乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T04:17:38Z","timestamp":1728101858273},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031733598","type":"print"},{"value":"9783031733604","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T00:00:00Z","timestamp":1728086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T00:00:00Z","timestamp":1728086400000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73360-4_11","type":"book-chapter","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T15:02:10Z","timestamp":1728054130000},"page":"99-109","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Conventional Parametric Modeling: Data-Driven Framework for\u00a0Estimation and\u00a0Prediction of\u00a0Time Activity Curves in\u00a0Dynamic PET Imaging"],"prefix":"10.1007","author":[{"given":"Niloufar","family":"Zakariaei","sequence":"first","affiliation":[]},{"given":"Arman","family":"Rahmim","sequence":"additional","affiliation":[]},{"given":"Eldad","family":"Haber","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,5]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Anderson, R.M., May, R.M.: Infectious Diseases of Humans: Dynamics and Control. Oxford University Press (1991)","DOI":"10.1093\/oso\/9780198545996.001.0001"},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1023\/A:1019144409525","volume":"14","author":"U Ascher","year":"1997","unstructured":"Ascher, U.: Stabilization of invariants of discretized differential systems. Numerical Algorithms 14, 1\u201323 (1997)","journal-title":"Numerical Algorithms"},{"key":"11_CR3","volume-title":"Numerical methods for Evolutionary Differential Equations","author":"U Ascher","year":"2010","unstructured":"Ascher, U.: Numerical methods for Evolutionary Differential Equations. SIAM, Philadelphia (2010)"},{"key":"11_CR4","unstructured":"Chen, J., Xu, X., Wu, Y., Zheng, H.: GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction. arXiv preprint arXiv:1812.04206 (2018)"},{"issue":"9","key":"11_CR5","doi-asserted-by":"publisher","first-page":"5023","DOI":"10.3390\/ijms23095023","volume":"23","author":"G Cri\u015fan","year":"2022","unstructured":"Cri\u015fan, G., Moldovean-Cioroianu, N.S., Timaru, D.G., Andrie\u015f, G., C\u0103inap, C., Chi\u015f, V.: Radiopharmaceuticals for pet and spect imaging: a literature review over the last decade. Int. J. Mol. Sci. 23(9), 5023 (2022)","journal-title":"Int. J. Mol. Sci."},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s11537-007-0647-x","volume":"2","author":"F Cucker","year":"2007","unstructured":"Cucker, F., Smale, S.: On the mathematics of emergence. Japan. J. Math. 2, 197\u2013227 (2007)","journal-title":"Japan. J. Math."},{"key":"11_CR7","doi-asserted-by":"publisher","unstructured":"De\u00a0Benetti, F., et al.: Self-supervised learning for physiologically-based pharmacokinetic modeling in dynamic pet. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023, pp. 290\u2013299. Springer Nature Switzerland, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43907-0_28","DOI":"10.1007\/978-3-031-43907-0_28"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Diekmann, O., Heesterbeek, H., Britton, T.: Mathematical Tools for Understanding Infectious Disease Dynamics. Princeton University Press (2012)","DOI":"10.23943\/princeton\/9780691155395.001.0001"},{"key":"11_CR9","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s00259-020-04843-6","volume":"48","author":"A Dimitrakopoulou-Strauss","year":"2021","unstructured":"Dimitrakopoulou-Strauss, A., Pan, L., Sachpekidis, C.: Kinetic modeling and parametric imaging with dynamic pet for oncological applications: general considerations, current clinical applications, and future perspectives. Eur. J. Nucl. Med. Mol. Imaging 48, 21\u201339 (2021)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"11_CR10","unstructured":"Eliasof, M., Haber, E., Treister, E.: Adr-gnn: advection-diffusion-reaction graph neural networks. arXiv preprint arXiv:2307.16092 (2023)"},{"key":"11_CR11","unstructured":"Haber, E., Ruthotto, L.: Stable architectures for deep neural networks. arxiv preprint 1705.03341 abs\/1705.03341, 1\u201321 (2017). http:\/\/arxiv.org\/abs\/1705.03341"},{"key":"11_CR12","doi-asserted-by":"publisher","unstructured":"Jadvar, H., Parker, J.: Clinical PET and PET\/CT. Springer-Verlag London, 1 edn. (2005). https:\/\/doi.org\/10.1007\/b138777","DOI":"10.1007\/b138777"},{"issue":"9","key":"11_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."},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Keeling, M.J., Rohani, P.: Modeling Infectious Diseases in Humans and Animals. Princeton University Press (2008)","DOI":"10.1515\/9781400841035"},{"key":"11_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Kondo, S., Miura, T.: Reaction-diffusion model as a framework for understanding biological pattern formation. Science 329(5999), 1616\u20131620 (2010)","DOI":"10.1126\/science.1179047"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s40336-014-0069-8","volume":"2","author":"FA Kotasidis","year":"2014","unstructured":"Kotasidis, F.A., Tsoumpas, C., Rahmim, A.: Advanced kinetic modelling strategies: towards adoption in clinical pet imaging. Clin. Trans. Imaging 2, 219\u2013237 (2014)","journal-title":"Clin. Trans. Imaging"},{"key":"11_CR18","first-page":"1097","volume":"61","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 61, 1097\u20131105 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"11_CR19","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1016\/B978-012744482-6.50026-0","volume":"46","author":"ED Morris","year":"2004","unstructured":"Morris, E.D., Endres, C.J., Schmidt, K.C., Christian, B.T., Muzic, R.F., Fisher, R.E.: Kinetic modeling in positron emission