{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:08:45Z","timestamp":1777655325102,"version":"3.51.4"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030322250","type":"print"},{"value":"9783030322267","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32226-7_36","type":"book-chapter","created":{"date-parts":[[2019,10,12]],"date-time":"2019-10-12T10:05:33Z","timestamp":1570874733000},"page":"319-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["R$$^{2}$$-Net: Recurrent and Recursive Network for Sparse-View CT Artifacts Removal"],"prefix":"10.1007","author":[{"given":"Tiancheng","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhisheng","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianlong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhouchen","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: IEEE conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"6","key":"36_CR2","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1109\/TMI.2018.2823338","volume":"37","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Liang, X., Dong, X., et al.: A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution. IEEE Trans. Med. Imaging 37(6), 1407\u20131417 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"36_CR3","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1109\/TMI.2018.2823768","volume":"37","author":"Y Han","year":"2018","unstructured":"Han, Y., Ye, J.C.: Framing U-Net via deep convolutional framelets: application to sparse-view CT. IEEE Trans. Med. Imaging 37(6), 1418\u20131429 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"36_CR4","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1118\/1.1455742","volume":"29","author":"AC Kak","year":"2002","unstructured":"Kak, A.C., Slaney, M., Wang, G.: Principles of computerized tomographic imaging. Med. Phys. 29(1), 107 (2002)","journal-title":"Med. Phys."},{"issue":"17","key":"36_CR5","doi-asserted-by":"publisher","first-page":"4777","DOI":"10.1088\/0031-9155\/53\/17\/021","volume":"53","author":"EY Sidky","year":"2008","unstructured":"Sidky, E.Y., Pan, X.: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys. Med. Biol. 53(17), 4777 (2008)","journal-title":"Phys. Med. Biol."},{"issue":"2","key":"36_CR6","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1118\/1.2836423","volume":"35","author":"GH Chen","year":"2008","unstructured":"Chen, G.H., Tang, J., Leng, S.: Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. Med. Phys. 35(2), 660\u2013663 (2008)","journal-title":"Med. Phys."},{"issue":"23","key":"36_CR7","doi-asserted-by":"publisher","first-page":"7923","DOI":"10.1088\/0031-9155\/57\/23\/7923","volume":"57","author":"Y Liu","year":"2012","unstructured":"Liu, Y., Ma, J., Fan, Y., et al.: Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction. Phys. Med. Biol. 57(23), 7923 (2012)","journal-title":"Phys. Med. Biol."},{"issue":"3","key":"36_CR8","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/TMI.2013.2295738","volume":"33","author":"Y Liu","year":"2014","unstructured":"Liu, Y., Liang, Z., Ma, J., et al.: Total variation-stokes strategy for sparse-view X-ray CT image reconstruction. IEEE Trans. Med. Imaging 33(3), 749\u2013763 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"36_CR9","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1109\/TMI.2014.2336860","volume":"33","author":"Y Chen","year":"2014","unstructured":"Chen, Y., Shi, L., Feng, Q., et al.: Artifact suppressed dictionary learning for low-dose CT image processing. IEEE Trans. Med. Imaging 33(12), 2271\u20132292 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"36_CR10","doi-asserted-by":"publisher","first-page":"2667","DOI":"10.1088\/0031-9155\/57\/9\/2667","volume":"57","author":"Y Chen","year":"2012","unstructured":"Chen, Y., Yang, Z., Hu, Y., et al.: Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means. Phys. Med. Biol. 57(9), 2667 (2012)","journal-title":"Phys. Med. Biol."},{"key":"36_CR11","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"},{"key":"36_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-030-00129-2_11","volume-title":"Machine Learning for Medical Image Reconstruction","author":"A Kofler","year":"2018","unstructured":"Kofler, A., Haltmeier, M., Kolbitsch, C., Kachelrie\u00df, M., Dewey, M.: A U-Nets cascade for sparse view computed tomography. In: Knoll, F., Maier, A., Rueckert, D. (eds.) MLMIR 2018. LNCS, vol. 11074, pp. 91\u201399. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00129-2_11"},{"key":"36_CR13","unstructured":"AAPM Low Dose CT Grand Challenge Homepage. https:\/\/www.aapm.org\/grandchallenge\/lowdosect\/. Accessed 3 July 2019"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32226-7_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T00:06:10Z","timestamp":1728691570000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32226-7_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322250","9783030322267"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32226-7_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1730","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":"539","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":"31% - 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":"3.07","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":"6.31","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}