Dear AuntMinnieEurope MRI Insider,
MRI research in Europe is in excellent shape and goes from strength to strength, judged on last month's International Society for Magnetic Resonance in Medicine (ISMRM) annual meeting in Toronto, where many European presenters played a leading role.
Today we're featuring an award-winning collaboration on diffusion MRI between two well-respected groups in Switzerland and the U.K. The lead author is convinced the study has significant implications for general radiologists, especially neuroradiologists.
Life expectancy in Spain is nearly 86 years -- the highest in Europe and the fourth highest worldwide. Analysis from Madrid suggests that the brains of so-called SuperAgers are less affected by the cognitive decline that tends to manifest as people age. Also, they are more likely to have greater movement speed and lower rates of anxiety and depression.
In other news, researchers from Turku in Finland have used functional MRI and PET to discover more about male orgasms. What did they learn? Find out in our special report.
Prof. Dr. Konstantin Nikolaou, the new president of the German Roentgen Society, has a keen interest in MRI, having been a member of ISMRM's Committee of Clinical Growth and a board member of the European Society of Cardiovascular Imaging. In his first major interview, he says his priorities will be to boost the visibility of radiology, modernize the training system, and implement AI.
Finally, we're highlighting a Dutch study that shows how functional MRI has provided evidence that prenatal maternal anxiety has a long-term negative impact on the children of women with the condition.
In this newsletter, we've outlined just a handful of the many reports posted in the MRI Community over the past few weeks. Please scroll through the full list below, and feel free to contact me if you have ideas for future coverage.


![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&fit=crop&h=112&q=70&w=112)