tomography. Emission tomography: the fundamentals of PET and SPECT 46(1), 499\u2013540 (2004)","journal-title":"Emission tomography: the fundamentals of PET and SPECT"},{"key":"11_CR20","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/0022-5193(82)90063-7","volume":"98","author":"J Murray","year":"1982","unstructured":"Murray, J.: Parameter space for Turing instability in reaction diffusion mechanisms: a comparison of models. J. Theoretical Biology 98, 143\u2013162 (1982)","journal-title":"J. Theoretical Biology"},{"key":"11_CR21","volume-title":"Deblurring Images","author":"J Nagy","year":"2006","unstructured":"Nagy, J., Hansen, P.: Deblurring Images. SIAM, Philadelphia (2006)"},{"key":"11_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physd.2019.04.001","volume":"399","author":"A Panfilov","year":"2019","unstructured":"Panfilov, A., Dierckx, H., Volpert, V.: Reaction-diffusion waves in cardiovascular diseases. Physica D 399, 1\u201334 (2019)","journal-title":"Physica D"},{"key":"11_CR23","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/s00259-018-4153-6","volume":"46","author":"A Rahmim","year":"2019","unstructured":"Rahmim, A., Lodge, M., Karakatsanis, N., et al.: Dynamic whole-body pet imaging: principles, potentials and applications. Eur. J. Nucl. Med. Mol. Imaging 46, 501\u2013518 (2019)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"11_CR24","unstructured":"Ruthotto, L., Haber, E.: Deep neural networks motivated by partial differential equations. arXiv preprint arXiv:1804.04272 (2018)"},{"key":"11_CR25","unstructured":"Ruuth, S.: Implicit-explicit methods for reaction\u2013diffusion problems. In preparation (1993)"},{"key":"11_CR26","unstructured":"Ruuth, S.: Efficient Algorithms for Diffusion-Generated Motion by Mean Curvature. Ph.D. thesis, Institute of Applied Mathematics, University of British Columbia (1996)"},{"key":"11_CR27","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/0022-5193(79)90042-0","volume":"81","author":"J Schnackenberg","year":"1979","unstructured":"Schnackenberg, J.: Simple chemical reaction systems with limit cycle behaviour. J. Theoretical Biology 81, 389\u2013400 (1979)","journal-title":"J. Theoretical Biology"},{"issue":"1","key":"11_CR28","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1109\/TMI.2019.2922448","volume":"39","author":"M Scipioni","year":"2020","unstructured":"Scipioni, M., Pedemonte, S., Santarelli, M.F., Landini, L.: Probabilistic graphical models for dynamic pet: a novel approach to direct parametric map estimation and image reconstruction. IEEE Trans. Med. Imaging 39(1), 152\u2013160 (2020). https:\/\/doi.org\/10.1109\/TMI.2019.2922448","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1007\/978-3-030-04167-0_33","volume-title":"Neural Information Processing","author":"Y Seo","year":"2018","unstructured":"Seo, Y., Defferrard, M., Vandergheynst, P., Bresson, X.: Structured sequence modeling with graph convolutional recurrent networks. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11301, pp. 362\u2013373. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-04167-0_33"},{"key":"11_CR30","unstructured":"Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems 32 (2019)"},{"issue":"24","key":"11_CR31","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.1001\/jama.2020.8420","volume":"323","author":"J Tolles","year":"2020","unstructured":"Tolles, J., Luong, T.: Modeling epidemics with compartmental models. JAMA 323(24), 2515\u20132516 (2020). https:\/\/doi.org\/10.1001\/jama.2020.8420","journal-title":"JAMA"},{"key":"11_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2023.101212","author":"J Trotter","year":"2023","unstructured":"Trotter, J., et al.: Positron emission tomography (pet)\/computed tomography (ct) imaging in radiation therapy treatment planning: a review of pet imaging tracers and methods to incorporate pet\/ct. Adv. Radiat. Oncol. (2023). https:\/\/doi.org\/10.1016\/j.adro.2023.101212","journal-title":"Adv. Radiat. Oncol."},{"issue":"1","key":"11_CR33","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/S0092-8240(05)80008-4","volume":"52","author":"AM Turing","year":"1990","unstructured":"Turing, A.M.: The chemical basis of morphogenesis. Bull. Math. Biol. 52(1), 153\u2013197 (1990)","journal-title":"Bull. Math. Biol."},{"issue":"6","key":"11_CR34","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1109\/TRPMS.2020.3025086","volume":"4","author":"G Wang","year":"2020","unstructured":"Wang, G., Rahmim, A., Gunn, R.N.: Pet parametric imaging: past, present, and future. IEEE Trans. Radiation Plasma Med. Sci. 4(6), 663\u2013675 (2020). https:\/\/doi.org\/10.1109\/TRPMS.2020.3025086","journal-title":"IEEE Trans. Radiation Plasma Med. Sci."},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Networks Learn. Syst. (2020)","DOI":"10.1109\/TNNLS.2020.2978386"}],"container-title":["Lecture Notes in Computer Science","Computational Mathematics Modeling in Cancer Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73360-4_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T15:03:26Z","timestamp":1728054206000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73360-4_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,5]]},"ISBN":["9783031733598","9783031733604"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73360-4_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,5]]},"assertion":[{"value":"5 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CMMCA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Computational Mathematics Modeling in Cancer Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cmmca2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cmmcaworkshop.github.io\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}