{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T13:20:54Z","timestamp":1726233654518},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031456725"},{"type":"electronic","value":"9783031456732"}],"license":[{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-45673-2_2","type":"book-chapter","created":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T04:02:16Z","timestamp":1697256136000},"page":"12-22","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Domain Iterative Network for\u00a0Simultaneous Denoising, Limited-Angle Reconstruction, and\u00a0Attenuation Correction of\u00a0Cardiac SPECT"],"prefix":"10.1007","author":[{"given":"Xiongchao","family":"Chen","sequence":"first","affiliation":[]},{"given":"Bo","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Huidong","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Xueqi","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Qiong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Albert J.","family":"Sinusas","sequence":"additional","affiliation":[]},{"given":"Chi","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,15]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","first-page":"1508","DOI":"10.1007\/s00259-021-05614-7","volume":"49","author":"N Aghakhan Olia","year":"2022","unstructured":"Aghakhan Olia, N., et al.: Deep learning-based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance. Eur. J. Nucl. Med. Mol. Imaging 49, 1508\u20131522 (2022)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Amirrashedi, M., Sarkar, S., Ghadiri, H., Ghafarian, P., Zaidi, H., Ay, M.R.: A deep neural network to recover missing data in small animal pet imaging: comparison between sinogram-and image-domain implementations. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1365\u20131368. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9433923"},{"issue":"1","key":"2_CR3","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1118\/1.4938098","volume":"43","author":"C Chan","year":"2016","unstructured":"Chan, C., et al.: The impact of system matrix dimension on small FOV SPECT reconstruction with truncated projections. Med. Phys. 43(1), 213\u2013224 (2016)","journal-title":"Med. Phys."},{"issue":"6","key":"2_CR4","doi-asserted-by":"publisher","first-page":"3379","DOI":"10.1007\/s12350-022-02978-7","volume":"29","author":"X Chen","year":"2022","unstructured":"Chen, X., et al.: Cross-vender, cross-tracer, and cross-protocol deep transfer learning for attenuation map generation of cardiac SPECT. J. Nucl. Cardiol. 29(6), 3379\u20133391 (2022)","journal-title":"J. Nucl. Cardiol."},{"key":"2_CR5","unstructured":"Chen, X., Liu, C.: Deep-learning-based methods of attenuation correction for SPECT and PET. J. Nuclear Cardiol. pp. 1\u201320 (2022)"},{"key":"2_CR6","unstructured":"Chen, X., Peng, Z., Valadez, G.H.: DD-CISENet: dual-domain cross-iteration squeeze and excitation network for accelerated MRI reconstruction. arXiv preprint arXiv:2305.00088 (2023)"},{"key":"2_CR7","unstructured":"Chen, X., Shinagawa, Y., Peng, Z., Valadez, G.H.: Dual-domain cross-iteration squeeze-excitation network for sparse reconstruction of brain MRI. arXiv preprint arXiv:2210.02523 (2022)"},{"issue":"5","key":"2_CR8","doi-asserted-by":"publisher","first-page":"2235","DOI":"10.1007\/s12350-021-02672-0","volume":"29","author":"X Chen","year":"2022","unstructured":"Chen, X., et al.: CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network. J. Nucl. Cardiol. 29(5), 2235\u20132250 (2022)","journal-title":"J. Nucl. Cardiol."},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"102840","DOI":"10.1016\/j.media.2023.102840","volume":"88","author":"X Chen","year":"2023","unstructured":"Chen, X., et al.: DuSFE: dual-channel squeeze-fusion-excitation co-attention for cross-modality registration of cardiac SPECT and CT. Med. Image Anal. 88, 102840 (2023)","journal-title":"Med. Image Anal."},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Chen, X., et al.: Dual-branch squeeze-fusion-excitation module for cross-modality registration of cardiac SPECT and CT. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol. 13436, pp. 46\u201355. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16446-0_5","DOI":"10.1007\/978-3-031-16446-0_5"},{"issue":"1","key":"2_CR11","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1002\/mp.15958","volume":"50","author":"X Chen","year":"2022","unstructured":"Chen, X., et al.: DuDoSS: deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT. Med. Phys. 50(1), 89\u2013103 (2022)","journal-title":"Med. Phys."},{"issue":"9","key":"2_CR12","doi-asserted-by":"publisher","first-page":"3046","DOI":"10.1007\/s00259-022-05718-8","volume":"49","author":"X Chen","year":"2022","unstructured":"Chen, X., et al.: Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT. Eur. J. Nucl. Med. Mol. Imaging 49(9), 3046\u20133060 (2022)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"10","key":"2_CR13","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1001\/jamacardio.2017.2471","volume":"2","author":"I Danad","year":"2017","unstructured":"Danad, I., et al.: Comparison of coronary CT angiography, SPECT, PET, and hybrid imaging for diagnosis of ischemic heart disease determined by fractional flow reserve. JAMA Cardiol. 