Brian Casey and Cynthia Keen[email protected]CTFDA hearings rise above medical radiation rhetoricDespite a few distractions, the U.S. Food and Drug Administration's hearings this week on medical radiation dose occurred in a constructive environment and represent the agency's serious intentions to resolve the dose dilemma, according to participants at the hearings.March 30, 2010Product NewsPhilips touts MRI advances, CT dose reduction at RSNACHICAGO - New clinical applications in MRI, dose reduction programs in CT and interventional radiology, and continuing progress in its new women's health division are among the highlights in the RSNA booth of Philips Healthcare at this week's meeting.December 1, 2009Industry NewsPhilips adds 128-slice CT, 3-tesla MRI, flat-panel surgical C-armCHICAGO - Philips Healthcare is using its RSNA appearance to feature a new 128-slice CT scanner, as well as a 3-tesla MRI system. The company is also discussing a prototype PET/MRI system and a surgical C-arm based on flat-panel digital technology.December 2, 2008Page 1 of 1Top 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 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=167&q=70&w=250)
![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)
