and TLC on PFT. Highlights: Our open source algorithm allows fast and fully automated calculation of lung volume in multidetector computed tomography. Lung volume measured by CT correlated significantly with pulmonary function testing. Integration into the clinical workflow offers calculations at the start of the reporting process without manual interaction.