Dear AuntMinnieEurope Member,
A leading musculoskeletal radiologist from Kyiv has recently described how she fled Ukraine and is living in Berlin, where she is campaigning for refugee radiologists' ability to work in their host countries.
Although many Ukrainian specialists would like to work as doctors to give back to their host nations, their diplomas aren't currently accepted in the European Union. In a riveting presentation at the annual meeting of the European Society of Musculoskeletal Radiology, Dr. Nataliia Nehria, medical director of MRT Plus in Kyiv, shared her experience in the aftermath of the Russian invasion and called for a simpler and more flexible procedure for validation of Ukrainian diplomas. Our coverage of her talk was the most highly viewed article of the week.
In other news, a group from Hannover Medical School in Germany received the highly coveted Image of the Year award at this week's Society of Nuclear Medicine and Molecular Imaging (SNMMI) meeting in Vancouver, Canada. The honor was bestowed for PET imaging that reveals poor outcomes in patients after a heart attack.
Using a gallium-68-labeled (Ga-68) radiotracer designed to bind to fibroblast cells (Ga-68 FAPI-46), the team found that the fibroblast-activation protein inhibitor (FAPI)-PET signal in injured heart muscle predicted heart dysfunction in patients more than four months later. Get the whole story in the Molecular Imaging Community.
Contrast-enhanced MRI is more sensitive for detecting breast cancer than contrast-enhanced mammography, but it is also less specific, a team of researchers from Austria recently reported. The clinical relevance of these diagnostic performance differences is still unclear, however. Find out more by visiting the Women's Imaging Community.
A French expert has found that an Meanwhile, a presentation at the recent Society for Imaging Informatics in Medicine (SIIM) meeting shared how cloud-based image exchange could finally eliminate the use of CDs.
![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)
