{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T18:23:50Z","timestamp":1792002230605,"version":"4.4.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439926","type":"print"},{"value":"9783031439933","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43993-3_9","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"88-98","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Simulation of\u00a0Arbitrary Level Contrast Dose in\u00a0MRI Using an\u00a0Iterative Global Transformer Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0531-2110","authenticated-orcid":false,"given":"Dayang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3009-5197","authenticated-orcid":false,"given":"Srivathsa","family":"Pasumarthi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5781-8848","authenticated-orcid":false,"given":"Greg","family":"Zaharchuk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2705-1268","authenticated-orcid":false,"given":"Ryan","family":"Chamberlain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Minton, L.E., Pandit, R., Porter, K.K.: Contrast-enhanced MRI: history and current recommendations. Appl. Radiol. 50(6), 15\u201319 (2021)","DOI":"10.37549\/AR2776"},{"issue":"4","key":"9_CR2","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1093\/ndt\/gfk062","volume":"21","author":"T Grobner","year":"2006","unstructured":"Grobner, T.: Gadolinium-a specific trigger for the development of nephrogenic fibrosing dermopathy and nephrogenic systemic fibrosis? Nephrol. Dial. Transplant. 21(4), 1104\u20131108 (2006)","journal-title":"Nephrol. Dial. Transplant."},{"key":"9_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.watres.2020.115966","volume":"182","author":"R Br\u00fcnjes","year":"2020","unstructured":"Br\u00fcnjes, R., Hofmann, T.: Anthropogenic gadolinium in freshwater and drinking water systems. Water Res. 182, 115966 (2020)","journal-title":"Water Res."},{"issue":"2","key":"9_CR4","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.ejrad.2009.05.060","volume":"72","author":"M Uffmann","year":"2009","unstructured":"Uffmann, M., Schaefer-Prokop, C.: Digital radiography: the balance between image quality and required radiation dose. Eur. J. Radiol. 72(2), 202\u2013208 (2009)","journal-title":"Eur. J. Radiol."},{"issue":"3","key":"9_CR5","doi-asserted-by":"publisher","first-page":"1687","DOI":"10.1002\/mrm.28808","volume":"86","author":"S Pasumarthi","year":"2021","unstructured":"Pasumarthi, S., Tamir, J.I., Christensen, S., Zaharchuk, G., Zhang, T., Gong, E.: A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI. Magn. Reson. Med. 86(3), 1687\u20131700 (2021)","journal-title":"Magn. Reson. Med."},{"issue":"2","key":"9_CR6","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1002\/jmri.25970","volume":"48","author":"E Gong","year":"2018","unstructured":"Gong, E., Pauly, J.M., Wintermark, M., Zaharchuk, G.: Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI. J. Magn. Reson. Imaging 48(2), 330\u2013340 (2018)","journal-title":"J. Magn. Reson. Imaging"},{"issue":"10","key":"9_CR7","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1097\/RLI.0000000000000583","volume":"54","author":"J Kleesiek","year":"2019","unstructured":"Kleesiek, J., et al.: Can virtual contrast enhancement in brain MRI replace gadolinium?: A feasibility study. Invest. Radiol. 54(10), 653\u2013660 (2019)","journal-title":"Invest. Radiol."},{"issue":"2","key":"9_CR8","doi-asserted-by":"publisher","first-page":"R1","DOI":"10.1088\/0031-9155\/57\/2\/R1","volume":"57","author":"S Sourbron","year":"2011","unstructured":"Sourbron, S., Buckley, D.L.: Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys. Med. Biol. 57(2), R1 (2011)","journal-title":"Phys. Med. Biol."},{"key":"9_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1007\/978-3-030-87589-3_43","volume-title":"Machine Learning in Medical Imaging","author":"D Wang","year":"2021","unstructured":"Wang, D., Wu, Z., Yu, H.: TED-Net: convolution-free T2T vision transformer-based encoder-decoder dilation network for low-dose CT\u00a0denoising. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds.) MLMI 2021. LNCS, vol. 12966, pp. 416\u2013425. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87589-3_43"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Liu, J., Pasumarthi, S., Duffy, B., Gong, E., Zaharchuk, G., Datta, K.: One model to synthesize them all: Multi-contrast multi-scale transformer for missing data imputation. arXiv preprint arXiv:2204.13738 (2022)","DOI":"10.1109\/TMI.2023.3261707"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Antipov, G., Baccouche, M., Dugelay, J.-L.: Face aging with conditional generative adversarial networks. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2089\u20132093. IEEE (2017)","DOI":"10.1109\/ICIP.2017.8296650"},{"key":"9_CR12","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1109\/TCI.2019.2956877","volume":"6","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Liu, Q., Zhang, M., Yang, Q., Wang, S., Liang, D.: IFR-Net: iterative feature refinement network for compressed sensing MRI. IEEE Trans. Comput. Imaging 6, 434\u2013446 (2019)","journal-title":"IEEE Trans. Comput. Imaging"},{"issue":"6","key":"9_CR13","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1038\/s42256-019-0057-9","volume":"1","author":"H Shan","year":"2019","unstructured":"Shan, H., et al.: Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction. Nat. Mach. Intell. 1(6), 269\u2013276 (2019)","journal-title":"Nat. Mach. Intell."},{"issue":"11","key":"9_CR14","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"9_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Marstal, K., Berendsen, F., Staring, M., Klein, S.: SimpleElastix: a user-friendly, multi-lingual library for medical image registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 134\u2013142 (2016)","DOI":"10.1109\/CVPRW.2016.78"},{"key":"9_CR18","unstructured":"Schell, M., et al.: Automated brain extraction of multi-sequence MRI using artificial neural networks. In: European Congress of Radiology-ECR 2019 (2019)"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"issue":"12","key":"9_CR20","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_CR21","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H.: Restormer: efficient transformer for high-resolution image restoration. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: SwiniR: image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"9_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","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"},{"key":"9_CR24","doi-asserted-by":"publisher","unstructured":"Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) Computer Vision-ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science. vol. 13803. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9","DOI":"10.1007\/978-3-031-25066-8_9"},{"issue":"8","key":"9_CR25","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1097\/RLI.0000000000000867","volume":"57","author":"A B\u00f4ne","year":"2022","unstructured":"B\u00f4ne, A., et al.: From dose reduction to contrast maximization: can deep learning amplify the impact of contrast media on brain magnetic resonance image quality? A reader study. Invest. Radiol. 57(8), 527\u2013535 (2022)","journal-title":"Invest. Radiol."},{"key":"9_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-030-11726-9_28","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"A Myronenko","year":"2019","unstructured":"Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311\u2013320. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_28"},{"issue":"6","key":"9_CR27","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/S0730-725X(03)00096-1","volume":"21","author":"J M\u00f8rkenborg","year":"2003","unstructured":"M\u00f8rkenborg, J., Pedersen, M., Jensen, F., St\u00f8dkilde-J\u00f8rgensen, H., Djurhuus, J., Fr\u00f8ki\u00e6r, J.: Quantitative assessment of GD-DTPA contrast agent from signal enhancement: an in-vitro study. Magn. Reson. Imaging 21(6), 637\u2013643 (2003)","journal-title":"Magn. Reson. Imaging"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43993-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T16:13:48Z","timestamp":1712074428000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43993-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439926","9783031439933"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43993-3_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"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":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","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":"2250","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":"730","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":"32% - 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","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":"5","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)"}}]}}