DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Chang-Ock | - |
dc.contributor.advisor | 이창옥 | - |
dc.contributor.author | Hwang, Dae Gwan | - |
dc.date.accessioned | 2021-05-13T19:35:00Z | - |
dc.date.available | 2021-05-13T19:35:00Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=911443&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/284813 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 수리과학과, 2020.2,[iii, 14 p. :] | - |
dc.description.abstract | Metal artifacts in CT image disturb accurate diagnosis. To date, many iterative and direct reconstruction methods have been developed to reduce metal artifacts. In this thesis, we address convolutional neural networks (CNN) based computed tomography (CT) image reconstruction. We verify that our algorithm can reduce metal artifacts in the image similar to those used in learning. However, if our algorithm encounters a new image that is not in the class of images for learning, it cannot beat the existing model based reconstruction results. Although our algorithm does not work properly when meeting new images, better model based reconstruction input data produces better output image. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Computed tomography (CT)▼ametal artifact reduction▼aCNN▼asinogram surgery▼asinogram | - |
dc.subject | 컴퓨터 단층 촬영▼a금속음영 제거▼a합성곱 신경망▼a사이노그램 수술▼a사이노그램 | - |
dc.title | Metal artifact reduction in X-ray CT based on CNN using model based reconstruction results | - |
dc.title.alternative | 모델기반 복원 결과를 사용한 합성곱 신경망에 기반한 X-ray CT의 금속음영 제거 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :수리과학과, | - |
dc.contributor.alternativeauthor | 황대관 | - |
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