[논문]Unsupervised CT Metal Artifact Learning Using Attention-Guided β-CycleGAN

Lee, Junghyun; Gu, Jawook; Ye, Jong Chul

doi:10.1109/tmi.2021.3101363

[해외논문] Unsupervised CT Metal Artifact Learning Using Attention-Guided β-CycleGAN

IEEE transactions on medical imaging, v.40 no.12, 2021년, pp.3932 - 3944

Lee, Junghyun (Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea) , Gu, Jawook (Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea) , Ye, Jong Chul (Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea)

Abstract ▼ AI-Helper

Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT). With the advance of deep learning approaches for image reconstruction, various deep learning methods have been suggested for metal artifact reduction, among which supervised learning methods are most popular. However, matched metal-artifact-free and metal artifact corrupted image pairs are difficult to obtain in real CT acquisition. Recently, a promising unsupervised learning for MAR was proposed using feature disentanglement, but the resulting network architecture is so complicated that it is difficult to handle large size clinical images. To address this, here we propose a simple and effective unsupervised learning method for MAR. The proposed method is based on a novel $\beta $-cycleGAN architecture derived from the optimal transport theory for appropriate feature space disentanglement. Moreover, by adding the convolutional block attention module (CBAM) layers in the generator, we show that the metal artifacts can be more focused so that it can be effectively removed. Experimental results confirm that we can achieve improved metal artifact reduction that preserves the detailed texture of the original image.

참고문헌 (39)

Koo, Terry K., Li, Mae Y.. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of chiropractic medicine, vol.15, no.2, 155-163.

상세보기
Yan, Ke, Wang, Xiaosong, Lu, Le, Summers, Ronald M.. DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. Journal of medical imaging, vol.5, no.3, 1-.

상세보기
10.1109/ICCV.2017.304
Lim, Sungjun, Park, Hyoungjun, Lee, Sang-Eun, Chang, Sunghoe, Sim, Byeongsu, Ye, Jong Chul. CycleGAN With a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry. IEEE transactions on computational imaging, vol.6, 1127-1138.

상세보기
arXiv 1701 07875 Wasserstein GAN arjovsky 2017
arXiv 1312 6114 Auto-encoding variational Bayes kingma 2013
Wang, Zhou, Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.13, no.4, 600-612.

상세보기
10.1109/CVPR.2017.632
Ye, Jong Chul, Han, Yoseob, Cha, Eunju. Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems. SIAM journal on imaging sciences, vol.11, no.2, 991-1048.

상세보기
Proc Adv Neural Inf Process Syst Improved training of Wasserstein GANs gulrajani 2017 5767
10.1007/978-3-030-00928-1_1
Zhang, Yanbo, Yu, Hengyong. Convolutional Neural Network Based Metal Artifact Reduction in X-Ray Computed Tomography. IEEE transactions on medical imaging, vol.37, no.6, 1370-1381.

상세보기
10.1109/CVPR.2019.01076
Proc Adv Neural Inf Process Syst Generative adversarial nets goodfellow 2014 2672
10.1109/ICCV.2017.244
10.1007/978-3-540-71050-9
Peyré, Gabriel, Cuturi, Marco. Computational Optimal Transport: With Applications to Data Science. Foundations and trends in machine learning, vol.11, no.5, 355-607.

상세보기
Sim, Byeongsu, Oh, Gyutaek, Kim, Jeongsol, Jung, Chanyong, Ye, JongChul. Optimal Transport Driven CycleGAN for Unsupervised Learning in Inverse Problems. SIAM journal on imaging sciences, vol.13, no.4, 2281-2306.

상세보기
Proc ICLR $\beta$ -VAE: Learning basic visual concepts with a constrained variational framework higgins 2017 2 6
Itti, L., Koch, C., Niebur, E.. A model of saliency-based visual attention for rapid scene analysis. IEEE transactions on pattern analysis and machine intelligence, vol.20, no.11, 1254-1259.

