Backhaus, Sören Jan

[["dc.bibliographiccitation.artnumber","oeac053"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","European Heart Journal Open"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Backhaus, Sören J"],["dc.contributor.author","Rösel, Simon F"],["dc.contributor.author","Stiermaier, Thomas"],["dc.contributor.author","Schmidt-Rimpler, Jonas"],["dc.contributor.author","Evertz, Ruben"],["dc.contributor.author","Schulz, Alexander"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Kowallick, Johannes T"],["dc.contributor.author","Kutty, Shelby"],["dc.contributor.author","Bigalke, Boris"],["dc.contributor.editor","Gimelli, Alessia"],["dc.date.accessioned","2022-11-01T10:16:55Z"],["dc.date.available","2022-11-01T10:16:55Z"],["dc.date.issued","2022"],["dc.description.abstract","Abstract\n \n Aims\n Deformation imaging enables optimized risk prediction following acute myocardial infarction (AMI). However, costly and time-consuming post processing has hindered widespread clinical implementation. Since manual left-ventricular long-axis strain (LV LAS) has been successfully proposed as a simple alternative for LV deformation imaging, we aimed at the validation of left-atrial (LA) LAS.\n \n \n Methods and results\n The AIDA STEMI and TATORT-NSTEMI trials recruited 795 patients with ST-elevation myocardial infarction and 440 with non-ST-elevation myocardial infarction. LA LAS was assessed as the systolic distance change between the middle of a line connecting the origins of the mitral leaflets and either a perpendicular line towards the posterior atrial wall (LAS90) or a line connecting to the LA posterior portion of the greatest distance irrespective of a predefined angle (LAS). Primary endpoint was major adverse cardiac event (MACE) occurrence within 12 months. There were no significant differences between LA LAS and LAS90, both with excellent reproducibility. LA LAS correlated significantly with LA reservoir function (Es, r = 0.60, P < 0.001). Impaired LA LAS resulted in higher MACE occurrence [hazard ratio (HR) 0.85, 95% confidence interval (CI) 0.82–0.88, P < 0.001]. LA LAS (HR 0.90, 95% CI 0.83–0.97, P = 0.005) and LV global longitudinal strain (GLS, P = 0.025) were the only independent predictors for MACE in multivariate analyses. C-statistics demonstrated incremental value of LA LAS in addition to GLS (P = 0.016) and non-inferiority compared with FT Es (area under the receiver operating characteristic curve 0.74 vs. 0.69, P = 0.256).\n \n \n Conclusion\n Left-atrial LAS provides fast and software-independent approximations of quantitative LA function with similar value for risk prediction compared with dedicated deformation imaging.\n \n \n Clinical trial registration\n ClinicalTrials.gov: NCT00712101 and NCT01612312"],["dc.identifier.doi","10.1093/ehjopen/oeac053"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/116687"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-605"],["dc.relation.eissn","2752-4191"],["dc.title","Left-atrial long-axis shortening allows effective quantification of atrial function and optimized risk prediction following acute myocardial infarction"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","965512"],["dc.bibliographiccitation.journal","Frontiers in Cardiovascular Medicine"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Evertz, Ruben"],["dc.contributor.author","Schulz, Alexander"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Backhaus, Sören J."],["dc.contributor.author","Vollmann, Dirk"],["dc.contributor.author","Kowallick, Johannes T."],["dc.contributor.author","von Haehling, Stephan"],["dc.contributor.author","Hasenfuß, Gerd"],["dc.contributor.author","Schuster, Andreas"],["dc.date.accessioned","2022-10-04T10:21:43Z"],["dc.date.available","2022-10-04T10:21:43Z"],["dc.date.issued","2022"],["dc.description.abstract","Background\r\n The risk of myocarditis after mRNA vaccination against COVID-19 has emerged recently. Current evidence suggests that young male patients are predominantly affected. In the majority of the cases, only mild symptoms were observed. However, little is known about cardiac magnetic resonance (CMR) imaging patterns in mRNA-related myocarditis and their differences when compared to classical viral myocarditis in the acute phase of inflammation.