Dear AuntMinnie Member,
The campaigning is over and the votes have been counted. The 2004 edition of the Minnies has drawn to a close, with final results now available.
This year's Minnies included 217 candidates competing in 14 categories. Your votes picked the winners in 12 of those categories, while two Best of the Web categories for excellence in Web site design at hospitals and imaging centers were selected by a panel of AuntMinnie staff members.
We'd like to congratulate the winners and the runners-up -- winners will receive trophies at this year's RSNA meeting in Chicago. We'd also like to thank the thousands of AuntMinnie members who participated in this exercise of democracy. By making your voice heard, you helped us recognize the radiology professionals who are advancing our specialty every day.
To see the list of Minnies winners, just go to minnies.auntminnie.com. For a complete list of all the Minnies candidates, click here.
When you're done, check out a new story in our Imaging Center Digital Community on controversial new payor rules aimed at limiting utilization of imaging studies. The rules could have a major impact on your practice -- especially if you're offering imaging services in just one or two modalities -- but could also cut down on physician self-referral. Learn all about it by going to centers.auntminnie.com.














![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&fit=crop&h=112&q=70&w=112)