{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T05:25:30Z","timestamp":1743485130816,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030885519"},{"type":"electronic","value":"9783030885526"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-88552-6_11","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T14:17:58Z","timestamp":1632925078000},"page":"109-119","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Real-Time Video Denoising to Reduce Ionizing Radiation Exposure in\u00a0Fluoroscopic Imaging"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9312-1773","authenticated-orcid":false,"given":"Dave","family":"Van Veen","sequence":"first","affiliation":[]},{"given":"Ben A.","family":"Duffy","sequence":"additional","affiliation":[]},{"given":"Long","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Keshav","family":"Datta","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Greg","family":"Zaharchuk","sequence":"additional","affiliation":[]},{"given":"Enhao","family":"Gong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"issue":"1","key":"11_CR1","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/s10851-017-0742-4","volume":"60","author":"P Arias","year":"2018","unstructured":"Arias, P., Morel, J.M.: Video denoising via empirical bayesian estimation of space-time patches. J. Math. Imag. Vis. 60(1), 70\u201393 (2018)","journal-title":"J. Math. Imag. Vis."},{"issue":"2","key":"11_CR2","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1148\/radiol.2542082312","volume":"254","author":"S Balter","year":"2010","unstructured":"Balter, S., Hopewell, J.W., Miller, D.L., Wagner, L.K., Zelefsky, M.J.: Fluoroscopically guided interventional procedures: a review of radiation effects on patients\u2019 skin and hair. Radiology 254(2), 326\u2013341 (2010)","journal-title":"Radiology"},{"issue":"2","key":"11_CR3","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."},{"issue":"8","key":"11_CR4","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","volume":"16","author":"K Dabov","year":"2007","unstructured":"Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080\u20132095 (2007)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Davy, A., Ehret, T., Morel, J.M., Arias, P., Facciolo, G.: Non-local video denoising by CNN. arXiv preprint arXiv:1811.12758 (2018)","DOI":"10.1109\/ICIP.2019.8803314"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2758\u20132766 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Hoffman, D.A., Lonstein, J.E., Morin, M.M., Visscher, W., Harris III, B.S., Boice, J.D., Jr.: Breast cancer in women with scoliosis exposed to multiple diagnostic X rays. JNCI J. Natl. Cancer Inst. 81(17), 1307\u20131312 (1989)","DOI":"10.1093\/jnci\/81.17.1307"},{"issue":"6","key":"11_CR8","doi-asserted-by":"publisher","first-page":"W565","DOI":"10.2214\/AJR.14.12513","volume":"203","author":"W Huda","year":"2014","unstructured":"Huda, W.: Kerma-area product in diagnostic radiology. Am. J. Roentgenol. 203(6), W565\u2013W569 (2014)","journal-title":"Am. J. Roentgenol."},{"key":"11_CR9","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"11_CR10","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"issue":"9","key":"11_CR11","doi-asserted-by":"publisher","first-page":"3952","DOI":"10.1109\/TIP.2012.2199324","volume":"21","author":"M Maggioni","year":"2012","unstructured":"Maggioni, M., Boracchi, G., Foi, A., Egiazarian, K.: Video denoising, deblocking, and enhancement through separable 4-d nonlocal spatiotemporal transforms. IEEE Trans. Image Process. 21(9), 3952\u20133966 (2012)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Maggioni, M., Huang, Y., Li, C., Xiao, S., Fu, Z., Song, F.: Efficient multi-stage video denoising with recurrent spatio-temporal fusion. arXiv preprint arXiv:2103.05407 (2021)","DOI":"10.1109\/CVPR46437.2021.00347"},{"issue":"6","key":"11_CR13","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1093\/occmed\/kqi048","volume":"55","author":"G Mastrangelo","year":"2005","unstructured":"Mastrangelo, G., Fedeli, U., Fadda, E., Giovanazzi, A., Scoizzato, L., Saia, B.: Increased cancer risk among surgeons in an orthopaedic hospital. Occup. Med. 55(6), 498\u2013500 (2005)","journal-title":"Occup. Med."