\r\n \r\n \r\n Methods and results\r\n \r\n In total, 10 mRNA vaccination-associated patients with myocarditis were retrospectively enrolled in this study and compared to 10 patients suffering from viral myocarditis, who were matched for age, sex, comorbidities, and laboratory markers. All patients (\r\n n\r\n = 20) were hospitalized and underwent a standardized clinical examination, as well as an echocardiography and a CMR. Both, clinical and imaging findings and, in particular, functional and volumetric CMR assessments, as well as detailed tissue characterization using late gadolinium enhancement and T1 + T2-weighted sequences, were compared between both groups. The median age of the overall cohort was 26 years (group 1: 25.5; group 2: 27.5;\r\n p\r\n = 0.57). All patients described chest pain as the leading reason for their initial presentation. CMR volumetric and functional parameters did not differ significantly between both groups. In all cases, the lateral left ventricular wall showed late gadolinium enhancement without significant differences in terms of the localization or in-depth tissue characterization (late gadolinium enhancement [LGE] enlargement: group 1: 5.4%; group 2: 6.5%;\r\n p\r\n = 0.14; T2 global/maximum value: group 1: 38.9/52 ms; group 2: 37.8/54.5 ms;\r\n p\r\n = 0.79 and\r\n p\r\n = 0.80).\r\n \r\n \r\n \r\n Conclusion\r\n This study yielded the first evidence that COVID-19 mRNA vaccine-associated myocarditis does not show specific CMR patterns during the very acute stage in the most affected patient group of young male patients. The observed imaging markers were closely related to regular viral myocarditis in our cohort. Additionally, we could not find any markers implying adverse outcomes in this relatively little number of patients; however, this has to be confirmed by future studies that will include larger sample sizes."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.doi","10.3389/fcvm.2022.965512"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/114481"],["dc.notes.intern","DOI-Import GROB-600"],["dc.relation.eissn","2297-055X"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Cardiovascular magnetic resonance imaging patterns of acute COVID-19 mRNA vaccine-associated myocarditis in young male patients: A first single-center experience"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","60"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of Cardiovascular Magnetic Resonance"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Metschies, Georg"],["dc.contributor.author","Billing, Marcus"],["dc.contributor.author","Schmidt-Rimpler, Jonas"],["dc.contributor.author","Kowallick, Johannes T."],["dc.contributor.author","Gertz, Roman J."],["dc.contributor.author","Lapinskas, Tomas"],["dc.contributor.author","Pieske-Kraigher, Elisabeth"],["dc.contributor.author","Pieske, Burkert"],["dc.contributor.author","Lotz, Joachim"],["dc.contributor.author","Bigalke, Boris"],["dc.contributor.author","Kutty, Shelby"],["dc.contributor.author","Hasenfuß, Gerd"],["dc.contributor.author","Kelle, Sebastian"],["dc.contributor.author","Schuster, Andreas"],["dc.contributor.author","Backhaus, Sören J."],["dc.date.accessioned","2021-11-25T11:12:48Z"],["dc.date.available","2021-11-25T11:12:48Z"],["dc.date.issued","2021-05-17"],["dc.date.updated","2021-11-19T12:47:36Z"],["dc.description.abstract","Abstract Background Myocardial deformation analyses using cardiovascular magnetic resonance (CMR) feature tracking (CMR-FT) have incremental value in the assessment of cardiac function beyond volumetric analyses. Since guidelines do not recommend specific imaging parameters, we aimed to define optimal spatial and temporal resolutions for CMR cine images to enable reliable post-processing. Methods Intra- and inter-observer reproducibility was assessed in 12 healthy subjects and 9 heart failure (HF) patients. Cine images were acquired with different temporal (20, 30, 40 and 50 frames/cardiac cycle) and spatial resolutions (high in-plane 1.5 × 1.5 mm through-plane 5 mm, standard 1.8 × 1.8 x 8mm and low 3.0 × 3.0 x 10mm). CMR-FT comprised left ventricular (LV) global and segmental longitudinal/circumferential strain (GLS/GCS) and associated systolic strain rates (SR), and right ventricular (RV) GLS. Results Temporal but not spatial resolution did impact absolute strain and SR. Maximum absolute changes between lowest and highest temporal resolution were as follows: 1.8% and 0.3%/s for LV GLS and SR, 2.5% and 0.6%/s for GCS and SR as well as 1.4% for RV GLS. Changes of strain values occurred comparing 20 and 30 frames/cardiac cycle including LV and RV GLS and GCS (p < 0.001–0.046). In contrast, SR values (LV GLS/GCS SR) changed significantly comparing all successive temporal resolutions (p < 0.001–0.013). LV strain and SR reproducibility was not affected by either temporal or spatial resolution, whilst RV strain variability decreased with augmentation of temporal resolution. Conclusion Temporal but not spatial resolution significantly affects strain and SR in CMR-FT deformation analyses. Strain analyses require lower temporal resolution and 30 frames/cardiac cycle offer consistent strain assessments, whilst SR measurements gain from further increases in temporal resolution."