DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Park, Sung Hong | - |
dc.contributor.advisor | 박성홍 | - |
dc.contributor.author | Kim, Bomin | - |
dc.date.accessioned | 2023-06-22T19:32:16Z | - |
dc.date.available | 2023-06-22T19:32:16Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1032729&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308376 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2023.2,[iii, 22 p. :] | - |
dc.description.abstract | One of the fundamental problems in MR image is its slow acquisition time compared to other imaging modalities. Several approaches have been proposed to accelerate MRI, and nowadays deep learning has been showing great promises in this area. However, since k-space line contains different information, which line to acquire (or sampling pattern) plays an important role in MRI reconstruction. Previous works focused on jointly optimizing the sampling pattern and reconstruction network or active sampling. In this work, we propose a novel strategy for determining sampling patterns, named Progressive Sampling Pattern Network (PSP-Net), which progressively optimize subject-common and subject-specific sampling patterns to improve the reconstruction performance with time efficiency. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | MR reconstruction▼aSampling pattern optimization▼aDeep learning | - |
dc.subject | 자기공명영상 재구성▼a샘플링 패턴 최적화▼a딥러닝 | - |
dc.title | PSP-Net: learning the optimal sampling pattern for MR reconstruction via progressive modeling | - |
dc.title.alternative | PSP-Net: 단계적 모델링을 통한 자기공명영상 재구성의 최적 샘플링 패턴 학습 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
dc.contributor.alternativeauthor | 김보민 | - |
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