Dear AuntMinnieEurope Member,
At the Olympics this week, all the attention has focused on the athletes, but it's vital to remember that the Games relies on legions of volunteers, including the 23 radiologists and 75 radiographers working in Tokyo.
We caught up with Dr. Yukihisa Saida to find out how things are going in the polyclinic. Read what he had to say in the MRI Community.
Reliable information about the COVID-19 pandemic's impact on medical imaging remains in short supply, but the European Association of Nuclear Medicine has sought to rectify the matter with a large survey of its members. A major surprise is that the volume of PET/CT scans actually increased in two countries in 2020. Learn more in the Molecular Imaging Community.
How can we improve the informed consent process for angioplasties and other procedures? What can we do differently? Interventional radiologist Dr. Chris Hammond has addressed these questions in a thought-provoking new column.
In other news, Spanish researchers have looked at how to boost outcomes in stroke. They're convinced that patients with suspected large vessel occlusion stroke have better prospects when they're transferred directly to the angiography suite for diagnosis. Their recent study is based on 174 cases.
Finally, a group from Montpelier, France, has supported the use of two-view digital breast tomosynthesis for the characterization of breast lesions, particularly when the readers are inexperienced. Head over to the Women's Imaging Community to get the 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)
