{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:55:55Z","timestamp":1743065755441,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322533"},{"type":"electronic","value":"9783030322540"}],"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\/978-3-030-32254-0_2","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"11-19","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories"],"prefix":"10.1007","author":[{"given":"Jan-Nico","family":"Zaech","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bastian","family":"Bier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Russell","family":"Taylor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Maier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathias","family":"Unberath","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"2_CR1","first-page":"780","volume-title":"Lecture Notes in Computer Science","author":"Cagla Deniz Bahadir","year":"2019","unstructured":"Bahadir, C.D., Dalca, A.V., Sabuncu, M.R.: Learning-based optimization of the under-sampling pattern in MRI. arXiv preprint arXiv:1901.01960 (2019)"},{"issue":"11","key":"2_CR2","doi-asserted-by":"publisher","first-page":"2906","DOI":"10.1007\/s00586-017-5139-y","volume":"26","author":"V Cordemans","year":"2017","unstructured":"Cordemans, V., Kaminski, L., Banse, X., Francq, B.G., Cartiaux, O.: Accuracy of a new intraoperative cone beam CT imaging technique compared to postoperative CT scan for assessment of pedicle screws placement and breaches detection. Eur. Spine. J. 26(11), 2906\u20132916 (2017)","journal-title":"Eur. Spine. J."},{"issue":"6","key":"2_CR3","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1364\/JOSAA.1.000612","volume":"1","author":"LA Feldkamp","year":"1984","unstructured":"Feldkamp, L.A., Davis, L., Kress, J.W.: Practical cone-beam algorithm. Josa a 1(6), 612\u2013619 (1984)","journal-title":"Josa a"},{"issue":"8","key":"2_CR4","doi-asserted-by":"publisher","first-page":"081902","DOI":"10.1118\/1.4883816","volume":"41","author":"GJ Gang","year":"2014","unstructured":"Gang, G.J., Stayman, J.W., Zbijewski, W., Siewerdsen, J.H.: Task-baseddetectability in CT image reconstruction by filtered backprojection andpenalized likelihood estimation. Med. Phys. 41(8), 081902 (2014)","journal-title":"Med. Phys."},{"issue":"2","key":"2_CR5","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/s00586-011-2011-3","volume":"21","author":"ID Gelalis","year":"2012","unstructured":"Gelalis, I.D., et al.: Accuracy of pedicle screw placement: a systematic review of prospective in vivo studies comparing free hand, fluoroscopy guidance and navigation techniques. Eur. Spine J. 21(2), 247\u2013255 (2012)","journal-title":"Eur. Spine J."},{"key":"2_CR6","doi-asserted-by":"publisher","first-page":"5826","DOI":"10.1109\/ACCESS.2016.2608621","volume":"4","author":"L Gjesteby","year":"2016","unstructured":"Gjesteby, L., et al.: Metal artifact reduction in ct: where are we after four decades? IEEE Access 4, 5826\u20135849 (2016)","journal-title":"IEEE Access"},{"key":"2_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-32875-7_1","volume-title":"Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis","author":"Fausto Milletari","year":"2019","unstructured":"Milletari, F., Birodkar, V., Sofka, M.: Straight to the point: reinforcement learning for user guidance in ultrasound. arXiv preprint arXiv:1903.00586 (2019)"},{"key":"2_CR8","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 [cs] (2014)"},{"key":"2_CR9","unstructured":"Stayman, J.W., Siewerdsen, J.H.: Task-based trajectories in iteratively reconstructed interventional cone-beam CT. In: Proceedings 12th International Meeting Fully 3D Image Reconstruction Radiology Nuclear Medcine, pp. 257\u2013260 (2013)"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Unberath, M., et al.: Enabling machine learning inx-ray-based procedures via realistic simulation of image formation. Int. J. Comput. Assist. Radiol. Surg. (2019). https:\/\/doi.org\/10.1007\/s11548-019-02011-2, https:\/\/doi.org\/10.1007\/s11548-019-02011-2","DOI":"10.1007\/s11548-019-02011-2 10.1007\/s11548-019-02011-2"},{"key":"2_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/978-3-030-00937-3_12","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"M Unberath","year":"2018","unstructured":"Unberath, M., et al.: DeepDRR \u2013 a catalyst for machine learning in fluoroscopy-guided procedures. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 98\u2013106. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_12"},{"key":"2_CR12","unstructured":"United States Bone and Joint Initiative: The Burden of Musculoskeletal Diseases in the United States (BMUS). Third Edition edn. (2014)"}],"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\/978-3-030-32254-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:10:19Z","timestamp":1728519019000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32254-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322533","9783030322540"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32254-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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"}]}}