Dear AuntMinnieEurope Member,
Dr. Adrian Brady is due to take over as president of the European Society of Radiology next month. What's occupying his mind? What are his areas of special interest?
Brady has shared his thoughts in an entertaining podcast, and we've summarized the contents for you in this week's top article. Find out more in the CT Community.
Prostate cancer staging is a rapidly evolving area, and PET/MRI is generating particular excitement. The results of a new study from the IRCCS Ospedale San Raffaele in Milan have been unveiled. Given the complex nature of research like this, it's understandable that the sample size was relatively small, but the findings still deserve a close look in our Molecular Imaging Community.
Another hot topic in hybrid imaging is PET/CT's role in tracking COVID-19 infection. At last week's Society of Nuclear Medicine and Molecular Imaging (SNMMI) annual meeting, a team from India elaborated on how F-18 FDG PET/CT can help to evaluate post-COVID lung disease.
German groups appear to win the SNMMI's Image of the Year award on a regular basis, and investigators from Hannover Medical School collected the 2022 prize.
They used a gallium-68-labeled (Ga-68) radiotracer designed to bind to fibroblast cells (Ga-68 FAPI-46) and performed fibroblast-activation protein inhibitor (FAPI)-PET imaging in 35 patients several days after they experienced a heart attack. The team found that the FAPI-PET signal in injured heart muscle predicted heart dysfunction in patients more than four months later.
Last but definitely not least, MRI researchers from Imperial College London believe they've made significant progress in identifying early-stage Alzheimer's patients for clinical trials of new drug treatments or lifestyle changes. Also, their technique might make it simpler and quicker to make a diagnosis. Go to the MRI Community.
![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)
