{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:43:00Z","timestamp":1742982180189,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"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_13","type":"book-chapter","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T23:35:39Z","timestamp":1663803339000},"page":"123-132","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DPDudoNet: Deep-Prior Based Dual-Domain Network for Low-Dose Computed Tomography Reconstruction"],"prefix":"10.1007","author":[{"given":"Temitope Emmanuel","family":"Komolafe","sequence":"first","affiliation":[]},{"given":"Yuhang","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Nizhuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Kaicong","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Guohua","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"issue":"1","key":"13_CR1","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1038\/bjc.2014.526","volume":"112","author":"N Journy","year":"2015","unstructured":"Journy, N., et al.: Are the studies on cancer risk from CT scans biased by indication? Elements of answer from a large-scale cohort study in France. Br. J. Cancer 112(1), 185\u2013193 (2015)","journal-title":"Br. J. Cancer"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Brink, J.A., Miller D.L.: U.S. national diagnostic reference levels: closing the gap. Radiology 277(1), 3\u20136 (2015)","DOI":"10.1148\/radiol.2015150971"},{"issue":"6","key":"13_CR3","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., Xie, Y., Cao, G.: 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":"3","key":"13_CR4","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1148\/radiol.2019191422","volume":"293","author":"A Mileto","year":"2019","unstructured":"Mileto, A., Guimaraes, L.S., McCollough, C.H., Fletcher, J.G., Yu, L.: State of the art in abdominal CT: the limits of iterative reconstruction algorithms. Radiology 293(3), 491\u2013503 (2019)","journal-title":"Radiology"},{"key":"13_CR5","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.neunet.2020.07.025","volume":"131","author":"C Tian","year":"2020","unstructured":"Tian, C., et al.: Deep learning on image denoising: an overview. Neural Netw 131, 251\u2013275 (2020)","journal-title":"Neural Netw"},{"key":"13_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104775","volume":"138","author":"Z Zhang","year":"2021","unstructured":"Zhang, Z., et al.: Self-supervised CT super-resolution with hybrid model. Compt. Biol. Med. 138, 104775 (2021)","journal-title":"Compt. Biol. Med."},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"Wang, H., et al.: InDuDoNet: an interpretable dual domain network for CT metal artifact reduction. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 107\u2013118. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87231-1_11","DOI":"10.1007\/978-3-030-87231-1_11"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Ghani, M.U., Karl, W.C.: Deep learning-based sinogram completion for low-dose CT. In: 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), pp. 1\u20135 (2018)","DOI":"10.1109\/IVMSPW.2018.8448403"},{"issue":"12","key":"13_CR9","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"},{"issue":"6","key":"13_CR10","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1109\/TMI.2018.2827462","volume":"37","author":"Q Yang","year":"2018","unstructured":"Yang, Q., et al.: Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348\u20131357 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR11","doi-asserted-by":"publisher","first-page":"71091","DOI":"10.1109\/ACCESS.2021.3079323","volume":"9","author":"F Jiao","year":"2021","unstructured":"Jiao, F., et al.: A dual-domain CNN-based network for CT reconstruction. IEEE Access 9, 71091\u201371103 (2021)","journal-title":"IEEE Access"},{"issue":"11","key":"13_CR12","doi-asserted-by":"publisher","first-page":"3002","DOI":"10.1109\/TMI.2021.3078067","volume":"40","author":"W Wu","year":"2021","unstructured":"Wu, W., et al.: DRONE: dual-domain residual-based optimization NEtwork for sparse-view CT reconstruction. IEEE Trans. Med. Imaging. 40(11), 3002\u20133014 (2021)","journal-title":"IEEE Trans. Med. Imaging."