{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T11:24:07Z","timestamp":1780572247010,"version":"3.54.1"},"reference-count":167,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["R01 EB000194"],"award-info":[{"award-number":["R01 EB000194"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["R03 EB027268"],"award-info":[{"award-number":["R03 EB027268"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. IEEE"],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1109\/jproc.2019.2936809","type":"journal-article","created":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T19:50:03Z","timestamp":1568922603000},"page":"51-68","source":"Crossref","is-referenced-by-count":94,"title":["Machine Learning in PET: From Photon Detection to Quantitative Image Reconstruction"],"prefix":"10.1109","volume":"108","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2669-2610","authenticated-orcid":false,"given":"Kuang","family":"Gong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4326-2986","authenticated-orcid":false,"given":"Eric","family":"Berg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0155-5644","authenticated-orcid":false,"given":"Simon R.","family":"Cherry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5428-0322","authenticated-orcid":false,"given":"Jinyi","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa9dc5"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2009.2021428"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2006.10.088"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2014.2375557"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/54\/7\/003"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2006.873711"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2011.2140382"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2014.2372788"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2006.10.073"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/48\/7\/302"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/aaef03"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2018.2884320"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/61\/5\/2196"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2004.835782"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1088\/1748-0221\/7\/06\/C06010"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2004.08.035"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/58\/5\/1375"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2003.812434"},{"key":"ref101","first-page":"569","article-title":"A deep learning-based approach for direct whole-body PET attenuation correction","volume":"60","author":"hemmen","year":"2019","journal-title":"J Nucl Med"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2018.2837738"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06229-1"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-9473(01)00065-2"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/58\/14\/4807"},{"key":"ref51","article-title":"Improving time-of-flight performance of BGO-SiPM detectors using convolutional neural networks","author":"berg","year":"2018","journal-title":"Proc IEEE Nucl Sci Symp Med Imag Conf"},{"key":"ref154","article-title":"CT-guided PET parametric image reconstruction using deep neural network without prior training data","volume":"10948","author":"cui","year":"2019","journal-title":"Proc SPIE"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00928-1_6"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2936204"},{"key":"ref155","first-page":"108","article-title":"Noise reduction with cross-tracer transfer deep learning for low-dose oncological PET","volume":"60","author":"liu","year":"2019","journal-title":"J Nucl Med"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1007\/1-84628-007-9_6"},{"key":"ref152","article-title":"Use of a 4D deep autoencoder to denoise dynamic PET data","author":"klyuzhin","year":"2018","journal-title":"Proc IEEE Nucl Sci Symp Med Imag Conf (NSS\/MIC)"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0184667"},{"key":"ref146","first-page":"425s","article-title":"BrainWeb: Online interface to a 3D MRI simulated brain database","volume":"5","author":"cocosco","year":"1997","journal-title":"NeuroImage"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1118\/1.3480985"},{"key":"ref148","article-title":"Deep image prior","author":"ulyanov","year":"2017","journal-title":"arXiv 1711 10925"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.7150\/thno.5130"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2832613"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2869871"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2888491"},{"key":"ref56","first-page":"859","article-title":"Correction methods for random coincidences in fully 3D whole-body PET: Impact on data and image quality","volume":"46","author":"brasse","year":"2005","journal-title":"J Nucl Med"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/44\/2\/020"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2711479"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1118\/1.595715"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/43\/4\/027"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref167","article-title":"Initial investigation of low-dose SPECT-MPI via deep learning","author":"ramon","year":"2018","journal-title":"Proc IEEE Nucl Sci Symp Med Imag Conf (NSS\/MIC)"},{"key":"ref166","article-title":"DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem","author":"h\u00e4ggstr\u00f6m","year":"2018","journal-title":"arXiv 1804 07851"},{"key":"ref165","doi-asserted-by":"crossref","DOI":"10.1117\/12.2534904","article-title":"MAPEM-net: An unrolled neural network for fully 3D PET image reconstruction","volume":"11072","author":"gong","year":"2019","journal-title":"Proc SPIE 15th Int Meeting Fully Three-Dimensional Image Reconstruct Radiol Nucl Med"},{"key":"ref164","article-title":"Improved low-count quantitative PET reconstruction with a variational neural network","author":"lim","year":"2019","journal-title":"arXiv 1906 02327"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2929230"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1117\/12.2534902"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2846226"},{"key":"ref160","article-title":"Self-attention generative adversarial networks","author":"zhang","year":"2018","journal-title":"arXiv 1805 08318"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa5e46"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1118\/1.2174132"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/60\/15\/5733"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.116.184028"},{"key":"ref159","doi-asserted-by":"crossref","DOI":"10.1117\/12.2534842","article-title":"Generative adversarial networks based regularized image reconstruction for PET","volume":"11072","author":"xie","year":"2019","journal-title":"Proc SPIE 15th Int Meeting Fully Three-Dimensional Image Reconstruct Radiol Nucl Med"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1088\/1748-0221\/8\/02\/C02050"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/61\/13\/4904"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2004.07.024"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.213"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2343916"},{"key":"ref9","first-page":"434","article-title":"Characterization of the vereos digital photon counting PET system","volume":"56","author":"miller","year":"2015","journal-title":"J Nucl Med"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2014.2303119"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2013.2264947"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2011.2150762"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2010.2081685"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/61\/18\/L38"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa6a49"},{"key":"ref44","first-page":"1512","article-title":"Design