Kamil Ugurbil, PhD, of the University of Minnesota in Minneapolis will be among the international luminaries speaking at the 13th Ultrahigh Field Magnetic Resonance 2022 meeting, to be held in Berlin on 9 September.
Kamil Ugurbil, PhD. Image courtesy of ISMRM.The meeting will be held at the Max Delbruck Communications Center, and will focus on clinical needs, research promises, and technical solutions.
Ugurbil's talk is called "Progress in Imaging the Human Torso at 10.5 T: Are We There Yet?" and will kick off a scientific session that explores cardiac and body MRI. He will also take part in a panel discussion on the future of ultrahigh field MRI.
To access the full program, go to the event organizers' website. Also, you can follow the meeting on Twitter @BUFF_MRI #UHFsymposium.
![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)