상세보기
arXiv 1805 08318 Self-attention generative adversarial networks zhang 2018
10.1148/radiology.164.2.3602406
arXiv 1806 07185 Mixed batches and symmetric discriminators for GAN training lucas 2018
Zhao, S., Robeltson, D.D., Wang, G., Whiting, B., Bae, K.T.. X-ray CT metal artifact reduction using wavelets: an application for imaging total hip prostheses. IEEE transactions on medical imaging, vol.19, no.12, 1238-1247.

상세보기
Meyer, Esther, Raupach, Rainer, Lell, Michael, Schmidt, Bernhard, Kachelrieß, Marc. Normalized metal artifact reduction (NMAR) in computed tomography. Medical physics, vol.37, no.10, 5482-5493.

상세보기
Mahnken, Andreas H., Raupach, Rainer, Wildberger, Joachim E., Jung, Bettina, Heussen, Nicole, Flohr, Thomas G., Günther, Rolf W., Schaller, Stefan. A New Algorithm for Metal Artifact Reduction in Computed Tomography: In Vitro and In Vivo Evaluation After Total Hip Replacement. Investigative radiology, vol.38, no.12, 769-775.

상세보기
Proc Eur Conf Comput Vis (ECCV) CBAM: Convolutional block attention module woo 2018 3
Wang, Ge, Snyder, D.L., O'Sullivan, J.A., Vannier, M.W.. Iterative deblurring for CT metal artifact reduction. IEEE transactions on medical imaging, vol.15, no.5, 657-664.

상세보기
Shepp, L. A., Vardi, Y.. Maximum Likelihood Reconstruction for Emission Tomography. IEEE transactions on medical imaging, vol.1, no.2, 113-122.

상세보기
Computed Tomography Principles Design Artifacts and Recent Advances hsieh 2003 114
De Man, B., Nuyts, J., Dupont, P., Marchal, G., Suetens, P.. An iterative maximum-likelihood polychromatic algorithm for CT. IEEE transactions on medical imaging, vol.20, no.10, 999-1008.

상세보기
Feldkamp, L. A., Davis, L. C., Kress, J. W.. Practical cone-beam algorithm. Journal of the Optical Society of America. A, Optics and image science, vol.1, no.6, 612-.

상세보기
Rensink, Ronald A.. The Dynamic Representation of Scenes. Visual cognition, vol.7, no.1, 17-42.

상세보기
Proc Adv Neural Inf Process Syst Learning to combine foveal glimpses with a third-order Boltzmann machine larochelle 2010 1243
Corbetta, Maurizio, Shulman, Gordon L.. Control of goal-directed and stimulus-driven attention in the brain. Nature reviews. Neuroscience, vol.3, no.3, 201-215.

상세보기
Liao, Haofu, Lin, Wei-An, Zhou, S. Kevin, Luo, Jiebo. ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction. IEEE transactions on medical imaging, vol.39, no.3, 634-643.

상세보기
De Man, B., Nuyts, J., Dupont, P., Marchal, G., Suetens, P.. Reduction of metal streak artifacts in X-ray computed tomography using a transmission maximum a posteriori algorithm. IEEE transactions on nuclear science, vol.47, no.3, 977-981.

상세보기
Proc 34th Int Conf Mach Learn Conditional image synthesis with auxiliary classifier GANs odena 2017 2642
10.1109/ISBI45749.2020.9098633

LOADING...

활용도 분석정보

상세보기

다운로드

내보내기

활용도 Top5 논문

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.

원문 URL 링크

DOI : 10.1109/TMI.2021.3101363
IEEE : 저널 > 논문

*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.

저작권 관리 안내

내보내기 메뉴

내보내기 구분

파일저장
인쇄
메일전송

구성항목

기본정보
상세정보

관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관

저장형식

Text(ASCII format)
Excel format
RefWorks Direct Export
RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley

메일정보

받는사람 (필수): @
보내는사람 (선택): @
제목
내용: KISTI 검색결과 이메일 서비스

안내

총 건의 자료가 검색되었습니다.

다운받으실 자료의 인덱스를 입력하세요. (1-10,000)

검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다.

데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요)

다운로드 파일은 UTF-8 형태로 저장됩니다.
파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오.

Text(ASCII format)
Excel format

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

AI-Helper ※ AI-Helper는 을 사용합니다.

AI-Helper

안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

연합인증