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 StoriesMRIWhole body MRI, deep-learning can 'map' distribution of fat and muscleThe map shows that the quality and amount of skeletal muscle, not just visceral fat, are strong predictors of disease.MRIPodcast: Twisted litigation of fatal MRI accident at open MRI centerBreastAI triage flags half of screen-detected cancers in top 2% of scansRadiation Oncology/TherapyWill upright radiotherapy become a new standard?Sponsor 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.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=400&q=70&w=600)
![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)
