Philip Ward[email protected]Editor's NoteAuntMinnieEurope.com Advanced Visualization Insider3D printing has developed an important medical role in war-torn regions, including Gaza and surrounding countries.December 11, 2025Advanced Visualization3D-printed prostheses bring hope in conflict zones3D-printing allows for faster, lighter, more affordable, and highly customized prosthetic production, and the technology can help patients regain mobility.December 11, 2025MRIAI comes under scrutiny in Swedish prostate MRI studyAn AI system trained in a screening population has lower specificity than radiologists using PI-RADS v2, but considerable care is vital, researchers from Gothenburg reported in European Radiology on 8 December.December 9, 2025Editor's NoteWeek in Review: Europeans excel at RSNA | Buttock shape & diabetes | McGinty on sustainabilityResearch from Spain, Italy, U.K., and France attracted considerable interest at this week’s RSNA meeting in Chicago.December 5, 2025MRIFrance gets set to shine at RSNA 2025An abundance of Gallic charm and research excellence promise to light up McCormick Place in Chicago on Tuesday.December 1, 2025Editor's NoteWeek in Review: Survey of radiology trainees | McCoubrie on MDTs | 10 tips on how to go greenThe findings of an important new survey of nearly 200 radiology trainees from 35 Italian teaching centers deserve close inspection.November 28, 2025Editor's NoteWeek in Review: Art and radiology | Manslaughter case in Rome | ESR to open Brussels officeArt and radiology may seem like very different fields, but they can intersect in intriguing ways, particularly in medical imaging and the visualization of complex clinical data.November 21, 2025Editor's NoteAuntMinnieEurope.com Ultrasound InsiderProf. Harun Yildiz has a love of not only pediatric radiology and ultrasound but also art. He’s spoken about his philosophy and approach to art and shared some of his images with us.November 20, 2025UltrasoundArt and radiology: Seeing beauty and value in images“Radiological illustrations are more than medical images. They teach!” That’s the view of Dr. Harun Yildiz from Bursa, Turkey. In a Q&A interview, he elaborates on his passion for art.November 20, 2025CTGianfranco Gualdi breaks his silenceProf. Gianfranco Gualdi, the senior radiologist from Rome who faces manslaughter charges over alleged malpractice, gave a presentation at a meeting held on 14 November. A video clip of him has appeared on social media.November 18, 2025Previous PagePage 3 of 31Next PageTop StoriesMolecular ImagingHow AI can help with PET/CT guideline deviationsResearchers have found a gap between what international PET/CT guidelines recommend and what actually happens in clinical practice.MRIMRI reveals how hidden muscle fat increases risk to heart, metabolismMolecular ImagingThe EANM joins EMUC26 and pushes its policy agenda in BrusselsMRIWhole body MRI, deep-learning can 'map' distribution of fat and muscleSponsor ContentTurning Innovation into Everyday Efficiency
![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=167&q=70&w=250)
