{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:45:06Z","timestamp":1775324706294,"version":"3.50.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031172465","type":"print"},{"value":"9783031172472","type":"electronic"}],"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_9","type":"book-chapter","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T23:35:39Z","timestamp":1663803339000},"page":"84-94","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["DuDoTrans: Dual-Domain Transformer for\u00a0Sparse-View CT Reconstruction"],"prefix":"10.1007","author":[{"given":"Ce","family":"Wang","sequence":"first","affiliation":[]},{"given":"Kun","family":"Shang","sequence":"additional","affiliation":[]},{"given":"Haimiao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[]},{"given":"S. Kevin","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"issue":"6","key":"9_CR1","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":"11","key":"9_CR2","doi-asserted-by":"publisher","first-page":"2607","DOI":"10.1109\/TMI.2019.2906853","volume":"38","author":"P Bao","year":"2019","unstructured":"Bao, P., et al.: Convolutional sparse coding for compressed sensing CT reconstruction. IEEE Trans. Med. Imaging 38(11), 2607\u20132619 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR3","unstructured":"Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:2105.05537 (2021)"},{"key":"9_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Pre-trained image processing transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12299\u201312310 (2021)","DOI":"10.1109\/CVPR46437.2021.01212"},{"issue":"6","key":"9_CR6","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1109\/TMI.2018.2805692","volume":"37","author":"H Chen","year":"2018","unstructured":"Chen, H., et al.: Learn: learned experts- assessment-based reconstruction network for sparse-data CT. IEEE Trans. Med. Imaging 37(6), 1333\u20131347 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"9_CR7","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"},{"key":"9_CR8","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1109\/TCI.2020.2996751","volume":"6","author":"W Cheng","year":"2020","unstructured":"Cheng, W., Wang, Y., Li, H., Duan, Y.: Learned full-sampling reconstruction from incomplete data. IEEE Trans. Comput. Imaging 6, 945\u2013957 (2020)","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"9_CR9","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"6","key":"9_CR10","doi-asserted-by":"publisher","first-page":"1440","DOI":"10.1109\/TMI.2018.2832656","volume":"37","author":"H Gupta","year":"2018","unstructured":"Gupta, H., Jin, K.H., Nguyen, H.Q., McCann, M.T., Unser, M.: CNN-based projected gradient descent for consistent CT image reconstruction. IEEE Trans. Med. Imaging 37(6), 1440\u20131453 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"9_CR11","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"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"9","key":"9_CR13","doi-asserted-by":"publisher","first-page":"4509","DOI":"10.1109\/TIP.2017.2713099","volume":"26","author":"KH Jin","year":"2017","unstructured":"Jin, K.H., McCann, M.T., Froustey, E., Unser, M.: Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process. 26(9), 4509\u20134522 (2017)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"9_CR14","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1109\/TMI.2014.2380993","volume":"34","author":"K Kim","year":"2014","unstructured":"Kim, K., et al.: Sparse-view spectral CT reconstruction using spectral patch-based low-rank penalty. IEEE Trans. Med. Imaging 34(3), 748\u2013760 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"9_CR16","unstructured":"Li, Y., Zhang, K., Cao, J., Timofte, R., Van Gool, L.: LocalViT: bringing locality to vision transformers. arXiv preprint arXiv:2104.05707 (2021)"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Lin, W.A., et al.: DuDoNet: dual domain network for CT metal artifact reduction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10512\u201310521 (2019)","DOI":"10.1109\/CVPR.2019.01076"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. arXiv preprint arXiv:2103.14030 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"5","key":"9_CR19","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1109\/LSP.2018.2816582","volume":"25","author":"F Mahmood","year":"2018","unstructured":"Mahmood, F., Shahid, N., Skoglund, U., Vandergheynst, P.: Adaptive graph-based total variation for tomographic reconstructions. IEEE Signal Process. Lett. 25(5), 700\u2013704 (2018)","journal-title":"IEEE Signal Process. Lett."},{"issue":"6Part35","key":"9_CR20","first-page":"3759","volume":"43","author":"C McCollough","year":"2016","unstructured":"McCollough, C.: TU-FG-207a-04: overview of the low dose CT grand challenge. Med. Phys. 43(6Part35), 3759\u20133760 (2016)","journal-title":"Med. Phys."},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Natterer, F.: The Mathematics of Computerized Tomography. SIAM (2001)","DOI":"10.1137\/1.9780898719284"},{"issue":"17","key":"9_CR22","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."},{"key":"9_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-030-87231-1_9","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"C Wang","year":"2021","unstructured":"Wang, C., et al.: Improving generalizability in limited-angle CT reconstruction with sinogram extrapolation. In: de Bruijn, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 86\u201396. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87231-1_9"},{"issue":"4","key":"9_CR24","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(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR25","unstructured":"Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: The Thirty-Seventh Asilomar Conference on Signals, Systems & Computers 2003, vol. 2, pp. 1398\u20131402. IEEE (2003)"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Xiu, Z., Chen, J., Henao, R., Goldstein, B., Carin, L., Tao, C.: Supercharging imbalanced data learning with energy-based contrastive representation transfer. In: Advances in Neural Information Processing Systems. Curran Associates, Inc. (2021)","DOI":"10.1109\/CVPR46437.2021.01603"},{"issue":"6","key":"9_CR27","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":"9_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-030-87237-3_5","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"S Yu","year":"2021","unstructured":"Yu, S., et al.: MIL-VT: multiple instance learning enhanced vision transformer for fundus image classification. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 45\u201354. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_5"},{"issue":"5","key":"9_CR29","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1109\/TBME.2015.2476371","volume":"63","author":"D Zeng","year":"2015","unstructured":"Zeng, D., et al.: Spectral CT image restoration via an average image-induced nonlocal means filter. IEEE Trans. Biomed. Eng. 63(5), 1044\u20131057 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"9_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-030-87202-1_13","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Pei, Y., Zha, H.: Learning dual transformer network for\u00a0diffeomorphic registration. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 129\u2013138. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_13"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yu, L., Liang, X., Zhao, W., Xing, L.: TransCT: dual-path transformer for low dose computed tomography. arXiv preprint arXiv:2103.00634 (2021)","DOI":"10.1007\/978-3-030-87231-1_6"},{"key":"9_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102289","volume":"75","author":"B Zhou","year":"2021","unstructured":"Zhou, B., Chen, X., Zhou, S.K., Duncan, J.S., Liu, C.: DuDoDR-Net: dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography. Med. Image Anal. 75, 102289 (2021)","journal-title":"Med. Image Anal."},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhou, S.K.: DuDorNet: learning a dual-domain recurrent network for fast mri reconstruction with deep t1 prior. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4273\u20134282 (2020)","DOI":"10.1109\/CVPR42600.2020.00433"},{"key":"9_CR34","unstructured":"Zhou, H.Y., Guo, J., Zhang, Y., Yu, L., Wang, L., Yu, Y.: NNFormer: interleaved transformer for volumetric segmentation. arXiv preprint arXiv:2109.03201 (2021)"},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, S.K., et al.: A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. In: Proceedings of the IEEE (2021)","DOI":"10.1109\/JPROC.2021.3054390"},{"key":"9_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102193","volume":"72","author":"SK Zhou","year":"2021","unstructured":"Zhou, S.K., Le, H.N., Luu, K., Nguyen, H.V., Ayache, N.: Deep reinforcement learning in medical imaging: a literature review. Med. Image Anal. 72, 102193 (2021)","journal-title":"Med. Image Anal."}],"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_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T23:46:36Z","timestamp":1663803996000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17247-2_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031172465","9783031172472"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17247-2_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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)"}}]}}