[논문]Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal

Han, Yoseob; Kim, Junyoung; Ye, Jong Chul

doi:10.1109/tmi.2020.3000341

[해외논문] Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal 원문보기

IEEE transactions on medical imaging, v.39 no.11, 2020년, pp.3571 - 3582

Han, Yoseob (Los Alamos National Laboratory, Theoretical Division, Los Alamos, NM, USA) , Kim, Junyoung (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea) , Ye, Jong Chul (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea)

Abstract ▼ AI-Helper

Conebeam CT using a circular trajectory is quite often used for various applications due to its relative simple geometry. For conebeam geometry, Feldkamp, Davis and Kress algorithm is regarded as the standard reconstruction method, but this algorithm suffers from so-called conebeam artifacts as the cone angle increases. Various model-based iterative reconstruction methods have been developed to reduce the cone-beam artifacts, but these algorithms usually require multiple applications of computational expensive forward and backprojections. In this paper, we develop a novel deep learning approach for accurate conebeam artifact removal. In particular, our deep network, designed on the differentiated backprojection domain, performs a data-driven inversion of an ill-posed deconvolution problem associated with the Hilbert transform. The reconstruction results along the coronal and sagittal directions are then combined using a spectral blending technique to minimize the spectral leakage. Experimental results under various conditions confirmed that our method generalizes well and outperforms the existing iterative methods despite significantly reduced runtime complexity.

참고문헌 (34)

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
arXiv 1412 3474 Deep domain confusion: Maximizing for domain invariance tzeng 2014
10.1145/2733373.2807412
IEEE Trans Med Imag Region of interest reconstruction from truncated data in circular cone-beam CT yu 2006 10.1109/TMI.2006.872329 25 869

상세보기
Kang, Eunhee, Koo, Hyun Jung, Yang, Dong Hyun, Seo, Joon Bum, Ye, Jong Chul. Cycle‐consistent adversarial denoising network for multiphase coronary CT angiography. Medical physics, vol.46, no.2, 550-562.

상세보기
Sidky, Emil Y, Jørgensen, Jakob H, Pan, Xiaochuan. Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle–Pock algorithm. Physics in medicine & biology, vol.57, no.10, 3065-3091.

상세보기
Zhang, Zheng, Han, Xiao, Pearson, Erik, Pelizzari, Charles, Sidky, Emil Y, Pan, Xiaochuan. Artifact reduction in short-scan CBCT by use of optimization-based reconstruction. Physics in medicine & biology, vol.61, no.9, 3387-3406.

상세보기
Xia, Dan, Langan, David A, Solomon, Stephen B, Zhang, Zheng, Chen, Buxin, Lai, Hao, Sidky, Emil Y, Pan, Xiaochuan. Optimization-based image reconstruction with artifact reduction in C-arm CBCT. Physics in medicine & biology, vol.61, no.20, 7300-7333.

상세보기
Proc Adv Neural Inf Process Syst ImageNet classification with deep convolutional neural networks krizhevsky 2012 1097
Proc Int Conf Med Image Comput Comput -Assist Intervent (MICCAI) U-net: Convolutional networks for biomedical image segmentation ronneberger 2015 234
arXiv 1611 06391 Deep residual learning for compressed sensing CT reconstruction via persistent homology analysis han 2016
Kang, Eunhee, Min, Junhong, Ye, Jong Chul. A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction. Medical physics, vol.44, no.10,

상세보기
Chen, Hu, Zhang, Yi, Kalra, Mannudeep K., Lin, Feng, Chen, Yang, Liao, Peixi, Zhou, Jiliu, Wang, Ge. Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network. IEEE transactions on medical imaging, vol.36, no.12, 2524-2535.

상세보기
Kang, Eunhee, Chang, Won, Yoo, Jaejun, Ye, Jong Chul. Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network. IEEE transactions on medical imaging, vol.37, no.6, 1358-1369.

상세보기
Jin, Kyong Hwan, McCann, Michael T., Froustey, Emmanuel, Unser, Michael. Deep Convolutional Neural Network for Inverse Problems in Imaging. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.26, no.9, 4509-4522.

