DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Cho, Seungryong | - |
dc.contributor.advisor | 조승룡 | - |
dc.contributor.author | Jeong, Uijin | - |
dc.date.accessioned | 2022-04-21T19:32:26Z | - |
dc.date.available | 2022-04-21T19:32:26Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=963487&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/295503 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 원자력및양자공학과, 2021.8,[iii, 27 p. :] | - |
dc.description.abstract | With the development of flat-panel detector, in X-ray imaging, X-ray scatter causes image quality degradation and incorrect estimation of primary radiation. To reduce or correct scatter artifacts, many kinds of methods have been continuously developed, and among them, the kernel-based scatter correction method has been widely adopted in terms of fast calculation and easy implementation. However, the kernel-based method using pencil beam is less accurate in estimating scatter near the breast boundary in digital breast tomosynthesis, where rotation of the source is added. In this study, we proposed a supervised learning-based scatter correction method using a convolutional neural network in numerical breast phantom with Monte Carlo simulation. Through the proposed method, we confirmed better performance in estimation of scatter than conventional kernel-based method and robustness according to breast density. We expect that the proposed method can be extended to clinical data in the future. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | X-ray scatter▼aDigital breast tomosynthesis▼aMonte Carlo simulation▼aConvolutional neural network | - |
dc.subject | 엑스선 산란▼a디지털 유방 단층영상합성▼a몬테카를로 시뮬레이션▼a합성곱 신경망 | - |
dc.title | Development of scatter correction method using convolutional neural network in digital breast tomosynthesis | - |
dc.title.alternative | 디지털 유방 단층영상합성에서의 합성곱 신경망을 이용한 산란 보정 방법 개발 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :원자력및양자공학과, | - |
dc.contributor.alternativeauthor | 정의진 | - |
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