The German Radiological Society (DRG) has released details about the 104th German Radiology Congress (RöKo), which will be presided over next year by Prof. Dr. Christiane Kuhl, PhD, the renowned breast imaging researcher from University Hospital RWTH Aachen.
The meeting will comprise a face-to-face gathering and an online part, both under the motto "The Research Adventure!"
Doctors, physicists, medical technologists for radiology, and natural scientists at the congress will discuss current developments in medical imaging and the targeted, image-guided treatment of diseases, the DRG announced on 20 October.
RöKo Digital starts on 1 March and lasts until 24 June. The face-to-face congress will be held from 17 to 19 May at the RheinMain CongressCenter in Wiesbaden, the state capital of Hesse.
More information can be found on the organizer's website.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
