Dear MRI Insider,
Pelvic floor disorders are a common health problem, and they can be exacerbated by advanced age, obesity, smoking, previous pelvic surgery, and other factors. MRI is proving of great value in these cases, but optimum technique is essential for success, says a Portuguese team.
The group's research received the accolade of a magna cum laude at ECR 2017, so clearly the authors know what they're talking about. Find out more here.
Use of 3-tesla MRI is central to the National Cohort Health Study in Germany, which began three years ago. Steady progress is being made in this hugely significant project, according to Dr. Fabian Bamberg, who has provided an update in an interview. To learn more, click here.
Following its recommendation to pull four linear-based gadolinium MRI contrast agents off the market, the European Medicines Agency's Pharmacovigilance Risk Assessment Committee (PRAC) is looking again at the evidence in response to requests from contrast developers affected by the decision. The PRAC expects to complete the review in July, so several months of uncertainly seems inevitable. To get the full background, see this column from Dr. Peter Rinck, PhD, and read our breaking news report.
Today, MRI can provide useful diagnostic information about stroke patients, but what do radiologists really need to know about cardiac findings in these cases? An award-winning Spanish team has provided some answers. To find out more, click here.
PET/MRI continues to show clinical promise, but a more coherent strategy for hybrid imaging is required, writes Dr. Arturo Chiti, a nuclear medicine specialist from Milan. He has proposed a four-point plan of action. Click here for the full story.
This letter features only a few of the many articles posted over the past few weeks in the MRI Community. Please scroll through the rest of our coverage below.
![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)
