The clinical potential of 11.7-tesla MRI, together with medicolegal investigations and patient safety topics, feature prominently in the lineup of AuntMinnieEurope.com's most popular articles in 2024. Our top 10 list has been published today.
During the first half of January, many of you were fully engaged in the suspension of Dr. Martin Schranz, the senior radiologist at University Hospital Kerry in Ireland. He eventually returned to work after a change of heart from the powers-that-be. Our second article on this saga occupies the number two spot.
The number one belongs to France. April's report about the first 11.7-tesla MRI scans of the human brain clearly intrigued many thousands of you. No doubt the stunning selection of clinical images and the expert analysis from Prof. Denis Le Bihan, PhD, grabbed your attention – as it did for French President Emmanuel Macron.
MRI safety remains a central issue in radiology. In late July, Associate Editor Frances Rylands-Monk broke the news of an accident in the city of Izmit, around 100 km southeast of Istanbul, Turkey. That article appears in the number three position.
Other standouts in the list are two cases of serious misconduct that led to the individuals being struck off (numbers 4 and 6), the suspension of seven radiologists at a Belgian hospital (number 5), and Dr. Paul McCoubrie's column about the U.K. junior doctors' strike (number 9).
Below is the full top 10, as measured by member traffic. We look forward to providing you with further insightful and entertaining coverage in 2025.
Top 10 stories for 2024
- French-led team unveils 11.7 tesla MRI scans of human brain. Posted 3 April
- Suspended Irish radiologist refuses to stay silent. Posted 9 January
- MRI accident causes serious damage at Turkish hospital. Posted 30 July
- Struck off: Spanish radiologist guilty of misconduct. Posted 30 April
- New Belgian hospital suspends entire team of radiologists. Posted 7 June
- Radiographer struck off for being 'rude,' 'aggressive'. Posted 6 March
- Siemens unveils Magnetom Flow MRI scanner at ECR 2024. Posted 29 February
- Debunked: The 10 most common myths about radiology. Posted 3 April
- Why are the juniors revolting? Posted 30 April
- Inquest into baby's death focuses on radiology staff shortages. Posted 30 January
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)
