(2020) PhD (c) in Engineering Science, mention in Electric Engineering, Universidad de Concepción, Chile.
Thesis project: Recognition of brain connectivity patterns using diffusion Magnetic Resonance imaging.
Most brain Magnetic Resonance imaging studies require at some stage to use image registration and normalization techniques. In general, these methods align a large number of subjects with an average brain template by modifying the 3D spatial coordinates and subsequently infer inter- and intra-individual results. However, the challenge of aligning multiple images is still an ill-posed problem, where the complexity of the brain architecture prevents to represent the brain structure of all subjects with a single brain. In addition, with the existence of the HCP (Human Connectome Project) database, which contains around 1200 high-quality subjects, the need to propose a new strategy that enables a more precise alignment is imminent, adapting to the characteristics of the population under analysis. In this research, we propose an algorithm to identify multiple brain patterns based on anatomical connectivity. The constructed multiple brain templates could be used to improve tractography analysis methods, such as clustering and segmentation, as well as image registration processes.
(2013) Telecommunications and Electronics Engineering, Universidad de Pinar del Río, Pinar del Río, Cuba.
Areas of Interest
– Analysis of Diffusion MRI
– Computational Neuroscience
– Cognitive Neuroscience
– Biomedical Signal processing
– Machine Learning