Erik L. Ridley[email protected]ECR 2025Canon highlights ultrasound and CT lung screening at ECR 2025Canon Medical Systems is emphasizing a new multipurpose ultrasound scanner and its lung cancer screening portfolio at ECR 2025.February 28, 2025ECR 2025Sealed MRI magnet, new CT scanner pace GEHC product launches at ECR 2025GE HealthCareGE HealthCare unveiled a new sealed MRI magnet platform and a super-premium CT scanner this year at ECR 2025.February 27, 2025ECR 2025Siemens emphasizes new software offering, stroke careSiemens HealthineersAt ECR 2025, Siemens Healthineers is featuring its mobile stroke unit offering and a new way for customers to access its imaging software.February 26, 2025UltrasoundRoad to ECR 2025: Ultrasound PreviewUltrasound's portability, reliability, sustainability, and versatility will be highlighted at this year's ECR, emphasizing the modality's added value in a wide variety of clinical applications.February 12, 2025Enterprise ImagingWhat were the key market trends at RSNA 2024?Industry specialist Steve Holloway and his squad of analysts spent countless hours on the exhibit floor of McCormick Place during RSNA. He tells us what grabbed his attention.December 9, 2024CTSiemens broadens photon-counting CT portfolioSiemens has introduced a new dual-source scanner and a single-source system as part of its Naeotom Alpha range of photon-counting systems.December 2, 2024AIAI helps out with spotting interval breast cancersA new study from Cambridge in the U.K. has assessed performance characteristics of AI software in identifying and localizing interval breast cancers.September 11, 2024AICan open-source software make the grade in radiology?An open-source large language model can offer comparable performance to proprietary models, a German research group says.August 16, 2024AIChatGPT can provide patients with radiation protection infoChatGPT can answer patient questions about radiation protection for medical imaging exams comparably to websites of radiology institutions, according to research published on 25 June in Radiology.July 4, 2024Clinical NewsWeek in Review: Avoiding MRI exam delays | ChatGPT and radiology reports | AI assesses mortality risk in TAVI patientsQuality improvement initiatives for avoiding MRI exam delays and the use of ChatGPT to produce structured radiology reports were among the top stories this week on AuntMinnieEurope.com.June 20, 2024Previous PagePage 2 of 4Next 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)
