dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Externe beoordelaar - External assesor, | |
dc.contributor.author | Noordman, Stan | |
dc.date.accessioned | 2021-12-14T00:00:17Z | |
dc.date.available | 2021-12-14T00:00:17Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/288 | |
dc.description.abstract | The 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.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | AI-based reconstruction of an image given an MR acquisition with very few k-space data samples | |
dc.title | Current Issues in Deep Learning for Undersampled Image Reconstruction | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | MRI acceleration; deep learning; image reconstruction | |
dc.subject.courseuu | Medical Imaging | |
dc.thesis.id | 1270 | |