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.citation","Journal of Cardiovascular Magnetic Resonance. 2021 May 17;23(1):60"],["dc.identifier.doi","10.1186/s12968-021-00740-5"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/93537"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.publisher","BioMed Central"],["dc.relation.eissn","1532-429X"],["dc.relation.orgunit","Klinik für Kardiologie und Pneumologie"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","Myocardial deformation"],["dc.subject","Strain"],["dc.subject","Cardiovascular magnetic resonance"],["dc.subject","Temporal resolution"],["dc.subject","Spatial resolution"],["dc.subject","Reproducibility"],["dc.title","Defining the optimal temporal and spatial resolution for cardiovascular magnetic resonance imaging feature tracking"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Backhaus, Sören J."],["dc.contributor.author","Aldehayat, Haneen"],["dc.contributor.author","Kowallick, Johannes T."],["dc.contributor.author","Evertz, Ruben"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Kutty, Shelby"],["dc.contributor.author","Bigalke, Boris"],["dc.contributor.author","Gutberlet, Matthias"],["dc.contributor.author","Hasenfuß, Gerd"],["dc.contributor.author","Thiele, Holger"],["dc.contributor.author","Schuster, Andreas"],["dc.date.accessioned","2022-09-01T09:50:07Z"],["dc.date.available","2022-09-01T09:50:07Z"],["dc.date.issued","2022"],["dc.description.abstract","Abstract\n \n Feasibility of automated volume-derived cardiac functional evaluation has successfully been demonstrated using cardiovascular magnetic resonance (CMR) imaging. Notwithstanding, strain assessment has proven incremental value for cardiovascular risk stratification. Since introduction of deformation imaging to clinical practice has been complicated by time-consuming post-processing, we sought to investigate automation respectively. CMR data (n = 1095 patients) from two prospectively recruited acute myocardial infarction (AMI) populations with ST-elevation (STEMI) (AIDA STEMI n = 759) and non-STEMI (TATORT-NSTEMI n = 336) were analysed fully automated and manually on conventional cine sequences. LV function assessment included global longitudinal, circumferential, and radial strains (GLS/GCS/GRS). Agreements were assessed between automated and manual strain assessments. The former were assessed for major adverse cardiac event (MACE) prediction within 12 months following AMI. Manually and automated derived GLS showed the best and excellent agreement with an intraclass correlation coefficient (ICC) of 0.81. Agreement was good for GCS and poor for GRS. Amongst automated analyses, GLS (HR 1.12, 95% CI 1.08–1.16,\n p\n  < 0.001) and GCS (HR 1.07, 95% CI 1.05–1.10,\n p\n  < 0.001) best predicted MACE with similar diagnostic accuracy compared to manual analyses; area under the curve (AUC) for GLS (auto 0.691 vs. manual 0.693,\n p\n  = 0.801) and GCS (auto 0.668 vs. manual 0.686,\n p\n  = 0.425). Amongst automated functional analyses, GLS was the only independent predictor of MACE in multivariate analyses (HR 1.10, 95% CI 1.04–1.15,\n p\n  < 0.001). Considering high agreement of automated GLS and equally high accuracy for risk prediction compared to the reference standard of manual analyses, automation may improve efficiency and aid in clinical routine implementation.\n \n Trial registration: ClinicalTrials.gov, NCT00712101 and NCT01612312."],["dc.description.sponsorship"," Deutsches Zentrum für Herz-Kreislaufforschung http://dx.doi.org/10.13039/100010447"],["dc.description.sponsorship"," Georg-August-Universität Göttingen 501100003385"],["dc.identifier.doi","10.1038/s41598-022-16228-w"],["dc.identifier.pii","16228"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113627"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-597"],["dc.relation.eissn","2045-2322"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Journal of Interventional Cardiology"],["dc.bibliographiccitation.lastpage","9"],["dc.bibliographiccitation.volume","2022"],["dc.contributor.author","Evertz, Ruben"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Backhaus, Sören