Dear AuntMinnieEurope Member,
When you think of leading experts in contrast media, the chances are the name of Prof. Henrik Thomsen will come to mind. He's been active in the field for many years and is widely respected.
To mark the centenary of Acta Radiologica, of which Thomsen is chief editor, he's collaborated with Dr. Yousef Nielsen to produce a comprehensive overview of the journal's past publications on contrast media. This article has prompted radiology historian Dr. Adrian Thomas to write a column about the topic. Find out more in the MRI Community.
In other news, an industry story with global implications broke on Tuesday, when GE announced that its healthcare, aviation, and energy businesses were to be split up. Interestingly, GE Healthcare is now to be led by Peter Arduini, who is also a director of the National Italian American Foundation, suggesting that Italy means a great deal to him. Get the full story in the CT Community
Prof. Vicky Goh is a soft-spoken, modest person, but she's always well worth listening to. After her keynote lecture at last week's British Institute of Radiology annual congress, there was a lively and informative Q&A discussion about hybrid imaging. Don't miss our report on this session.
The BerlinCaseViewer has proved to be a valuable resource during the pandemic. Initially, it could only be used on an iPad, and this restricted uptake, but now it can be used on Android devices, in addition to iPhones.
Last but not least, German researchers have developed a dark-field chest x-ray system and tested its performance for early detection and quantification of emphysema in chronic obstructive pulmonary disease. They found that dark-field x-ray detected structural impairments associated with emphysema in tiny alveolar surface areas of the lungs.
![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)
