{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T05:25:53Z","timestamp":1743485153095,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031172465"},{"type":"electronic","value":"9783031172472"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-17247-2_10","type":"book-chapter","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T23:35:39Z","timestamp":1663803339000},"page":"95-104","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Denoising Network for\u00a0X-Ray Fluoroscopic Image Sequences of\u00a0Moving Objects"],"prefix":"10.1007","author":[{"given":"Wonjin","family":"Kim","sequence":"first","affiliation":[]},{"given":"Wonkyeong","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Sun-Young","family":"Jeon","sequence":"additional","affiliation":[]},{"given":"Nayeon","family":"Kang","sequence":"additional","affiliation":[]},{"given":"Geonhui","family":"Jo","sequence":"additional","affiliation":[]},{"given":"Jang-Hwan","family":"Choi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"issue":"4","key":"10_CR1","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.jbiomech.2011.12.022","volume":"45","author":"P Bifulco","year":"2012","unstructured":"Bifulco, P., Cesarelli, M., Cerciello, T., Romano, M.: A continuous description of intervertebral motion by means of spline interpolation of kinematic data extracted by videofluoroscopy. J. Biomech. 45(4), 634\u2013641 (2012)","journal-title":"J. Biomech."},{"issue":"2","key":"10_CR2","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s11548-012-0772-8","volume":"8","author":"M Cesarelli","year":"2013","unstructured":"Cesarelli, M., Bifulco, P., Cerciello, T., Romano, M., Paura, L.: X-ray fluoroscopy noise modeling for filter design. Int. J. Comput. Assist. Radiol. Surg. 8(2), 269\u2013278 (2013)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Chan, K.C., Wang, X., Yu, K., Dong, C., Loy, C.C.: Basicvsr: the search for essential components in video super-resolution and beyond. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4947\u20134956 (2021)","DOI":"10.1109\/CVPR46437.2021.00491"},{"issue":"12","key":"10_CR4","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1109\/TMI.2017.2715284","volume":"36","author":"H Chen","year":"2017","unstructured":"Chen, H., et al.: Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36(12), 2524\u20132535 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"10_CR6","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"10_CR7","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, pp. 2672\u20132680. Curran Associates, Inc. (2014)"},{"key":"10_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"issue":"6","key":"10_CR9","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1109\/TMI.2018.2823756","volume":"37","author":"E Kang","year":"2018","unstructured":"Kang, E., Chang, W., Yoo, J., Ye, J.C.: Deep convolutional framelet denosing for low-dose CT via wavelet residual network. IEEE Trans. Med. Imaging 37(6), 1358\u20131369 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"10_CR11","unstructured":"Lehtinen, J., et al.: Noise2Noise: learning image restoration without clean data. In: Proceedings of the 35th International Conference on Machine Learning, vol. 80, pp. 2965\u20132974. PMLR (2018)"},{"key":"10_CR12","unstructured":"Liang, J., et al.: VRT: a video restoration transformer. arXiv preprint arXiv:2201.12288 (2022)"},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"Luo, Y., et al.: Edge-enhancement densenet for x-ray fluoroscopy image denoising in cardiac electrophysiology procedures. Med. Phys. 49(2), 1262\u20131275 (2022). https:\/\/doi.org\/10.1002\/mp.15426. https:\/\/aapm.onlinelibrary.wiley.com\/doi\/full\/10.1002\/mp.15426","DOI":"10.1002\/mp.15426"},{"issue":"9","key":"10_CR14","doi-asserted-by":"publisher","first-page":"2626","DOI":"10.1109\/TBME.2020.3041571","volume":"68","author":"Y Luo","year":"2021","unstructured":"Luo, Y., Majoe, S., Kui, J., Qi, H., Pushparajah, K., Rhode, K.: Ultra-dense denoising network: application to cardiac catheter-based x-ray procedures. IEEE Trans. Biomed. Eng. 68(9), 2626\u20132636 (2021). https:\/\/doi.org\/10.1109\/TBME.2020.3041571","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Matviychuk, Y., et al.: Learning a multiscale patch-based representation for image denoising in x-ray fluoroscopy. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 2330\u20132334. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7532775"},{"issue":"9","key":"10_CR16","doi-asserted-by":"publisher","first-page":"2558","DOI":"10.1109\/TBME.2012.2206808","volume":"59","author":"M Moradi","year":"2012","unstructured":"Moradi, M., et al.: Seed localization in ultrasound and registration to C-arm fluoroscopy using matched needle tracks for prostate brachytherapy. IEEE Trans. Biomed. Eng. 59(9), 2558\u20132567 (2012)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Moran, N., Schmidt, D., Zhong, Y., Coady, P.: Noisier2Noise: learning to denoise from unpaired noisy data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12064\u201312072 (2020)","DOI":"10.1109\/CVPR42600.2020.01208"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Ranjan, A., Black, M.J.: Optical flow estimation using a spatial pyramid network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4161\u20134170 (2017)","DOI":"10.1109\/CVPR.2017.291"},{"key":"10_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"5","key":"10_CR20","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1148\/radiographics.16.5.8888398","volume":"16","author":"TB Shope","year":"1996","unstructured":"Shope, T.B.: Radiation-induced skin injuries from fluoroscopy. Radiographics 16(5), 1195\u20131199 (1996)","journal-title":"Radiographics"},{"issue":"2","key":"10_CR21","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.bspc.2011.02.003","volume":"7","author":"M Tomic","year":"2012","unstructured":"Tomic, M., Loncaric, S., Sersic, D.: Adaptive spatio-temporal denoising of fluoroscopic x-ray sequences. Biomed. Signal Process. Control 7(2), 173\u2013179 (2012)","journal-title":"Biomed. Signal Process. Control"},{"issue":"2","key":"10_CR22","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/j.ijrobp.2009.01.020","volume":"74","author":"J Wang","year":"2009","unstructured":"Wang, J., Zhu, L., Xing, L.: Noise reduction in low-dose x-ray fluoroscopy for image-guided radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 74(2), 637\u2013643 (2009)","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"10_CR23","doi-asserted-by":"publisher","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00813","DOI":"10.1109\/CVPR.2018.00813"},{"issue":"4","key":"10_CR24","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/4233.681173","volume":"1","author":"J Weese","year":"1997","unstructured":"Weese, J., Penney, G.P., Desmedt, P., Buzug, T.M., Hill, D.L., Hawkes, D.J.: Voxel-based 2-D\/3-D registration of fluoroscopy images and CT scans for image-guided surgery. IEEE Trans. Inf. Technol. Biomed. 1(4), 284\u2013293 (1997)","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"issue":"5","key":"10_CR25","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1109\/TMI.2004.826051","volume":"23","author":"T Yamazaki","year":"2004","unstructured":"Yamazaki, T., et al.: Improvement of depth position in 2-D\/3-D registration of knee implants using single-plane fluoroscopy. IEEE Trans. Med. Imaging 23(5), 602\u2013612 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"10_CR26","doi-asserted-by":"publisher","first-page":"2480","DOI":"10.1109\/TPAMI.2020.2968521","volume":"43","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 43(7), 2480\u20132495 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Medical Image Reconstruction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17247-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T23:46:30Z","timestamp":1663803990000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17247-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031172465","9783031172472"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17247-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning for Medical Image Reconstruction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmir2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmir2022\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"79% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,43","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1,58","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}