Cardiff University and Siemens Healthineers have entered a strategic partnership to develop advanced medical technologies, with a focus on MR imaging and in vitro diagnostics.
The alliance builds on a 10-year collaboration between the two, and they will work together on developments in MR hardware, biophysical modeling, and MR acquisition strategies using AI to develop efficient acquisition protocols. These aim to capture intricate "fingerprints" of tissue microstructure in health, developmental processes, aging, and various disease states.
The company and university added that integrating these imaging and biochemical analysis modalities will provide innovative approaches to the diagnosis and treatment of other conditions including cancer and diseases associated with infection and immunity.
The partnership also builds on existing collaborations in the field of clinical laboratory diagnostics, with the university having one onsite scientist from Siemens Healthineers. The Cardiff University Brain Research Imaging Centre is developing a step-change in imaging technology and has leveraged Siemens Healthineers' imaging technology to secure grant income of over 54.5 million pounds.
![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)