J."],["dc.contributor.author","Schulz, Alexander"],["dc.contributor.author","Beuthner, Bo Eric"],["dc.contributor.author","Topci, Rodi"],["dc.contributor.author","Toischer, Karl"],["dc.contributor.author","Puls, Miriam"],["dc.contributor.author","Kowallick, Johannes T."],["dc.contributor.author","Hasenfuß, Gerd"],["dc.contributor.editor","Kim, Michael C."],["dc.date.accessioned","2022-06-01T09:39:37Z"],["dc.date.available","2022-06-01T09:39:37Z"],["dc.date.issued","2022"],["dc.description.abstract","Background. Cardiovascular magnetic resonance imaging is considered the reference standard for assessing cardiac morphology and function and has demonstrated prognostic utility in patients undergoing transcatheter aortic valve replacement (TAVR). Novel fully automated analyses may facilitate data analyses but have not yet been compared against conventional manual data acquisition in patients with severe aortic stenosis (AS). Methods. Fully automated and manual biventricular assessments were performed in 139 AS patients scheduled for TAVR using commercially available software (suiteHEART®, Neosoft; QMass®, Medis Medical Imaging Systems). Volumetric assessment included left ventricular (LV) mass, LV/right ventricular (RV) end-diastolic/end-systolic volume, LV/RV stroke volume, and LV/RV ejection fraction (EF). Results of fully automated and manual analyses were compared. Regression analyses and receiver operator characteristics including area under the curve (AUC) calculation for prediction of the primary study endpoint cardiovascular (CV) death were performed. Results. Fully automated and manual assessment of LVEF revealed similar prediction of CV mortality in univariable (manual: hazard ratio (HR) 0.970 (95% CI 0.943–0.997) p = 0.032 ; automated: HR 0.967 (95% CI 0.939–0.995) p = 0.022 ) and multivariable analyses (model 1: (including significant univariable parameters) manual: HR 0.968 (95% CI 0.938–0.999) p = 0.043 ; automated: HR 0.963 [95% CI 0.933–0.995] p = 0.024 ; model 2: (including CV risk factors) manual: HR 0.962 (95% CI 0.920–0.996) p = 0.027 ; automated: HR 0.954 (95% CI 0.920–0.989) p = 0.011 ). There were no differences in AUC (LVEF fully automated: 0.686; manual: 0.661; p = 0.21 ). Absolute values of LV volumes differed significantly between automated and manual approaches ( p < 0.001 for all). Fully automated quantification resulted in a time saving of 10 minutes per patient. Conclusion. Fully automated biventricular volumetric assessments enable efficient and equal risk prediction compared to conventional manual approaches. In addition to significant time saving, this may provide the tools for optimized clinical management and stratification of patients with severe AS undergoing TAVR."],["dc.identifier.doi","10.1155/2022/1368878"],["dc.identifier.pii","1368878"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/108521"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-572"],["dc.relation.eissn","1540-8183"],["dc.relation.issn","0896-4327"],["dc.title","Artificial Intelligence Enabled Fully Automated CMR Function Quantification for Optimized Risk Stratification in Patients Undergoing Transcatheter Aortic Valve Replacement"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","45"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of Cardiovascular Magnetic Resonance"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Backhaus, Sören J."],["dc.contributor.author","Beuthner, Bo E."],["dc.contributor.author","Topci, Rodi"],["dc.contributor.author","Rigorth, Karl-Rudolf"],["dc.contributor.author","Kowallick, Johannes T."],["dc.contributor.author","Evertz, Ruben"],["dc.contributor.author","Schnelle, Moritz"],["dc.contributor.author","Ravassa, Susana"],["dc.contributor.author","Díez, Javier"],["dc.contributor.author","Toischer, Karl"],["dc.contributor.author","Seidler, Tim"],["dc.contributor.author","Puls, Miriam"],["dc.contributor.author","Hasenfuß, Gerd"],["dc.contributor.author","Schuster, Andreas"],["dc.date.accessioned","2022-08-18T12:40:06Z"],["dc.date.available","2022-08-18T12:40:06Z"],["dc.date.issued","2022-07-28"],["dc.date.updated","2022-07-29T12:18:01Z"],["dc.description.abstract","Abstract\n \n Background\n Since cardiovascular magnetic resonance (CMR) imaging allows comprehensive quantification of both myocardial function and structure we aimed to assess myocardial remodeling processes in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR).