Dear AuntMinnieEurope Member,
Madan Rehani, PhD, has had a longstanding and active role in radiation protection, having worked for the World Health Organization and radiation safety groups, as well as in academia. He's a much-admired figure across the globe, so his close involvement in a research program involving breast imaging is bound to generate considerable interest.
You can find out more about his team's latest findings in our news report in the Women's Imaging Community.
Reading about errors made by colleagues can provide a wonderful learning opportunity, but to protect the identities of individuals and the patients involved, detailed information about incidents is often lacking. In a bid to help others, two Australian investigators have now provided precise descriptions of 10 actual cases of mistakes made by trainees. Head across to the CT Community for the full story.
Predatory journals are thriving in the era of open access publishing, including in radiology. These operations are proving costly to authors and risk undermining the publication process, and urgent action is needed, according to research from India.
In other news, a large exhibition opening in Germany on 1 October has caught the attention of the Maverinck. It's made him think more deeply about epidemics. Don't miss his new column.
Last but not least, we have a sports imaging article for you about the Paralympics in Tokyo. The ability of the athletes to overcome huge adversity is always a striking element of this event, but what specific challenges did it pose for the radiologists on duty in the polyclinic? Discover more in the MRI Community.
![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)
