Dear AuntMinnie Member,
The Minnies are moving into their final stage. Your votes have selected two finalists in each of our 12 categories, and now we're calling on AuntMinnie members to choose the final winners in our annual radiology awards event.
Our categories range from Best New Radiology Product to Most Effective Radiology Educator. We've also thrown in a couple of categories to let you tell us your opinions on what's hot in the specialty, like Scientific Paper of the Year and Biggest Threat to Radiology.
Winners will receive elegant trophies to be awarded at this year's RSNA meeting in Chicago, as well as the knowledge that their achievements have received the recognition of their peers.
Voting in the finals round will be open for only a few short weeks, so be sure to head for minnies.auntminnie.com today to cast your vote!
![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.auntminnie.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)
