End-to-end neural network for adaptive Rx beamforming for speed of sound heterogeneity compensation음속도 이질성을 보상하기 위한 인공 신경망을 이용한 적응형 수신 빔포밍 방법

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B-mode image, which is the most widely used medical ultrasound image modality, is generated by the delay-and-sum algorithm. The conventional delay-and-sum algorithm is based on the assumption that the target object is composed of a homogeneous substance, and organizes the output image by applying proper delays on received RF signals. However, such an assumption sometimes degrades the image resolution because of the heterogeneity of body tissue. In this paper, we propose a beamforming processor consisting of a speed of sound map reconstructor and an adaptive Rx beamformer to enhance B-mode image performance. Two artificial neural networks are connected end-to-end.
Advisors
Bae, Hyeon-Minresearcher배현민researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2022
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2022.2,[iii, 26 p. :]

URI
http://hdl.handle.net/10203/309925
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=997182&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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