Magnetic resonance imaging (MRI)
Project goals
At the five NAKO sites in Augsburg, Berlin, Essen,
Mannheim and Neubrandenburg, a selected group of study participants will take part in a magnetic resonance imaging (MRI) examination.
MRI is a non-invasive, high-resolution imaging procedure that works without X-rays or radioactive radiation, but only through the use of magnetic fields. The aim is to visualise the entire interior of the body and even organ movements in three dimensions through individual layers. Early changes to the organs, pathological processes or even special variants of the human body can be visualised. MRI was included in the NAKO study in order to better understand the development of common diseases and thus, for example, to develop better preventive procedures.
First results
See publications
Publications
Jung M, Raghu VK, Reisert M, et al. Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western population. eBioMedicine. 2024;110:105467. http://doi.org/10.1016/j.ebiom.2024.105467
Kellner E, Sekula P, Lipovsek J, et al. Imaging markers derived from MRI-based automated kidney segmentation. Deutsches Ärzteblatt international. 2024. http://doi.org/10.3238/arztebl.m2024.0040
Schlett CL, Hendel T, Hirsch J, et al. Quantitative, Organ-Specific Interscanner and Intrascanner Variability for 3 T Whole-Body Magnetic Resonance Imaging in a Multicenter, Multivendor Study. Investigative Radiology. 2016;51(4):255-265. http://doi.org/10.1097/RLI.0000000000000237
Küstner T, Hepp T, Fischer M, et al. Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies. Radiology: Artificial Intelligence. 2020;2(6):e200010. http://doi.org/10.1148/ryai.2020200010
Schuppert C, Krüchten RV, Hirsch JG, et al. Whole-Body Magnetic Resonance Imaging in the Large Population-Based German National Cohort Study: Predictive Capability of Automated Image Quality Assessment for Protocol Repetitions. Invest Radiol. 2022;57(7):478-487. http://doi.org/10.1097/RLI.0000000000000861
Kart T, Fischer M, Winzeck S, et al. Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies. Sci Rep. 2022;12(1):18733. http://doi.org/10.1038/s41598-022-23632-9
Gatidis S, Kart T, Fischer M, et al. Better Together: Data Harmonization and Cross-Study Analysis of Abdominal MRI Data From UK Biobank and the German National Cohort. Invest Radiol. 2023;58(5):346-354. http://doi.org/10.1097/RLI.0000000000000941
Haueise T, Schick F, Stefan N, et al. Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort. Sci Adv. 2023;9(19):eadd0433. http://doi.org/10.1126/sciadv.add0433
Fischer M, Küstner T, Pappa S, et al. Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study. BMC Med Imaging. 2023;23(1):104. http://doi.org/10.1186/s12880-023-01056-9
Schuppert C, Rospleszcz S, Hirsch JG, et al. Automated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study. Sci Rep. 2023;13(1):22745. http://doi.org/10.1038/s41598-023-49569-1
Speakers
Prof. Dr. Fabian Bamberg
Prof. Dr. Christopher Schlett (Deputy Spokesperson)