Dear AuntMinnieEurope Member,
It's been an extremely busy and significant week for radiology in France.
The French national congress, JFR, is one of medical imaging's big three meetings of the year, along with RSNA and ECR. Frances Rylands-Monk, the fluent French speaker on our team who lives in Brittany, has covered the event for us.
As you'll see from the impressive lineup below, there have been many highlights, but arguably the biggest surprise was the release of a new rap song. It's had over 4,000 views since 9 October and is an intriguing production with a novel approach.
On top of that, news broke from Paris that an 11.7-tesla MRI scanner has produced its first images. Don't miss our exclusive interview with Dr. Denis Le Bihan, PhD, the visionary founder of NeuroSpin, where the machine is located. He explains why they scanned a pumpkin and talks about the group's future plans and goals. Find out more in the MRI Community.
Taking place at the same time as JFR was the European Society of Magnetic Resonance in Medicine and Biology (ESMRMB) virtual meeting, at which Prof. Dr. Regina Beets-Tan from Amsterdam gave a broad-ranging keynote lecture on oncologic imaging. As a past winner of the Most Effective Radiology Educator award in the EuroMinnies and president-elect of the European Society of Radiology, she's always worth listening to.
Away from the congress scene, an Italian survey highlighted patients' "substantial lack" of knowledge about medical radiation. The authors are convinced about the need for intervention to achieve better patient awareness of the risks. For the full story, go to the CT Community.
Finally, if you're fascinated by images of ancient mummies, take a look at this report from Germany, which includes some striking cases from Egypt and South America.
![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)
