Dear AuntMinnieEurope Member,
Some observers were mighty puzzled when Dr. Noel Fitzpatrick was unveiled as a keynote lecturer for the U.K. Radiological Conference. They wondered what radiologists could learn from the man known in TV circles as the "Bionic Vet" for his radical surgery on animals. A common concern was whether the congress organizers were opting for a celebrity speaker over a scientist.
Fitzpatrick won over the audience in Manchester last week by outlining what human doctors can learn from their veterinary colleagues. He also did what many eminent speakers fail to do: He entertained and enlivened the crowd. Click here to find out how he did it.
Radiology in the Netherlands owes a huge debt to Dr. Carl Puylaert, who died two weeks ago. Fellow radiologists Dr. Kees Vellenga and Dr. Paul Algra both knew him well, and we invited them to pay tribute to this inspirational leader and teacher. Get the story here.
RadMiner is probably not as familiar to you as Google and other search engines, but following a successful pilot project in Germany, it may soon become a useful everyday tool. As part of our news coverage from the Computer Assisted Radiology and Surgery (CARS) congress in Pisa, Italy, we asked the developers to explain more about its potential. Go to our Healthcare Informatics Digital Community, or click here. Also, make sure you check out our other articles from the congress.
Since the Poly Implant Prosthèse scandal, women with breast implants have become more frequent visitors to imaging departments across Europe. Researchers have discovered a novel way of using MRI more effectively on these patients. Visit our Women's Imaging Digital Community, or click here.
Last but not least, our Case of the Week section now has a subspecialty archive, and this will help you search for specific cases. To access it, click here. Alternatively, go the Education drop-down menu at the top of our home page and click on the second item, called Archives by Category. Please tell your colleagues about this new feature.
![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)
