The European Society of Radiology (ESR), in cooperation with the European Institute for Biomedical Imaging Research (EIBIR), is creating the ESR Research Seed Grant initiative for 2022 and is calling for applications.
The program aims to stimulate and provide funding for innovative projects and pilot studies that will lead to larger studies and further funding applications. Funding is for up to three grantees for two years. The maximum funding amount for each project is 14,000 euros.
The call for proposals is open to researchers of all ages working in radiology departments in an Eastern European, Central Asian, or African country. Applicants must be members of the ESR. The project proposed should not have previously received funding from another grant or be part of another ongoing project application.
Project proposals must align with the ESR Research Seed Grant Global thematic areas. These include enhancing quality in radiological imaging and interventional radiology and value-based radiology. Focus will be placed on, but not limited to, research in ultrasound, CT, and conventional imaging. For further details, visit the EIBIR website.
![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)
