Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP

Birk, Florian; Mahler, Lucas; Steiglechner, Julius; Wang, Qi; Scheffler, Klaus; Heule, Rahel

dc.contributor.author	Birk, Florian
dc.contributor.author	Mahler, Lucas
dc.contributor.author	Steiglechner, Julius
dc.contributor.author	Wang, Qi
dc.contributor.author	Scheffler, Klaus
dc.contributor.author	Heule, Rahel
dc.date.accessioned	2025-12-22T07:17:45Z
dc.date.available	2025-12-22T07:17:45Z
dc.date.issued	2025
dc.identifier.issn	2045-2322
dc.identifier.uri	http://hdl.handle.net/10900/173418
dc.language.iso	en	de_DE
dc.publisher	Berlin : Nature Portfolio	de_DE
dc.relation.uri	http://dx.doi.org/10.1038/s41598-025-88579-z	de_DE
dc.subject.ddc	500	de_DE
dc.title	Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP	de_DE
dc.type	Article	de_DE
utue.quellen.id	20250630000000_01759
utue.personen.roh	Birk, Florian
utue.personen.roh	Mahler, Lucas
utue.personen.roh	Steiglechner, Julius
utue.personen.roh	Wang, Qi
utue.personen.roh	Scheffler, Klaus
utue.personen.roh	Heule, Rahel
dcterms.isPartOf.ZSTitelID	Scientific Reports	de_DE
dcterms.isPartOf.ZS-Issue	Article 4825	de_DE
dcterms.isPartOf.ZS-Volume	15 (1)	de_DE
utue.fakultaet	04 Medizinische Fakultät	de_DE