DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Bae, Hyeon-Min | - |
dc.contributor.advisor | 배현민 | - |
dc.contributor.author | Oh, Seok-Hwan | - |
dc.date.accessioned | 2023-06-23T19:33:52Z | - |
dc.date.available | 2023-06-23T19:33:52Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1030654&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/309129 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2023.2,[v, 42 p. :] | - |
dc.description.abstract | In this dissertation, a single-probe ultrasonic imaging system that captures multi-variable quantitative profiles is presented. As pathological changes cause biomechanical property variation, quantitative imaging has great potential for lesion characterization. The proposed system simultaneously extracts four clinically informative quantitative biomarkers, such as the speed of sound, attenuation coefficient, effective scatter density, and effective scatter radius, in real-time using a single scalable neural network. A b-mode contents-aware quantitative imaging scheme is proposed and enhances reconstruction accuracy by applying quantitative style information to structurally accurate b-mode images. In real-time medical imaging, spatio-temporal consistency is an important research challenge, as the temporal property variation hinders the accurate diagnosis of a lesion. Therefore, a residual quantitative imaging network is proposed and enhances spatio-temporal consistency of generated images by formulating a feed-back loop and inferencing residuals of adjacent frames. The performance of the proposed system was evaluated through numerical simulations and phantom, ex vivo and in-vivo measurements. In the phantom and ex vivo experiments, the SQI-Net demonstrated the classification of cyst, and benign- and malignant-like inclusions through a comprehensive analysis of four reconstructed images. In an in-vivo study, the proposed system demonstrates 0.907 area under curvature in the differential diagnosis of breast cancer | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Quantitative ultrasound imaging▼aMedical ultrasound▼aBreast cancer diagnosis▼aDeep neural network | - |
dc.subject | 정량적 초음파 영상화▼a의료용 초음파▼a유방암 진단▼a심층 신경망 기법 | - |
dc.title | Multi-variable quantitative ultrasound imaging for differential breast cancer diagnosis | - |
dc.title.alternative | 인공지능 기반의 다변수 정량 초음파 영상화를 통한 유방암 진단 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 오석환 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.