DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 박성홍 | - |
dc.contributor.author | Lee, Jaa-Yeon | - |
dc.contributor.author | 이자연 | - |
dc.date.accessioned | 2024-07-25T19:30:58Z | - |
dc.date.available | 2024-07-25T19:30:58Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1045786&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/320598 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2023.8,[vi, 37 p. :] | - |
dc.description.abstract | Metal artifacts caused by metallic implants present a significant challenge in accurate diagnosis using MRI. While various techniques have been developed to mitigate these artifacts, their clinical utility is often limited due to long scan times. Among these techniques, Slice Encoding for Metal Artifact Correction (SEMAC) has gained popularity. In this thesis, we propose an artificial neural network approach to accelerate SEMAC, enabling the suppression of metal artifacts in a shorter scan time without compromising image quality. Building upon previous work on jointly optimizing sampling patterns in MRI reconstruction (J-MoDL), we effectively address metal artifacts by simultaneously optimizing multi-contrast MRI and multi-dimensional sampling patterns. By optimizing sampling positions on both in-plane and SEMAC dimensions, we achieve promising results in fast MRI with enhanced metal artifact correction. Our experiments on patients with degenerative spinal diseases, known for severe metal artifacts, demonstrate the model’s exceptional performance in correcting metal artifacts quantitatively and perceptually. Furthermore, we establish the balanced reconstruction quality across multi-contrast MR images by employing a different denoiser named XNet. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | SEMAC▼a다중 MR 이미징▼a딥러닝▼a샘플링 패턴 최적화▼a금속 왜곡물 완화 고속화 | - |
dc.subject | SEMAC▼aMulti-Contrast MRI▼aDeep Learning▼aSampling Pattern Optimization▼aAccelerated Metal Artifact Correction | - |
dc.title | Fast metal artifact correction of multi-contrast MRI by jointly optimizing multi-dimensional sampling pattern with model-based deep learning | - |
dc.title.alternative | 고속 금속 인공물 감소를 위한 다중 대비 자기공명영상의 모델 기반 딥러닝을 통한 다중 디멘션 최적 샘플링 패턴 학습 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
dc.contributor.alternativeauthor | Park, Sung-Hong | - |
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