Document type

Bachelor thesis

Publication date

Publication license

cc-by-nc-nd (c) autor Ariadna Izquierdo Gómez, 2024
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/213011

Pipeline for pre-processing and processing of diffusion-weighted images with single phase-encoding

Journal Title

Director/Tutor

Journal ISSN

Volume Title

Related resource

Abstract

Diffusion-weighted magnetic resonance imaging (DWI) captures the movement of water molecules in brain tissue, which is particularly useful for characterizing white matter, where diffusion is mostly anisotropic due to axonal myelination. To fully determine its structure, diffusion tensor imaging (DTI) can be computed, from which fractional anisotropy (FA) maps are extracted, indicating the directionality of water molecule diffusion. Additionally, tractographies, which approximate white matter tracts, are useful for understanding the brain’s structural integrity. DWI data tends to be highly distorted due to its acquisition method, making pre-processing extremely important. Although many pre-processing software and pipelines focus on opposite-phase encoding acquisitions, single-phase acquisitions are often neglected. This study aims to address these single-phase sequences to determine the best pipeline for image correction and the subsequent extraction of FA maps and tractographies, utilizing FSL, MRtrix, and DIPY. To test the developed pipelines, the analysis of a sample of 75 subjects, including patients with chronic HIV and controls, will be performed, to identify any significant differences between the two groups.

Description

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2023-2024. Tutor/Director: Roser Sala Llonch ; Director: Gemma Monté Rubio

Citation

Citation

IZQUIERDO GÓMEZ, Ariadna. Pipeline for pre-processing and processing of diffusion-weighted images with single phase-encoding. [consulted: 14 of June of 2026]. Available at: https://hdl.handle.net/2445/213011

Export metadata

JSON - METS

Share record