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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorExterne beoordelaar - External assesor,
dc.contributor.authorNoordman, Stan
dc.date.accessioned2021-12-14T00:00:17Z
dc.date.available2021-12-14T00:00:17Z
dc.date.issued2021
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/288
dc.description.abstractThe subject matter is deep learning-based image reconstruction using undersampled k-space. In this literature review, I am exploring the bottlenecks that are preventing a major breakthrough in this field. I aimed to differ from the "average" literature review on this subject, which are primarily aimed at researchers. I intend for this piece to be more accessible to a broader audience. A piece of criticism I wanted to highlight in particular, are the weak quantitative metrics used in many/all papers reviewed.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectAI-based reconstruction of an image given an MR acquisition with very few k-space data samples
dc.titleCurrent Issues in Deep Learning for Undersampled Image Reconstruction
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsMRI acceleration; deep learning; image reconstruction
dc.subject.courseuuMedical Imaging
dc.thesis.id1270


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