Dear AuntMinnieEurope Member,
The second wave of COVID-19 is taking a firm grip across large swathes of Europe, so this week's lead article about the successful development of a free training tool to aid detection is timely news indeed.
The project is an example of international collaboration at its best. Three radiologists based in Germany, Italy, and Australia worked together between March and May on producing 19 interactive cases and presenting them in a practical and easy-to-use format. Don't miss our report in the CT Community.
Digital breast tomosynthesis (DBT) can bring many benefits, but an important potential downside is reader fatigue due to lengthy reporting times. A U.K. team has found radiologists blink much more often after examining 20 DBT cases. The group suggests that taking a break after 20 reports can help to eliminate mistakes. Learn more in the Women's Imaging Community
The annual congress of the European Association of Nuclear Medicine (EANM 2020) wraps up today. As part of our coverage, we've posted two articles. The first features new data and clinical images from the Mayo Clinic in Arizona about PET/CT of neuroendocrine tumors. It speaks volumes about EANM 2020 that investigators from this prestigious U.S. facility chose the virtual congress to share its experiences.
Our second piece is from the plenary session about total-body PET. We've focused on the visionary talk about dynamic PET given by Prof. Irène Buvat, from the Curie Institute and France's national institute of health and medical research, INSERM. Visit the Molecular Imaging Community for more on this story.
Finally, we bring you a profile of the Enhancing Neuro Imaging Genetics through Meta-Analysis working group. Its main goal is to facilitate the development and dissemination of multiscale and big data analysis pipelines for traumatic brain injury patients.
![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)
