Dr. Matthias Dietzel[email protected]Residents/FellowsDr. Adrian Dixon, breast experts & joggers excel at DRKHAMBURG - "Sherlock Homes is nothing compared to a radiologist," Dr. Adrian Dixon pointed out in an interview with the congress newspaper at last week's German Radiology Congress (DRK) in Hamburg. Dixon received honorary membership of the German Radiological Society at the meeting.May 19, 2015Clinical NewsIntervention, youth, breast MRI dominate agenda at DRKThe latest developments in breast MRI and interventional radiology were among the highlights of the 95th congress of the German Society of Radiology (DRK), and Drs. Christiane Kuhl and Thomas Helbich were two key speakers, writes AuntMinnieEurope.com's editorial adviser Dr. Matthias Dietzel.June 3, 2014Molecular ImagingWhite men, breast MRI come under scrutiny at DRK 2013After four days of intensive teaching, networking, updating, and discussions of the latest news, the German radiological congress, the DRK, is over. Die weissen Männer caused a stir, and the breast MRI sessions were particularly strong, writes AuntMinnieEurope.com's editorial adviser Dr. Matthias Dietzel.June 3, 2013Clinical NewsReport from DRK 2011: Highlights from breast researcherFind out what you missed at the showcase meeting in Hamburg: A breast imaging researcher from Jena, Germany, outlines his highlights from the German national congress (Deutscher Röntgenkongress, DRK).June 6, 2011Page 1 of 1Top StoriesMolecular ImagingHow AI can help with PET/CT guideline deviationsResearchers have found a gap between what international PET/CT guidelines recommend and what actually happens in clinical practice.MRIMRI reveals how hidden muscle fat increases risk to heart, metabolismMolecular ImagingThe EANM joins EMUC26 and pushes its policy agenda in BrusselsMRIWhole 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.auntminnieeurope.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.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)