상세보기
Zou, Yu, Pan, Xiaochuan. Exact image reconstruction on PI-lines from minimum data in helical cone-beam CT. Physics in medicine & biology, vol.49, no.6, 941-959.

상세보기
Mori, Shinichiro, Endo, Masahiro, Komatsu, Shuhei, Kandatsu, Susumu, Yashiro, Tomoyasu, Baba, Masayuki. A combination-weighted Feldkamp-based reconstruction algorithm for cone-beam CT. Physics in medicine & biology, vol.51, no.16, 3953-3965.

상세보기
Pack, Jed D, Noo, Fr？d？ric. Cone-beam reconstruction using 1D filtering along the projection of M-lines. Inverse problems, vol.21, no.3, 1105-1120.

상세보기
Tang, Xiangyang, Hsieh, Jiang, Hagiwara, Akira, Nilsen, Roy A, Thibault, Jean-Baptiste, Drapkin, Evgeny. A three-dimensional weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT under a circular source trajectory. Physics in medicine & biology, vol.50, no.16, 3889-3905.

상세보기
Bartolac, Steven, Clackdoyle, Rolf, Noo, Frederic, Siewerdsen, Jeff, Moseley, Douglas, Jaffray, David. A local shift‐variant Fourier model and experimental validation of circular cone‐beam computed tomography artifacts. Medical physics, vol.36, no.2, 500-512.

상세보기
Zou, Yu, Pan, Xiaochuan. Image reconstruction on PI-lines by use of filtered backprojection in helical cone-beam CT. Physics in medicine & biology, vol.49, no.12, 2717-2731.

상세보기
Tuy, Heang K.. An Inversion Formula for Cone-Beam Reconstruction. SIAM journal on applied mathematics, vol.43, no.3, 546-552.

상세보기
Peyrin, F., Amiel, M., Goutte, R.. Analysis of a cone beam x-ray tomographic system for different scanning modes. Journal of the Optical Society of America. A, Optics and image science, vol.9, no.9, 1554-.

상세보기
Proc Fully 3D Image Reconstruction in Radiology and Nuclear Medicine Mitigating cone-beam artifacts in short-scan CT imaging for large cone-angle scans pack 2013 307
Grass, M, K？hler, Th, Proksa, R. Angular weighted hybrid cone-beam CT reconstruction for circular trajectories. Physics in medicine & biology, vol.46, no.6, 1595-1610.

상세보기
Sidky, Emil Y, Pan, Xiaochuan. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Physics in medicine & biology, vol.53, no.17, 4777-4807.

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
Han, Yoseob, Ye, Jong Chul. Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT. IEEE transactions on medical imaging, vol.37, no.6, 1418-1429.

상세보기
Han, Yoseob, Ye, Jong Chul. One network to solve all ROIs: Deep learning CT for any ROI using differentiated backprojection. Medical physics, vol.46, no.12,

상세보기
Proc 5th Int Conf Image Formation X-ray Comput Tomogr Deep learning interior tomography for region-of-interest reconstruction han 2018 1
Proceedings 36th Int Conf Mach Learn Understanding geometry of encoder-decoder CNNs ye 2019 97 7064
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
Dennerlein, F., Noo, F., Schondube, H., Lauritsch, G., Hornegger, J.. A Factorization Approach for Cone-Beam Reconstruction on a Circular Short-Scan. IEEE transactions on medical imaging, vol.27, no.7, 887-896.

상세보기
Lee, Minji, Han, Yoseob, Ward, John Paul, Unser, Michael, Ye, Jong Chul. Interior Tomography Using 1D Generalized Total Variation. Part II: Multiscale Implementation. SIAM journal on imaging sciences, vol.8, no.4, 2452-2486.

상세보기

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 보기

AccessON 원문보기

원문 URL 링크

DOI : 10.1109/TMI.2020.3000341
IEEE : 저널 > 논문
AccessON : 저널

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

오픈액세스(OA) 유형

GREEN

저자가 공개 리포지터리에 출판본, post-print, 또는 pre-print를 셀프 아카이빙 하여 자유로운 이용이 가능한 논문

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