Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging

Hammernik, Kerstin; Kustner, Thomas; Yaman, Burhaneddin; Huang, Zhengnan; Rueckert, Daniel; Knoll, Florian; Akcakaya, Mehmet

dc.contributor.author	Küstner, Thomas
dc.date.accessioned	2023-10-10T09:19:53Z
dc.date.available	2023-10-10T09:19:53Z
dc.date.issued	2023
dc.identifier.issn	1053-5888
dc.identifier.uri	http://hdl.handle.net/10900/146111
dc.language.iso	en	de_DE
dc.publisher	IEEE - Inst Electrical Electronics Engineers Inc	de_DE
dc.relation.uri	http://dx.doi.org/10.1109/MSP.2022.3215288	de_DE
dc.subject.ddc	600	de_DE
dc.title	Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging	de_DE
dc.type	Article	de_DE
utue.quellen.id	20230619000000_01696
utue.publikation.seiten	98-114	de_DE
utue.personen.roh	Hammernik, Kerstin
utue.personen.roh	Kustner, Thomas
utue.personen.roh	Yaman, Burhaneddin
utue.personen.roh	Huang, Zhengnan
utue.personen.roh	Rueckert, Daniel
utue.personen.roh	Knoll, Florian
utue.personen.roh	Akcakaya, Mehmet
dcterms.isPartOf.ZSTitelID	IEEE Signal Processing Magazine	de_DE
dcterms.isPartOf.ZS-Issue	1	de_DE
dcterms.isPartOf.ZS-Volume	40	de_DE
utue.fakultaet	04 Medizinische Fakultät	de_DE