Dear AuntMinnieEurope Member,
Much has been said and written over the past three decades about the Nobel prize for MRI, but very little discussion has taken place about a potential award for PET imaging.
Prof. Hans Ringertz was closely involved in the judging process for many years at the Karolinska in Stockholm. He's always very careful with his words, so it's surely significant that he spoke at ECR 2022 about a Nobel prize for PET. Find out more in the Molecular Imaging Community.
Much positive energy was evident at ECR 2022, and there was a generally upbeat mood among the estimated 8,000-10,000 attendees in Vienna. However, the worsening conflict in Ukraine cast a long shadow over the congress.
On the second day of the meeting, news broke of the missile attack on an imaging facility in Vinnytsia. Neurologist Dr. Natalya Falshtynska, who was seriously injured in the attack, died in hospital on 19 July, taking the death toll to 25.
In other news, U.K. scientists have published new data on overdiagnosis in breast screening. Their findings deserve a close look in the Women's Imaging Community.
When it comes to breast MRI, does a clinical decision algorithm outweigh reader experience? A highly respected research group from Vienna has addressed this important question in a study published in European Radiology on 19 July. Go to the MRI Community to learn more.
Where access to MRI is limited or a patient has a contraindication to MRI -- for example, due to a hip prosthesis or a pacemaker -- microultrasound can help to detect lesions that are unlikely to be seen on conventional ultrasound, according to Canadian researchers.
Looking ahead, we'll bring you more news and video interviews from ECR 2022. Please watch out for them in the coming weeks.
![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)
