Isabelle Wegmann Hachette[email protected]Ultrasound3D/4D ultrasound matures as it shows clinical valueInitially a technology used only by early adopters, 3D/4D ultrasound has indeed come of age, buoyed by mounting evidence of its diagnostic utility in a variety of clinical applications. Today, 3D/4D ultrasound is increasingly being used by healthcare institutions around the world.October 18, 2016Ultrasound3D/4D ultrasound imaging: Coming of ageAlthough high-performance 3D/4D technology has been available in ultrasound for years, it hasn't routinely been used in all clinical practices. That may change in 2013, however, thanks to a number of key trends that are expected to drive widespread adoption.January 3, 2013Page 1 of 1Top StoriesAIImaging fellow tries vibe coding to solve radiology challengesVibe coding outside the radiology department led to exploring real-world use cases.ISMRM 2026ISMRM annual meeting in Cape Town to highlight South African 'Ubuntu'InterventionalRadiation segmentectomy can be optimized before liver transplantWomens ImagingCardiac devices affect mammography image qualitySponsor ContentThe Wedding Was Easy. The Marriage Is the Work.
![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.auntminnie.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)
