Super-resolution in brain Diffusion Weighted Imaging (DWI)
Abstract. Diffusion Weighted (DW) imaging has proven to be useful at analysing brain architecture as well as at establishing brain tract organization and neuronal connectivity. However, an actual clinical use of DW images is currently limited by a series of acquisition artifacts, among them the part...
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| Formato: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2014 |
| País: | Colombia |
| Recursos: | Universidad Nacional de Colombia |
| Repositorio: | Repositorio UN |
| Idioma: | español |
| OAI Identifier: | oai:repositorio.unal.edu.co:unal/54123 |
| Acesso em linha: | https://repositorio.unal.edu.co/handle/unal/54123 http://bdigital.unal.edu.co/48958/ |
| Access Level: | acceso abierto |
| Palavra-chave: | 61 Ciencias médicas; Medicina / Medicine and health 62 Ingeniería y operaciones afines / Engineering Super-resolution Image processing Diffusion Weighted Imaging Shearlet transform Multiscale representation Sparse representation Super-resoluci´on, Imágenes de Diffusión Ponderada (DWI) Transformada Shearlet Representaciones multi-escala Representaciones sparse |
| Resumo: | Abstract. Diffusion Weighted (DW) imaging has proven to be useful at analysing brain architecture as well as at establishing brain tract organization and neuronal connectivity. However, an actual clinical use of DW images is currently limited by a series of acquisition artifacts, among them the partial volume effect (PVE) that may completely alter the spatial resolution and therefore the visualization of microanatomical details. In this work, a new superresolution method will be presented, taking advantage of the redundant structural patterns that shape the brain. The proposed method couples low-high resolution information and explores different directional spaces that might exploit the spectral content of the DW images. A comparison of this proposal with a classical image interpolation method demostrates an improvement of about 3 dB when using the typical PSNR. |
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