Dear AuntMinnieEurope Member,
The tragic death of Dr. Alban Gervaise, PhD, leaves some unanswered questions: Was he targeted by the attacker? Was it a random killing? Can more be done to ensure the safety of military radiologists?
Our report on the incident is this week's most viewed article. Let's hope the French authorities provide some answers over the coming weeks and months.
Another popular news item has been the interview with a German radiologist about his team's efforts to help deliver aid to Ukrainian refugees. After dropping supplies at the Polish border, the group is bringing some refugees back to Mainz, where they are even taking care of their health. Don't miss this uplifting article.
Yesterday's radiology census report represented "a stark wake-up call" for the U.K. government and National Health Service, according to the respected interventional radiologist Dr. Raman Uberoi. This annual document always contains a wealth of useful data and information. Look out for our follow-up coverage later this month.
Meanwhile, women referred for breast MRI exams often have metallic biopsy clips placed within or adjacent to a lesion, and these clips produce artifacts that can lead to misinterpretations. Austrian researchers have taken a close look at this topic, and their analysis deserves a close look. Find out more in the Women's Imaging Community.
Also, we have an article about a thought-provoking study by Keith Cover, PhD, an independent researcher from Amsterdam. He found that MRI screening can detect invasive breast cancers about six years earlier than mammography. Go to the MRI Community for the full 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)
