Dear AuntMinnieEurope Member,
Any radiologist seeking to specialize in an area with guaranteed growth potential in the years ahead must surely put imaging of psychiatric disorders near the top of their list, particularly given the rise of mental illness during the pandemic.
A fascinating session on the current and future clinical applications of functional imaging for psychiatric pathologies took place recently at the French national congress, JFR. Find out more in the Artificial Intelligence Community.
News broke earlier this week about the death of a patient in an MRI accident in South Korea. This follows an incident in the University of Utah Hospital in Salt Lake City, in which a worker died when helping to move an MRI scanner. Get the full details in the MRI Community.
Meanwhile, Spanish researchers have released new findings about the use of ultrasound to identify unilateral axillary adenopathy in patients who had the Pfizer-BioNTech COVID-19 vaccine. Radiologists must consider recent vaccination history as a possible differential diagnosis for patients with unilateral axillary adenopathy, such as during breast cancer screening, they say. Go to the Women's Imaging Community.
Abdominal pain in pregnancy is surprisingly common, and interpretation of acute abdominal pathology in an obstetric patient on MRI is becoming an increasingly valuable skill for on-call radiologists. That's the view of Australian authors, who are convinced that following a simple three-step approach can lead to notable improvements in the diagnosis of appendicitis in pregnant women.
Last but not least, a U.K. report on the use of outsourcing has generated considerable interest. Regular columnists Drs. Giles Maskell and Paul McCoubrie have shared their views with us on the subject.











![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&fit=crop&h=112&q=70&w=112)





