Dr. María Jesús Díaz Candamio and Dr. Erik Ranschaert[email protected]Organized Radiology IssuesRadiologists must go global with TwitterTwitter is becoming an invaluable platform for radiologists, and after a slow start, European radiologists are embracing the platform. Dr. María Jesús Díaz Candamio and Dr. Erik Ranschaert offer a variety of reasons why Europeans should start tweeting -- even if they aren't doing it in English.July 4, 2017Page 1 of 1Top StoriesCTWelding fumes linked to distinct lung CT changes, functionDiagnosing welder's pneumoconiosis via imaging is challenging and often underestimated due to the high smoking rates among welders.Molecular ImagingHow AI can help with PET/CT guideline deviationsMRIMRI reveals how hidden muscle fat increases risk to heart, metabolismMolecular ImagingThe EANM joins EMUC26 and pushes its policy agenda in BrusselsSponsor ContentTurning Innovation into Everyday Efficiency
![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)