and performance of the 6 GHz waveform digitizing chip DRS4","author":"ritt","year":"2018","journal-title":"Proc IEEE Nucl Sci Symp Conf Rec (NSS)"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2014.12.005"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12937"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa7b49"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1053\/j.semnuclmed.2012.08.006"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1118\/1.4928400"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.116.175398"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/60\/20\/8047"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.111.092577"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.109.065425"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.116.188268"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2662206"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/47\/7\/310"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12122"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa8851"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.03.045"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2018.2877644"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.06.048"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2461533"},{"key":"ref132","article-title":"200x Low-dose PET reconstruction using deep learning","author":"xu","year":"2017","journal-title":"arXiv 1712 04119"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1118\/1.3578928"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-019-04468-4"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018180940"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab0dc0"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1109\/NSSMIC.2017.8532624"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2013.2273417"},{"key":"ref139","first-page":"99a","article-title":"Brain PET dose reduction using a shallow artificial neural network","volume":"59","author":"yang","year":"2018","journal-title":"J Nucl Med"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.113.136341"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2340135"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017161603"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/57\/4\/885"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0150-3"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2212719"},{"key":"ref141","article-title":"Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss","author":"ouyang","year":"0","journal-title":"Med Phys"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2409157"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2803681"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.4.2.023502"},{"key":"ref143","article-title":"Super-resolution PET imaging using convolutional neural networks","author":"song","year":"2019","journal-title":"arXiv 1906 03645"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aaa8a6"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2830381"},{"key":"ref2","first-page":"73","article-title":"Time-of-flight positron emission tomography: Status relative to conventional PET","volume":"24","author":"budinger","year":"1983","journal-title":"J Nucl Med"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.108.054726"},{"key":"ref145","article-title":"Deep-fill: Deep learning based sinogram domain gap filling in positron emission tomography","author":"shiri","year":"2019","journal-title":"arXiv 1906 07168"},{"key":"ref1","article-title":"Performance characteristics of the digital biograph vision PET\/CT system","author":"sluis","year":"0","journal-title":"J Nucl Med"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2827462"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.118.209288"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2708987"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.117.198051"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.117.202317"},{"key":"ref106","article-title":"Wasserstein gan","author":"arjovsky","year":"2017","journal-title":"arXiv 1701 07875"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.118.214320"},{"key":"ref105","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.01005"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-019-04380-x"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aac763"},{"key":"ref103","first-page":"14","article-title":"Deep MR to CT synthesis using unpaired data","author":"wolterink","year":"2017","journal-title":"Proc Int Workshop Simul Synthesis Med Imag"},{"key":"ref102","first-page":"417","article-title":"Medical image synthesis with context-aware generative adversarial networks","author":"nie","year":"2017","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/23.873020"},{"key":"ref112","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/978-94-015-8749-5_18","article-title":"A single scatter simulation technique for scatter correction in 3D PET","author":"watson","year":"1996","journal-title":"Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab0606"},{"key":"ref96","article-title":"Generation of PET attenuation map for whole-body time-of-flight 18 F-FDG PET\/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps","author":"hwang","year":"0","journal-title":"J Nucl Med"},{"key":"ref97","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.1986.4337143"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1063\/1.1715998"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/56\/8\/004"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/S0168-9002(02)00861-6"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/54\/11\/015"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/23.159655"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2013.2292881"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2008.922811"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1007\/s10334-012-0334-7"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/NSSMIC.2017.8533103"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1053\/j.semnuclmed.2012.08.002"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009715923555"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-008-1007-7"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0081390"},{"key":"ref19","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1118\/1.4941014"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/NSSMIC.2017.8532979"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13274"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1109\/NSSMIC.2010.5874281"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1007\/s10334-012-0353-4"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/NSSMIC.2018.8824594"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.109.073999"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2017170700"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/60\/3\/961"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2564440"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2018.01.083"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.1996.0066"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-012-2113-0"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2013.2278624"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12155"}],"container-title":["Proceedings of the IEEE"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5\/8944310\/08844693.pdf?arnumber=8844693","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T13:39:54Z","timestamp":1651066794000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8844693\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1]]},"references-count":167,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/jproc.2019.2936809","relation":{},"ISSN":["0018-9219","1558-2256"],"issn-type":[{"value":"0018-9219","type":"print"},{"value":"1558-2256","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1]]}}}