},{"issue":"2","key":"13_CR13","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MSP.2020.3016905","volume":"38","author":"V Monga","year":"2021","unstructured":"Monga, V., Li, Y., Eldar, Y.C.: Algorithm unrolling: interpretable, efficient deep learning for signal and image processing. IEEE Signal Process 38(2), 18\u201344 (2021)","journal-title":"IEEE Signal Process"},{"issue":"3","key":"13_CR14","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1109\/TPAMI.2018.2883941","volume":"42","author":"Y Yang","year":"2020","unstructured":"Yang, Y., Sun, J., Li, H., Xu, Z.: ADMM-CSNet: a deep learning approach for image compressive sensing. IEEE Trans. Pattern Anal. 42(3), 521\u2013538 (2020)","journal-title":"IEEE Trans. Pattern Anal."},{"issue":"6","key":"13_CR15","doi-asserted-by":"publisher","first-page":"1322","DOI":"10.1109\/TMI.2018.2799231","volume":"37","author":"J Adler","year":"2018","unstructured":"Adler, J., \u00d6ktem, O.: Learned primal-dual reconstruction. IEEE Trans. Med. Imaging 37(6), 1322\u20131332 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"13_CR16","doi-asserted-by":"publisher","first-page":"2459","DOI":"10.1109\/TMI.2021.3088344","volume":"40","author":"W Xia","year":"2021","unstructured":"Xia, W., et al.: MAGIC: manifold and graph integrative convolutional network for low-dose CT reconstruction. IEEE Trans. Med. Imaging 40(12), 2459\u20133472 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR17","unstructured":"Zhang, Y., et al.: LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT. arXiv preprint arXiv:2012.06983 (2020)"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dong, B., Liu, B.: JSR-Net: A deep network for joint spatial-radon domain CT reconstruction from incomplete data ICASSP 2019. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3657\u20133661 (2019)","DOI":"10.1109\/ICASSP.2019.8682178"},{"issue":"7","key":"13_CR19","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1007\/s11263-020-01303-4","volume":"128","author":"D Ulyanov","year":"2020","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. Int. J. Comput. Vision 128(7), 1867\u20131888 (2020). https:\/\/doi.org\/10.1007\/s11263-020-01303-4","journal-title":"Int. J. Comput. Vision"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"issue":"1","key":"13_CR21","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","volume":"3","author":"H Zhao","year":"2017","unstructured":"Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Trans Comput Imaging 3(1), 47\u201357 (2017)","journal-title":"IEEE Trans Comput Imaging"},{"key":"13_CR22","unstructured":"AAPM. (2015). Low Dose CT Grand Challenge. [Online]. Available: https:\/\/www.aapm.org\/GrandChallange\/LowDoseCT\/#"},{"issue":"2","key":"13_CR23","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1118\/1.595715","volume":"12","author":"RL Siddon","year":"1985","unstructured":"Siddon, R.L.: Fast calculation of the exact radiological path for a three-dimensional CT array. Med. Phys. 12(2), 252\u2013255 (1985)","journal-title":"Med. Phys."},{"key":"13_CR24","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1109\/83.841940","volume":"9","author":"N Damera-Venkata","year":"2000","unstructured":"Damera-Venkata, N., Kite, T.D., Geisler, W.S., Evans, B.L., Bovik, A.C.: Image quality assessment based on a degradation model. IEEE Trans. Image Process 9, 636\u2013650 (2000)","journal-title":"IEEE Trans. Image Process"},{"key":"13_CR25","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process 13, 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process"},{"key":"13_CR26","unstructured":"Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} symposium on operating systems design and implementation {OSDI 16}, pp. 265\u2013283 (2016)"},{"key":"13_CR27","doi-asserted-by":"crossref","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)","DOI":"10.1109\/TIP.2007.901238"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Guan, S., Loew, M.: Analysis of generalizability of deep neural networks based on the complexity of decision boundary. In: 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.101\u2013106 (2020)","DOI":"10.1109\/ICMLA51294.2020.00025"}],"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_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T23:46:09Z","timestamp":1663803969000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17247-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031172465","9783031172472"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17247-2_13","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)"}}]}}