Dr. Lars ThoreliusClinical NewsCan ultrasound benefit from the rest of radiology?In part II of his two-part series on improving the clinical practice of ultrasound, Dr. Lars Thorelius from University Hospital in Linköping, Sweden, describes how his department has applied a radiological attitude and workflow to the modality, involving standardized scanning and full documentation, and the positive effects that have resulted.March 7, 2007Page 1 of 1Top StoriesHomeRadiology's utilization problem sustainability still missedTurning off scanners helps, but not ordering unnecessary ones in the first place helps more. One Basel radiologist put the benefit into numbers.CTWelding fumes linked to distinct lung CT changes, functionMolecular ImagingHow AI can help with PET/CT guideline deviationsMRIMRI reveals how hidden muscle fat increases risk to heart, metabolismSponsor 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&dpr=2&fit=crop&h=167&q=70&w=250)
