(A) deep convolutional neural network for Rx-subsampled B-mode ultrasound imaging reconstruction = 리시버-다운샘플링된 B-Mode 초음파 영상 복원을 위한 딥 컨볼루션 뉴럴 네트워크

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There is an increasing demand for applications of B-mode ultrasound imaging, such as ultra-fast ultrasound or portable ultrasound. In order to meet such this demand, the technique of reconstructing high quality images using a limited number of RF data is needed. The existing methods use hardware changes of the ultrasound device or algorithms having the high complexity and large computations. Therefore, there are hardware limitations that it can not be applied to other ultrasound devices and software limitations that reconstruction time is long. To overcome these technical limitations, we propose the method of Rx-subsampling and learning receiver(Rx)-scanline(SC) 2-D data planes with a deep convolutional neural network. Because Rx-SC 2-D data planes represent the redundancy, the proposed method can interpolate Rx-subsampled Rx-SC 2-D data planes. In this paper, the data acquired directly by using the ultrasound device is learned by the deep convolutional neural network, and images with high quality are reconstructed at a high speed. In addition, by applying the same neural network irrespective of the transducers or the scanned regions, we show that the proposed method has the universality.
Advisors
Ye, Jong Chulresearcher예종철researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2018.2,[iv, 39 p. :]

Keywords

Deep convolutional neural network▼aB-mode ultrasound imaging▼aRx-SC coordinating 2-D data plane▼aredundancy▼auniversality; 딥 컨볼루션 뉴럴 네트워크▼aB-모드 초음파 영상▼a리시버-스캔라인 좌표의 2-D 데이터 평면▼a중복성▼a보편성

URI
http://hdl.handle.net/10203/266172
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=733824&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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