| 14-01-2020 |
Cuantificación de remodelamiento en pacientes con tetralogía de Fallot
1 de cada 2000 niños nace con tetralogía de Fallot, una combinación de 4 serios defectos congénitos en el corazón. Para corregir estas deficiencias, se realizan una serie de operaciones, a las cuales el corazón responde remodelándose, adaptando su forma y función. Sin embargo, la manera en que el corazón responde no está caracterizada completamente. El objetivo de esta investigación es cuantificar localmente cómo se adapta el corazón a las sucesivas cirugías. Para esto utilizaremos imágenes de resonancia magnética y el método de elementos finitos. Esta investigación permitirá el desarrollo de modelos predictivos que ayuden a planificar y optimizar las cirugías.
Prerequisitos:
no tiene.
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 7/10 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 14-01-2020 |
Quantification of remodeling in patients with tetralogy of Fallot
1 in 2000 kids are born with tetralogy of Fallot, a combination of 4 serious congenital heart defects. To overcome these problems, the patient undergoes a series of surgeries and the heart responds by remodeling, adapting its form and function. However, the way this adaptation occurs is not fully characterized. The goal of this study is to locally quantify the remodeling of the heart in response to these surgeries. We will magnetic resonance images and the finite element method. This research will enable the development of predictive models to help the planning and optimization of the current surgeries.
Prerequisites:
None.
Evaluation method: Nota 1-7, with 7/10 available vacants |
Mentor(s): Open in the plataform |
| 07-01-2020 |
Graph neural networks for unstructured data in cardiovascular disease
Traditional deep learning approaches rely on structured data, such as images, to make predictions. However, there are cases where the data is unstructured, such as the geometry of the heart. This type of information can be represented with graphs. In this study, we will develop a novel type of neural network that can operate on these graphs and make predictions about cardiovascular diseases. Evaluation method: Nota 1-7, with -1/4 available vacants |
Mentor(s): Open in the plataform |