{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T21:04:24Z","timestamp":1761253464539,"version":"3.37.3"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100010686","name":"Project P3-Stroke","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010686","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014419","name":"EIT Health Innovation Project","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014419","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014419","name":"EIT Health through EIT","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014419","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Body of the European Union"},{"DOI":"10.13039\/501100001652","name":"Emerging Fields Initiative (EFI) of the Friedrich-Alexander University Erlangen-N\u00fcrnberg","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001652","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1109\/tmi.2020.2998179","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T20:39:15Z","timestamp":1590698355000},"page":"3488-3498","source":"Crossref","is-referenced-by-count":7,"title":["Known Operator Learning Enables Constrained Projection Geometry Conversion: Parallel to Cone-Beam for Hybrid MR\/X-Ray Imaging"],"prefix":"10.1109","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9514-5389","authenticated-orcid":false,"given":"Christopher","family":"Syben","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0573-9838","authenticated-orcid":false,"given":"Bernhard","family":"Stimpel","sequence":"additional","affiliation":[]},{"given":"Philipp","family":"Roser","sequence":"additional","affiliation":[]},{"given":"Arnd","family":"Dorfler","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9550-5284","authenticated-orcid":false,"given":"Andreas","family":"Maier","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref31","article-title":"MRzero—Fully automated invention of MRI sequences using supervised learning","author":"loktyushin","year":"2020","journal-title":"arXiv 2002 04265"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20351-1_61"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/aa9581"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/aaf14a"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2799231"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/nature25988"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.zemedi.2018.12.003"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00928-1_17"},{"key":"ref16","article-title":"On instabilities of deep learning in image reconstruction–does AI come at a cost?","author":"antun","year":"2019","journal-title":"arXiv 1902 05300"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0077-5"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32239-7_21"},{"key":"ref19","article-title":"Big in japan: Regularizing networks for solving inverse problems","author":"schwab","year":"2018","journal-title":"arXiv 1812 00965"},{"key":"ref28","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"Proc of USENIX Symp on Operating Systems Design and Implementation (OSDI)"},{"article-title":"Methods for performing digital subtraction angiography, hybrid imaging devices, computer programs, and electronically readable storage media","year":"2019","author":"leghissa","key":"ref4"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13753"},{"key":"ref3","first-page":"503","article-title":"Deriving neural network architectures using precision learning: Parallel-to-fan beam conversion","author":"syben","year":"2018","journal-title":"Proc German Conf Pattern Recognit"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aac5b9"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/NSSMIC.2018.8824435"},{"key":"ref5","first-page":"1","article-title":"MR-projection imaging with perspective distortion as in X-ray fluoroscopy for interventional X\/MR-hybrid applications","author":"lommen","year":"2018","journal-title":"12th Interventional MRI Symposium"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2713099"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1017\/S0962492918000016"},{"key":"ref2","article-title":"Multi-modal deep guided filtering for comprehensible medical image processing supplementary material","author":"stimpel","year":"2019","journal-title":"IEEE Trans Med Imag"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2833635"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-55108-8","article-title":"projection-to-projection translation for hybrid X-ray andmagnetic resonance imaging","volume":"9","author":"stimpel","year":"2019","journal-title":"Sci Rep"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2833499"},{"journal-title":"Principles of Computerized Tomographic Imaging","year":"1988","author":"kak","key":"ref22"},{"key":"ref21","first-page":"386","article-title":"Precision learning: Reconstruction filter kernel discretization","author":"syben","year":"2018","journal-title":"Proc 5th Int Conf Image Formation X-ray Comput Tomogr"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1117\/12.2292384"},{"article-title":"C-arm computed tomography with extended axial field-of-view","year":"2014","author":"yu","key":"ref23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139047838"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-96520-8_1"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/9242349\/09103110.pdf?arnumber=9103110","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T13:58:36Z","timestamp":1651067916000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9103110\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11]]},"references-count":31,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2020.2998179","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"type":"print","value":"0278-0062"},{"type":"electronic","value":"1558-254X"}],"subject":[],"published":{"date-parts":[[2020,11]]}}}