Dear AuntMinnieEurope Member,
A common debate within European radiology during recent years has been whether generalists or specialists will be needed in the future. There's no easy answer, of course. Every radiologist has to specialize to some extent, but the key priority is to maintain general know-how and develop subspecialist skills at the same time. Striking this balance can help to ensure success.
In a frank and highly personal guest blog, Norwegian radiologist and AuntMinnieEurope.com editorial advisor Dr. Anagha Parkar explains the process she went through when she reassessed her career objectives and looked at how to meet her own training needs. Click here to read more.
Another ongoing issue is PACS in France. Compared with Scandinavia and most German-speaking countries, PACS is not well developed in France, but the situation is steadily changing. For our exclusive interview with Dr. Jean-Philippe Masson, general secretary of the French Union of Private Radiologists, click here.
Taking practical steps to lower radiation dose is a major consideration in every hospital today, and some leading U.K. researchers have studied how to optimize acquisition techniques in perfusion CT of the thorax, abdomen, and pelvis. To read about their important study in the May edition of European Radiology, click here.
In the Republic of Ireland, a new diagnostic reference level standard is being developed to define radiation exposure limits from mobile chest x-ray performed in the neonatal intensive care services. Interestingly, infant body weight has been given precedence over infant age. Click here to find out more.
There was also some significant industry news this week when Toshiba announced that it plans to acquire Vital Images. Toshiba thinks this transaction will allow it to meet the global demand for advanced visualization and imaging informatics provided to healthcare professionals and through healthcare IT providers. Check out our Advanced Visualization Digital Community for more details.
![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)
