Dear AuntMinnieEurope Member,
As the new year looms large, now is the ideal time to draw breath and reflect on the most popular articles published by AuntMinnieEurope.com during the past 12 months. This annual tradition always provides an informative guide as to what's uppermost in the minds of the European imaging community.
The debate about breast screening has intensified and taken numerous twists and turns over the course of the year, so it's no great surprise that two of our top four articles are about this topic. The issue seems far from being resolved, and further reports appear certain to follow in 2015.
Sports imaging is also a high priority, and thousands of subscribers clicked on our stories about the World Cup and the Winter Olympics.
The controversy about the Nobel Prize for MRI resurfaced at ECR 2014 earlier this year. Dissatisfaction over long waiting times and the backlog of examinations also came to the fore, particularly in France and the U.K.
Our full list of top 10 articles, measured by the total number of page views, is given below. To access the relevant article, simply click on the title:
- World Cup controversy over Neymar CT scan goes viral
- Norwegians seek to add clarity to breast screening dispute
- Radiology under the Nazis
- Mammo skeptics make new bid to stop U.K. breast screening trial
- Conflict over recognition for MRI discovery resurfaces at ECR
- Some practical tips on buying a new scanner
- Guy Sebag: Europe mourns a top pediatric radiologist
- Claustrophobia, MRI, and the human factor
- French MRI waiting times are 'worst in a decade'
- Scanning from Sochi: Winter Olympics medical teams are ready
During 2014, it's been a real pleasure and privilege for us to keep you informed about events and developments in the world of medical imaging. We would like to thank you for your continued support, encouragement, and participation. Many thanks also to our editorial advisory board and regular columnists.
My colleagues and I wish you all the very best for the new year, and we look forward to continuing to serve you in 2015.
![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)
