Dear AuntMinnieEurope Member,
The name of Dr. Arshad Munir probably won't mean anything to you. He wasn't a pioneer or an expert in a particular field. If you Google him, hardly anything comes up. Apparently he was just a hardworking, patient-focused radiologist who did his job.
This is why we've made the tragic story of Dr. Munir's death this week's top article. For more than a year now, COVID-19 has quietly picked off good people. It's vital for the global imaging community to remember those who've lost their lives, including Prof. Natalia Rotaru, PhD, from Moldova; Dr. Mushira Mahfouz Qudsy from Egypt; and Dr. Carlo Amodio and Dr. Maurizio Galderisi from Italy.
In other news, a long-awaited Swedish breast study on interval cancers was published on 6 April. Don't miss our report. You'll find it in the Women's Imaging Community.
Point-of-care ultrasound (POCUS) continues to make rapid progress, and it is proving especially useful in remote areas. Swiss researchers have described how they used POCUS to assist in a rescue on Mount Everest.
Over the past few months, many of you have enjoyed Dr. Chris Hammond's thought-provoking columns. As well as being a vascular radiologist, he has a strong interest in health economics and ethics. His latest article looks at what is meant by "clinical need" and "comprehensive service."
Staff recruitment in nuclear medicine appears to be a growing problem. In the Netherlands, the number of residents choosing nuclear medicine as a specialty dropped from 50 to 14 trainees in 2019, and the situation is causing a reassessment of how to attract the much-needed workers of the future. Find out more in the Molecular Imaging 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)
