Paul Davis[email protected]HomeResponsive - Making the most of the new banner ad formatsAugust 5, 2014HomeResponsive - The Power of Landing PagesAugust 5, 2014HomeResponsive - 7 Deadly Sins of Landing Page DesignAugust 5, 2014HomeResponsive - 5 Short Videos to help you improve your eBroadcastsAugust 5, 2014HomeMismatched GoalsJune 29, 2009HomeLost in Translation...February 5, 2008HomeAgents of Change: The new wave of crawlers, spiders and botsMarch 13, 2006HomeDefining your successJune 7, 2005HomeDefining your successJune 5, 2005Industry NewsRich Media: The power of being richMarch 30, 2005Previous PagePage 2 of 3Next PageTop StoriesInterventionalRadiation segmentectomy can be optimized before liver transplantAs of 2023, yttrium 90 (Y-90) radioembolization has become the most frequently used local-regional therapy in the U.S. for bridging patients with HCC to liver transplant.Womens ImagingCardiac devices affect mammography image qualityMRIMRI reveals how hidden muscle fat increases risk to heart, metabolismMRIWhole body MRI, deep-learning can 'map' distribution of fat and muscleSponsor ContentRadiology is Under Pressure
![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&fit=crop&h=167&q=70&w=250)
![A 53-year-old patient (patient number four) with a recurrent pituitary adenoma with extension of a cystic component of disease to the medial temporal lobe apparent on MRI (contoured in blue), and extension of disease to the left sphenoid bone and orbital apex apparent on [68Ga]Ga-DOTA-TATE (contoured in yellow).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/04/pituitary-tumor.QGsEnyB4bU.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
