Diagnosis of cardiovascular disease would be impossible without imaging, according to comments from Gilbert Habib, president-elect of the European Association of Cardiovascular Imaging (EACVI), at EuroEcho-Imaging 2013 in Istanbul.
Habib added that overall knowledge of different imaging techniques allows clinicians to select the most appropriate strategy for individual patients.
The key message at EuroEcho this year is that several imaging techniques are now available, Habib told attendees at the four-day forum of scientific sessions.
In addition, recent advances in minimally invasive percutaneous interventions would not have been possible without parallel developments in cardiac imaging, Habib said, which allows for the precise guidance of catheters, optimization of results, and detection of complications.
Young investigators presented their work at this meeting and were awarded for their presentations. One noteworthy study came from Dr. Maja Cikes from Zagreb, Hungary, on the effect of afterload on left ventricular function, as evaluated by myocardial deformation.
![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)
