Joint Session of the ESR and EIBIR
Funding the future of research in artificial intelligence (AI) and cardiac imaging: reports from the ESR Research Seed Grant projects
Lectures
ESR/EIBIR - Reliability of cardiothoracic ratio as an estimate of cardiac enlargement compared to gold standard MRI
04:55M. Arzanauskaite, Liverpool / UK
Learning Objectives
1. To learn about the reliability of cardiothoracic ratio as an estimate of cardiac enlargement.
2. To learn about the project progress.
ESR/EIBIR - MRI-assessed myocardial deformation of the systemic right ventricle in patients with transposition of the great arteries: a cross-sectional study
05:05M. Hrabak Paar, Zagreb / HR
Learning Objectives
1. To learn about MRI-assessed myocardial deformation.
2. To learn about the project progress.
ESR/EIBIR - RISK: risk maps using T2 mapping and diffusion MR sequences of the prostate
04:58M. Lenz, Cologne / DE
Learning Objectives
1. To learn about risk maps using T2 mapping and diffusion MR sequences.
2. To learn about the project progress.
ESR/EIBIR - MR-based biomarkers of tumour hypoxia
06:51I. Kurilova, Amsterdam / NL
Learning Objectives
1. To learn about AI-driven MR-based imaging of tumour hypoxia.
2. To learn about the project progress.
ESR/EIBIR - Value of cardiac magnetic resonance imaging derived temporal myocardial strain pattern for non-invasive diagnosis of myocarditis
04:53F. Laqua, Greifswald / DE
Learning Objectives
1. To learn about spatio-temporal cardiac motion patterns.
2. To learn about the project progress.
ESR/EIBIR - Impact of myocardial motion on the relationship between transmitral flow and left ventricular filling rates
05:07C. Reiter, Graz / AT
Learning Objectives
1. To learn about compressed sensing whole-heart 4D-Flow imaging.
2. To learn about the project progress.
ESR/EIBIR - CT-based radiomics analysis to predict malignancy in patients with intraductal papillary mucinous neoplasm (IPMN) of the pancreas
06:57D. Tobaly, Clichy / FR
Learning Objectives
1. To learn about T-based radiomics analysis in cancer.
2. To learn about the project progress.
ESR/EIBIR - Towards automated TNM-staging of NSCLC in FDG-PET/CT: evaluation of combined deep learning strategies
05:56T. Weikert, Basel / CH
Learning Objectives
1. To learn about the integration of an algorithm for automated detection of cancer.
2. To learn about the project progress.