{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T14:57:57Z","timestamp":1775228277666,"version":"3.50.1"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030322472","type":"print"},{"value":"9783030322489","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\/978-3-030-32248-9_6","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"48-56","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Deep Learning Based Framework for Direct Reconstruction of PET Images"],"prefix":"10.1007","author":[{"given":"Zhiyuan","family":"Liu","sequence":"first","affiliation":[]},{"given":"Huai","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Huafeng","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"15","key":"6_CR1","doi-asserted-by":"publisher","first-page":"R541","DOI":"10.1088\/0031-9155\/51\/15\/R01","volume":"51","author":"J Qi","year":"2006","unstructured":"Qi, J., Leahy, R.M.: Iterative reconstruction techniques in emission computed tomography. Phys. Med. Biol. 51(15), R541\u2013R578 (2006). https:\/\/doi.org\/10.1088\/0031-9155\/51\/15\/R01","journal-title":"Phys. Med. Biol."},{"issue":"2","key":"6_CR2","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1109\/TMI.1982.4307558","volume":"1","author":"LA Shepp","year":"1982","unstructured":"Shepp, L.A., Vardi, Y.: Maximum likelihood reconstruction for emission tomography. IEEE Trans. Med. Imaging 1(2), 113\u2013122 (1982). https:\/\/doi.org\/10.1109\/TMI.1982.4307558","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"6_CR3","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TMI.1987.4307826","volume":"6","author":"E Levitan","year":"1987","unstructured":"Levitan, E., Herman, G.T.: A maximum a posteriori probability expectation maximization algorithm for image reconstruction in emission tomography. IEEE Trans. Med. Imaging 6(3), 185\u2013192 (1987). https:\/\/doi.org\/10.1109\/TMI.1987.4307826","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"6_CR4","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1016\/j.dsp.2006.08.006","volume":"16","author":"J Zhou","year":"2006","unstructured":"Zhou, J., Luo, L.M.: Sequential weighted least squares algorithm for PET image reconstruction. Digit. Sig. Proc. 16(6), 735\u2013745 (2006). https:\/\/doi.org\/10.1016\/j.dsp.2006.08.006","journal-title":"Digit. Sig. Proc."},{"issue":"5","key":"6_CR5","doi-asserted-by":"publisher","first-page":"3364","DOI":"10.1109\/TNS.2013.2278121","volume":"60","author":"J Cabello","year":"2013","unstructured":"Cabello, J., Torres-Espallardo, I., Gillam, J.E., Rafecas, M.: PET reconstruction from truncated projections using total-variation regularization for hadron therapy monitoring. IEEE Trans. Nucl. Sci. 60(5), 3364\u20133372 (2013). https:\/\/doi.org\/10.1109\/TNS.2013.2278121","journal-title":"IEEE Trans. Nucl. Sci."},{"issue":"1","key":"6_CR6","doi-asserted-by":"publisher","first-page":"6700","DOI":"10.1038\/s41598-018-25153-w","volume":"8","author":"S Xie","year":"2018","unstructured":"Xie, S., et al.: Artifact removal using improved GoogLeNet for sparse-view CT reconstruction. Sci. Rep. 8(1), 6700\u20136709 (2018). https:\/\/doi.org\/10.1038\/s41598-018-25153-w","journal-title":"Sci. Rep."},{"issue":"6","key":"6_CR7","doi-asserted-by":"publisher","first-page":"1478","DOI":"10.1109\/TMI.2018.2832613","volume":"37","author":"K Kim","year":"2018","unstructured":"Kim, K., et al.: Penalized PET reconstruction using deep learning prior and local linear fitting. IEEE Trans. Med. Imaging 37(6), 1478\u20131487 (2018). https:\/\/doi.org\/10.1109\/TMI.2018.2832613","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"6_CR8","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1109\/tmi.2018.2869871","volume":"38","author":"K Gong","year":"2019","unstructured":"Gong, K., et al.: Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans. Med. Imaging 38(3), 675\u2013685 (2019). https:\/\/doi.org\/10.1109\/tmi.2018.2869871","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7697","key":"6_CR9","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1038\/nature25988","volume":"555","author":"B Zhu","year":"2018","unstructured":"Zhu, B., Liu, J.Z., Cauley, S.F., Rosen, B.R., Rosen, M.S.: Image reconstruction by domain-transform manifold learning. Nature 555(7697), 487\u2013492 (2018). https:\/\/doi.org\/10.1038\/nature25988","journal-title":"Nature"},{"key":"6_CR10","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.media.2019.03.013","volume":"54","author":"I H\u00e4ggstr\u00f6m","year":"2019","unstructured":"H\u00e4ggstr\u00f6m, I., Schmidtlein, C.R., Campanella, G., Fuchs, T.J.: DeepPET: a deep encoder\u2013decoder network for directly solving the PET image reconstruction inverse problem. Med. Image Anal. 54, 253\u2013262 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.03.013","journal-title":"Med. Image Anal."},{"key":"6_CR11","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets (2014)"},{"key":"6_CR12","doi-asserted-by":"publisher","unstructured":"Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Paper Presented at the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 5967\u20135976 (2017). https:\/\/doi.org\/10.1109\/cvpr.2017.632","DOI":"10.1109\/cvpr.2017.632"}],"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\/978-3-030-32248-9_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:21:40Z","timestamp":1728519700000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32248-9_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322472","9783030322489"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32248-9_6","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"}]}}