Thesis etd-05082024-154649 |
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Thesis type
Tesi di laurea magistrale
URN
etd-05082024-154649
Thesis title
Deep Learning-based deformable registration of MRI multisequence images for artefact correction
Department
INGEGNERIA DELL'INFORMAZIONE
Course of study
INGEGNERIA BIOMEDICA
Supervisors
.
relatore Prof. Vozzi, Giovanni
relatore Prof. Positano, Vincenzo
relatore Dott. Rolla, Riccardo
relatore Prof. Positano, Vincenzo
relatore Dott. Rolla, Riccardo
Keywords
- deep learning
- interpolation techniques
- medical image registration
- mri artefacts
- supervised learning-based model
Graduation session start date
31/05/2024
Availability
Withheld
Release date
31/05/2094
Abstract (Inglese)
Abstract (Italiano)
Survey on deep learning in medical image registration and MRI artifacts. Implementation of a Supervised Learning-based model for multisequence artifacted MRI images to predict the alignment of input image pairs through the use of displacement vectors. Discussion, analysis, and validation of different types of interpolation techniques to achieve the best registration using quantitative and qualitative indexes.
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