VIENNA - The unusually mild temperatures and clear skies in Vienna over the past few days must have tempted some ECR 2014 delegates to skip the less urgent sessions, but there was no indication this happened. The busy corridors and healthy attendance in most rooms of the Austria Center suggest that very few attendees got sidetracked.
Our team of six editors has been keeping busy, too, as you'll see by the comprehensive coverage in our RADCast @ ECR.
ECR 2014 has been a well-organized and successful meeting, and please make some time to check out our news stories from the conference. Although the conference ended this afternoon, we're continuing to post new material.
And don't miss our new feature this year -- a series of seven short videos on a range of topics:
- Errors in radiology with Dr. Catherine Mandel
- Mobile devices and teleradiology with Dr. Erik Ranschaert
- Turf battles with Dr. Georg Bongartz
- Working in the Middle East and tips for expatriates by Dr. Michael Fuchsjäger
- ECR and the crisis in Ukraine with Dr. Valentin Sinitsyn
- EuroSafe Imaging campaign with Dr. Guy Frija
- European Society of Medical Imaging Informatics (EuSoMII) with Dr. Emanuele Neri
Be sure to check out these videos, news stories, and more in our RADCast, available at radcast.auntminnieeurope.com. We hope you enjoy the coverage as much as we have enjoyed putting it together.
![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)
