MRI safety topics, along with the risk of cancer caused by lifetime exposure to CT, continue to be uppermost in the minds of the European medical imaging community, according to our list of top 10 articles for this year.
In March, we published a report about a TikTok video posted by a patient who had experienced pain during an MRI exam due to her hair extensions. This sparked a debate among safety experts about whether extensions represent a serious risk. Our report on this issue was the second most popular story of the year.
A month later, we broke the news in the English-language media about an MRI safety incident in Turku, Finland, involving a floor cleaning machine being sucked into a scanner. This turned out to be our most-viewed article in 2025.
Also during April, researchers suggested that cancers associated with radiation from CT scans could eventually account for 5% of all new cases annually. The study was published in JAMA Internal Medicine, and our report was the third most popular story among readers.
Below is the full top 10 list of articles on AuntMinnieEurope for the past year, as measured by page views. We hope you enjoy reading this list. Looking ahead, we relish the prospect of providing you with the news that matters in 2026.
Top 10 stories for 2025
- Floor cleaner sucked into MRI in Finland. Posted 22 April
- Hair extensions and MRI: To scan or not to scan? Posted 17 March
- CT estimated to cause 5% of new cancer cases. Posted 16 April
- ChatGPT gathers momentum in MR imaging. Posted 16 September
- Coroner voices deep concerns over radiology services. Posted 29 April
- How do clinicians perceive use of AI to triage brain MRI? Posted 7 January
- Radiology mourns death of Ireland’s Barry Kelly. Posted 24 June
- Merger of radiology and pathology: What’s the next step? Posted 3 November
- F-18 PSMA PET-CT proves clinical value in prostate cancer. Posted 24 April
- Radiology errors lead to scan delays and avoidable deaths. Posted 24 March
Philip Ward
Editor in Chief
AuntMinnieEurope.com
![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&fit=crop&h=100&q=70&w=100)
![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)
