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dc.contributor.author 이문환 -
dc.contributor.author 황재윤 -
dc.date.accessioned 2021-06-10T20:03:15Z -
dc.date.available 2021-06-10T20:03:15Z -
dc.date.created 2021-04-01 -
dc.date.created 2021-04-01 -
dc.date.created 2021-04-01 -
dc.date.created 2021-04-01 -
dc.date.created 2021-04-01 -
dc.date.created 2021-04-01 -
dc.date.issued 2021-03 -
dc.identifier.citation Journal of the Acoustical Society of Korea, v.40, no.2, pp.169 - 175 -
dc.identifier.issn 1225-4428 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/13702 -
dc.description.abstract 최근 입자 조작, 신경 자극 등을 위해 초음파 홀로그램과 그 응용에 대해 연구가 활발히 되고 있다. 하지만홀로그램을 생성할 송신 신호 위상의 결정은 이전의 시간 소모적인 반복 최적화 방법에서 크게 벗어나지 않고 있다. 이에 본 연구에서는 광학 홀로그램 생성을 위해 활용된 바 있는 딥러닝 기법을 초음파 홀로그램 생성을 위해 적용하여소개한다. U-Net을 기반으로 알고리즘을 구성하였으며 원 모양의 데이터셋에 대해 학습하고 영어 알파벳에 대해 평가함으로써 그 일반화 가능성을 검증하였다. 또한 시뮬레이션을 통해 기존 알고리즘과 계산속도, 정확도, 균일도 측면에서 비교하였다. 결과적으로 정확도와 균일도는 기존에 비해 다소 떨어지지만 계산속도가 약 190배 빨라졌다. 따라서, 이 결과를 통해 딥러닝 기반 초음파 홀로그램 생성 알고리즘은 기존 방법보다 초음파 홀로그램을 빠르게 형성할 수 있는 것을 확인할 수 있었다. Recently, an ultrasound hologram and its applications have gained attention in the ultrasound research field. However, the determination technique of transmit signal phases, which generate a hologram, has not been significantly advanced from the previous algorithms which are time-consuming iterative methods. Thus, we applied the deep learning technique, which has been previously adopted to generate an optical hologram, to generate an ultrasound hologram. We further examined the Deep learning-based Holographic Ultrasound Generation algorithm (Deep-HUG). We implement the U-Net-based algorithm and examine its generalizability by training on a dataset, which consists of randomly distributed disks, and testing on the alphabets (A-Z). Furthermore, we compare the Deep-HUG with the previous algorithm in terms of computation time, accuracy, and uniformity. It was found that the accuracy and uniformity of the Deep-HUG are somewhat lower than those of the previous algorithm whereas the computation time is 190 times faster than that of the previous algorithm, demonstrating that Deep-HUG has potential as a useful technique to rapidly generate an ultrasound hologram for various applications. -
dc.language Korean -
dc.publisher Acoustical Society of Korea -
dc.title 딥러닝 기반 초음파 홀로그램 생성 알고리즘 개발 -
dc.title.alternative Development of deep learning-based holographic ultrasound generation algorithm -
dc.type Article -
dc.identifier.doi 10.7776/ASK.2021.40.2.169 -
dc.identifier.wosid 000659314900009 -
dc.identifier.scopusid 2-s2.0-85108249652 -
dc.type.local Article(Overseas) -
dc.type.rims ART -
dc.description.journalClass 1 -
dc.citation.publicationname Journal of the Acoustical Society of Korea -
dc.identifier.kciid ART002699558 -
dc.contributor.localauthor 이문환 -
dc.contributor.localauthor 황재윤 -
dc.contributor.nonIdAuthor 이문환 -
dc.identifier.citationVolume 40 -
dc.identifier.citationNumber 2 -
dc.identifier.citationStartPage 169 -
dc.identifier.citationEndPage 175 -
dc.identifier.citationTitle Journal of the Acoustical Society of Korea -
dc.description.isOpenAccess Y -
dc.subject.keywordAuthor 초음파 홀로그램 -
dc.subject.keywordAuthor 딥러닝 -
dc.subject.keywordAuthor 위상 검색 -
dc.subject.keywordAuthor Ultrasound hologram -
dc.subject.keywordAuthor Deep learning -
dc.subject.keywordAuthor U-Net -
dc.subject.keywordAuthor Phase retrieval -

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