},{"key":"11_CR14","unstructured":"NVIDIA: Tensorrt open source software (2018). https:\/\/developer.nvidia.com\/tensorrt"},{"key":"11_CR15","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch (2017)"},{"issue":"6","key":"11_CR16","doi-asserted-by":"publisher","first-page":"1785","DOI":"10.1148\/rg.266065063","volume":"26","author":"AN Primak","year":"2006","unstructured":"Primak, A.N., McCollough, C.H., Bruesewitz, M.R., Zhang, J., Fletcher, J.G.: Relationship between noise, dose, and pitch in cardiac multi-detector row CT. Radiographics 26(6), 1785\u20131794 (2006)","journal-title":"Radiographics"},{"key":"11_CR17","volume-title":"Medical Imaging Signals and Systems","author":"JL Prince","year":"2006","unstructured":"Prince, J.L., Links, J.M.: Medical Imaging Signals and Systems. Pearson Prentice Hall, Upper Saddle River (2006)"},{"issue":"20","key":"11_CR18","doi-asserted-by":"publisher","first-page":"2637","DOI":"10.1097\/00007632-200010150-00016","volume":"25","author":"YR Rampersaud","year":"2000","unstructured":"Rampersaud, Y.R., Foley, K.T., Shen, A.C., Williams, S., Solomito, M.: Radiation exposure to the spine surgeon during fluoroscopically assisted pedicle screw insertion. Spine 25(20), 2637\u20132645 (2000)","journal-title":"Spine"},{"key":"11_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":"11","key":"11_CR20","doi-asserted-by":"publisher","first-page":"2385","DOI":"10.1109\/TIP.2009.2025923","volume":"18","author":"MP Sampat","year":"2009","unstructured":"Sampat, M.P., Wang, Z., Gupta, S., Bovik, A.C., Markey, M.K.: Complex wavelet structural similarity: a new image similarity index. IEEE Trans. Image Process. 18(11), 2385\u20132401 (2009)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"11_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-019-0713-7","volume":"18","author":"A Sarno","year":"2019","unstructured":"Sarno, A., et al.: Real-time algorithm for poissonian noise reduction in low-dose fluoroscopy: performance evaluation. Biomed. Eng. Online 18(1), 1\u201321 (2019)","journal-title":"Biomed. Eng. Online"},{"issue":"4","key":"11_CR22","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1542\/peds.112.4.971","volume":"112","author":"TL Slovis","year":"2003","unstructured":"Slovis, T.L.: Children, computed tomography radiation dose, and the as low as reasonably achievable (ALARA) concept. Pediatrics 112(4), 971\u2013972 (2003)","journal-title":"Pediatrics"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Tang, X., Zhen, P., Kang, M., Yi, H., Wang, W., Chen, H.B.: Learning enriched features for video denoising with convolutional neural network. In: 2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), pp. 248\u2013251. IEEE (2020)","DOI":"10.1109\/APCCAS50809.2020.9301660"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Tassano, M., Delon, J., Veit, T.: DVDNet: a fast network for deep video denoising. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1805\u20131809. IEEE (2019)","DOI":"10.1109\/ICIP.2019.8803136"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Tassano, M., Delon, J., Veit, T.: FastDVDNet: towards real-time deep video denoising without flow estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1354\u20131363 (2020)","DOI":"10.1109\/CVPR42600.2020.00143"},{"key":"11_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-030-01216-8_8","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Wu","year":"2018","unstructured":"Wu, S., Xu, J., Tai, Y.-W., Tang, C.-K.: Deep high dynamic range imaging with large foreground motions. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 120\u2013135. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_8"},{"issue":"3","key":"11_CR27","doi-asserted-by":"publisher","first-page":"250","DOI":"10.4103\/0019-5413.181791","volume":"50","author":"K-H Zhou","year":"2016","unstructured":"Zhou, K.-H., Luo, C.-F., Chen, N., Hu, C.-F., Pan, F.-G.: Minimally invasive surgery under fluoro-navigation for anterior pelvic ring fractures. Indian J. Orthopaedics 50(3), 250\u2013255 (2016). https:\/\/doi.org\/10.4103\/0019-5413.181791","journal-title":"Indian J. Orthopaedics"}],"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\/978-3-030-88552-6_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T19:09:50Z","timestamp":1673377790000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88552-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030885519","9783030885526"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88552-6_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmir2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmir2021\/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":"cmt3.research.microsoft.com","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20","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":"13","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":"65% - 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":"2.65","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":"3.53","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}