Dear AuntMinnieEurope Member,
You have to admire the courage of Dr. Adrian Brady from Dublin. He's tackled head-on one of the most urgent and challenging issues faced by medical imaging today: the workload of a radiologist.
"Our work is visualized in terms of x-rays or cross-sectional studies reported; if we are generating reports, we are working. If we are not producing reports, then we are not being productive," he noted.
His analysis and viewpoint will be of great interest to everybody working in the field. For more of his words of wisdom, click here.
Brain drain also is a constant concern in some countries, particularly those in Central and Eastern Europe. Hungary is continuing to lose many radiologists to Scandinavia, Germany, the U.K., and the Republic of Ireland, and this is having a huge impact on the nation's healthcare system. Our Hungarian-based correspondent, Robin Marshall, has investigated this problem, and you can read his in-depth article by clicking here.
The University of Munich is a leading global research center, and any new studies conducted there warrant close scrutiny. Dr. Konstantin Nikolaou and his team have found that CT can play a role in functional cardiac imaging with the use of a dynamic CT perfusion technique they have developed that measures myocardial blood flow. Click here for the full story.
Gun crime is increasing at an alarming rate in many societies, and this means hospitals are now treating more cases of gunshot injuries than ever before. Plain radiography and CT are still the most commonly used modalities for imaging of gunshot wounds, but angiography and MRI are playing an increasing role. Click here for the story.
![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)