2(10), 1100\u20131107 (2017)","journal-title":"JAMA Cardiol."},{"key":"2_CR14","unstructured":"GE-HealthCare: Ge myospect es: a perfect fit for today\u2019s practice of cardiology. https:\/\/www.gehealthcare.com\/products\/molecular-imaging\/myospect (2023)"},{"issue":"7","key":"2_CR15","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.2967\/jnumed.107.040535","volume":"48","author":"S Goetze","year":"2007","unstructured":"Goetze, S., Brown, T.L., Lavely, W.C., Zhang, Z., Bengel, F.M.: Attenuation correction in myocardial perfusion SPECT\/CT: effects of misregistration and value of reregistration. J. Nucl. Med. 48(7), 1090\u20131095 (2007)","journal-title":"J. Nucl. Med."},{"key":"2_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"12","key":"2_CR17","doi-asserted-by":"publisher","first-page":"2997","DOI":"10.1088\/0031-9155\/59\/12\/2997","volume":"59","author":"S Niu","year":"2014","unstructured":"Niu, S., et al.: Sparse-view x-ray CT reconstruction via total generalized variation regularization. Phys. Med. Biol. 59(12), 2997 (2014)","journal-title":"Phys. Med. Biol."},{"key":"2_CR18","unstructured":"Oktay, O., et al.: Attention u-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"2_CR19","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"issue":"8","key":"2_CR20","doi-asserted-by":"publisher","first-page":"084002","DOI":"10.1088\/1361-6420\/ab958b","volume":"36","author":"MA Rahman","year":"2020","unstructured":"Rahman, M.A., Zhu, Y., Clarkson, E., Kupinski, M.A., Frey, E.C., Jha, A.K.: Fisher information analysis of list-mode SPECT emission data for joint estimation of activity and attenuation distribution. Inverse Prob. 36(8), 084002 (2020)","journal-title":"Inverse Prob."},{"key":"2_CR21","doi-asserted-by":"publisher","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","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2_CR22","doi-asserted-by":"publisher","first-page":"2383","DOI":"10.1007\/s00259-020-04746-6","volume":"47","author":"L Shi","year":"2020","unstructured":"Shi, L., Onofrey, J.A., Liu, H., Liu, Y.H., Liu, C.: Deep learning-based attenuation map generation for myocardial perfusion SPECT. Eur. J. Nucl. Med. Mol. Imaging 47, 2383\u20132395 (2020)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"6","key":"2_CR23","doi-asserted-by":"publisher","first-page":"2761","DOI":"10.1007\/s12350-020-02119-y","volume":"28","author":"I Shiri","year":"2020","unstructured":"Shiri, I., et al.: Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks. J. Nucl. Cardiol. 28(6), 2761\u20132779 (2020)","journal-title":"J. Nucl. Cardiol."},{"issue":"3","key":"2_CR24","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1007\/s12350-022-03045-x","volume":"30","author":"J Sun","year":"2022","unstructured":"Sun, J., et al.: Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT. J. Nucl. Cardiol. 30(3), 970\u2013985 (2022)","journal-title":"J. Nucl. Cardiol."},{"issue":"1","key":"2_CR25","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1007\/s12350-018-1378-5","volume":"27","author":"RG Wells","year":"2020","unstructured":"Wells, R.G.: Dose reduction is good but it is image quality that matters. J. Nucl. Cardiol. 27(1), 238\u2013240 (2020)","journal-title":"J. Nucl. Cardiol."},{"issue":"23","key":"2_CR26","doi-asserted-by":"publisher","first-page":"235017","DOI":"10.1088\/1361-6560\/ab4919","volume":"64","author":"W Whiteley","year":"2019","unstructured":"Whiteley, W., Gregor, J.: CNN-based pet sinogram repair to mitigate defective block detectors. Phys. Med. Biol. 64(23), 235017 (2019)","journal-title":"Phys. Med. Biol."},{"key":"2_CR27","unstructured":"You, K., Long, M., Wang, J., Jordan, M.I.: How does learning rate decay help modern neural networks? arXiv preprint arXiv:1908.01878 (2019)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45673-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T12:52:25Z","timestamp":1710334345000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45673-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,15]]},"ISBN":["9783031456725","9783031456732"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45673-2_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,10,15]]},"assertion":[{"value":"15 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2023?pli=1","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":"139","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":"93","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":"67% - 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","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":"4","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)"}}]}}