Fecha
2024-06-01
Editor/a
Director/a
Coordinador/a
Prologuista
Revisor/a
Ilustrador/a
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editorial
Citas
0 citas en 
0 citas en 
Resumen
Diffusion Magnetic Resonance Imaging (dMRI) is the main non-invasive technique for understanding brain structural connectivity. However, given its inherent low signal-to-noise ratio, thermal noise particularly affects this modality and its downstream analysis. To overcome this challenge, several denoising methods have been developed over the years. Based on previous works, and as an extension, we assess and characterise here the impact of some of the selfsupervised Deep Learning approaches recently developed for denoising and show comparisons with alternative traditional approaches in both synthetic and real brain datasets.
Descripción
Categorías UNESCO
Palabras clave
Diffusion MRI, denoising, self-supervised learning, diffusion models, Generative models, MPPCA, NLM, Patch2Self, DDM2, Noise floor, uncertainty
Citación
Rapado Morales, Dayris. Trabajo Fin de Máster: Exploring self-supervised learning denoising in diffusion MRI - A characterisation of their impact under different noise regimes. Universidad Nacional de Educación a Distancia (UNED), 2024
Centro
E.T.S. de Ingeniería Informática
Departamento
Inteligencia Artificial