![Loading...](https://link.springer.com/static/c4a417b97a76cc2980e3c25e2271af3129e08bbe/images/pdf-preview/spacer.gif)
-
Chapter and Conference Paper
Characterizing Myocardial Ischemia and Reperfusion Patterns with Hierarchical Manifold Learning
We aim at better understanding the mechanisms of ischemia and reperfusion, in the context of acute myocardial infarction. For this purpose, imaging and in particular magnetic resonance imaging are of great val...
-
Chapter and Conference Paper
Reinforcement Learning for Active Modality Selection During Diagnosis
Diagnosis through imaging generally requires the combination of several modalities. Algorithms for data fusion allow merging information from different sources, mostly combining all images in a single step. In...
-
Chapter and Conference Paper
Influence of Morphometric and Mechanical Factors in Thoracic Aorta Finite Element Modeling
Evaluation of mechanical properties from thoracic aorta finite element (FE) modeling can help to better stratify patients in need of intervention. This paper assesses the influence of various factors in the co...
-
Chapter and Conference Paper
Hierarchical Multi-modality Prediction Model to Assess Obesity-Related Remodelling
The diagnosis of cardiovascular illnesses uses multiple modalities in order to obtain a complete and as robust as possible assessment of the heart. However, when addressing distinct pathologies, not all inform...
-
Chapter and Conference Paper
Systematic Study of Joint Influence of Angular Resolution and Noise in Cardiac Diffusion Tensor Imaging
Diffusion tensor imaging (DTI) is a promising imaging technique to non-invasively study diffusion properties and fiber structures of myocardial tissues. Previous studies have investigated the influence of nois...
-
Chapter and Conference Paper
Investigation of the Impact of Normalization on the Study of Interactions Between Myocardial Shape and Deformation
Myocardial shape and deformation are two relevant descriptors for the study of cardiac function and can undergo strong interactions depending on diseases. Manifold learning provides low dimensional representat...
-
Chapter and Conference Paper
Population-Based Personalization of Geometric Models of Myocardial Infarction
We propose a strategy to perform population-based personalization of a model, to overcome the limits of case-based personalization for generating virtual populations from models that include randomness. We for...