Imalife is an additional assessment, performed in ~12,000 adult Lifelines participants in collaboration with the UMCG Department of Radiology.
The aim of Imalife was to use a new computed tomography (CT) scan technique to evaluate quantitative imaging biomarkers of early stages of lung cancer, chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD) (the “Big-3”) at ultra-low radiation dose. Imaging biomarkers, combined with clinical and laboratory biomarkers and medical decision support systems, can in the future open up new avenues for effective prevention and/or early diagnostic and treatment protocols. This study provides an invaluable resource for the development and validation of biomarker profiles in the context of personalized medicine.

Participants underwent ultra-low-dose CT scanning of lungs and heart with third-generation dual-source CT (SOMATOM Force, Siemens). Scanning was performed during suspended inspiration, according to the standard vendor recommended protocol, with tube current adjusted automatically by the system for body size based on the scout images. The scout images were followed by a CT scan of the lungs in inspiration, and, in a subset of 100 participants per 5-year age/gender category, also in expiration. The latter was performed to determine normal values for expiratory lung density.
To obtain a CT scan of the heart at the moment that the movement of the coronary arteries is the lowest, participants were fitted with ECG electrodes by CT personnel. The software of the CT scanner decided what the best moment is during a regular heartbeat to acquire the CT data of the heart, and subsequently the CT scan of heart was obtained.
The primary end-point was the establishment of reference values of:

• lung density
• bronchial wall thickness
• vascular calcification
• lung nodules

Imalife ran from 2017 to 2022. The assessments were performed in ~12.000 Lifelines participants of ≥45 years who completed a pulmonary function test.

• Xia, C et al. (2019) Cardiovascular Risk Factors and Coronary Calcification in a Middle-aged Dutch Population. Journal of Thoracic Imaging 36(3): 174-180
• Van den Oever L.B. et al. (2020) Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium. European Journal of Radiology 129, 109114
• Xia, C et al. (2021) High-pitch dual-source CT for coronary artery calcium scoring: A head-to-head comparison of non-triggered chest versus triggered cardiac acquisition. Journal of Cardiovascular Computed Tomography 15(1): 65-72
• Lancaster, H et al. (2021) Seasonal prevalence and characteristics of low-dose CT detected lung nodules in a general Dutch population. Sci Rep 11(1): 9139
• Cai, J. et al. (2022) CT characteristics of solid pulmonary nodules of never smokers versus smokers: A population-based study. Eur J Radiol 154:110410
• Wisselink, H. et al. (2023) Predicted versus CT-derived total lung volume in a general population: The ImaLife study. Plos ONE 18(6):e0287383
• Dudurych, I. et al. (2023) Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction. Eur Radiol 33, 6718–6725
• Wisselink, H et al (2023) CT-based emphysema characterization per lobe: A proof of concept Eur J Rad 160, 110709
• Dudurych, I. et al. (2021) Creating a training set for artificial intelligence from initial segmentations of airways Eur Rad Exp 5, 54

A precise list of variables derived from CT-scans will be described later.