\n \n \n Methods\n CMR imaging was performed in 40 patients with severe AS before and 1 year after TAVR. Image analyses comprised assessments of myocardial volumes, CMR-feature-tracking based atrial and ventricular strain, myocardial T1 mapping, extracellular volume fraction-based calculation of left ventricular (LV) cellular and matrix volumes, as well as ischemic and non-ischemic late gadolinium enhancement analyses. Moreover, biomarkers including NT-proBNP as well as functional and clinical status were documented.\n \n \n Results\n Myocardial function improved 1 year after TAVR: LV ejection fraction (57.9 ± 16.9% to 65.4 ± 14.5%, p = 0.002); LV global longitudinal (− 21.4 ± 8.0% to -25.0 ± 6.4%, p < 0.001) and circumferential strain (− 36.9 ± 14.3% to − 42.6 ± 11.8%, p = 0.001); left atrial reservoir (13.3 ± 6.3% to 17.8 ± 6.7%, p = 0.001), conduit (5.5 ± 3.2% to 8.4 ± 4.6%, p = 0.001) and boosterpump strain (8.2 ± 4.6% to 9.9 ± 4.2%, p = 0.027). This was paralleled by regression of total myocardial volume (90.3 ± 21.0 ml/m2 to 73.5 ± 17.0 ml/m2, p < 0.001) including cellular (55.2 ± 13.2 ml/m2 to 45.3 ± 11.1 ml/m2, p < 0.001) and matrix volumes (20.7 ± 6.1 ml/m2 to 18.8 ± 5.3 ml/m2, p = 0.036). These changes were paralleled by recovery from heart failure (decrease of NYHA class: p < 0.001; declining NT-proBNP levels: 2456 ± 3002 ng/L to 988 ± 1222 ng/L, p = 0.001).\n \n \n Conclusion\n CMR imaging enables comprehensive detection of myocardial remodeling in patients undergoing TAVR. Regression of LV matrix volume as a surrogate for reversible diffuse myocardial fibrosis is accompanied by increase of myocardial function and recovery from heart failure. Further data are required to define the value of these parameters as therapeutic targets for optimized management of TAVR patients.\n Trial registration DRKS, DRKS00024479. Registered 10 December 2021—Retrospectively registered, \n https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00024479"],["dc.identifier.citation","Journal of Cardiovascular Magnetic Resonance. 2022 Jul 28;24(1):45"],["dc.identifier.doi","10.1186/s12968-022-00874-0"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112977"],["dc.language.iso","en"],["dc.publisher","BioMed Central"],["dc.rights.holder","The Author(s)"],["dc.subject","Cardiac magnetic resonance imaging"],["dc.subject","Transcatheter aortic valve replacement"],["dc.subject","Myocardial remodeling"],["dc.subject","Assessment of myocardial function and structure"],["dc.title","Functional and structural reverse myocardial remodeling following transcatheter aortic valve replacement: a prospective cardiovascular magnetic resonance study"],["dc.type","journal_article"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1563"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","JACC: Cardiovascular Imaging"],["dc.bibliographiccitation.lastpage","1574"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Corral Acero, Jorge"],["dc.contributor.author","Schuster, Andreas"],["dc.contributor.author","Zacur, Ernesto"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Stiermaier, Thomas"],["dc.contributor.author","Backhaus, Sören J."],["dc.contributor.author","Thiele, Holger"],["dc.contributor.author","Bueno-Orovio, Alfonso"],["dc.contributor.author","Lamata, Pablo"],["dc.contributor.author","Eitel, Ingo"],["dc.contributor.author","Grau, Vicente"],["dc.date.accessioned","2022-11-01T10:16:30Z"],["dc.date.available","2022-11-01T10:16:30Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1016/j.jcmg.2021.11.027"],["dc.identifier.pii","S1936878X21008998"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/116579"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-605"],["dc.relation.issn","1936-878X"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Understanding and Improving Risk Assessment After Myocardial Infarction Using Automated Left Ventricular Shape Analysis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","943"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","JACC: Cardiovascular Imaging"],["dc.bibliographiccitation.lastpage","945"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Backhaus, Sören J."],["dc.contributor.author","Rösel, Simon F."],["dc.contributor.author","Schulz, Alexander"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Hellenkamp, Kristian"],["dc.contributor.author","Gertz, Roman J."],["dc.contributor.author","Wachter, Rolf"],["dc.contributor.author","Steinmetz, Michael"],["dc.contributor.author","Kutty, Shelby"],["dc.contributor.author","Raaz, Uwe"],["dc.contributor.author","Schuster, Andreas"],["dc.date.accessioned","2022-07-01T07:35:48Z"],["dc.date.available","2022-07-01T07:35:48Z"],["dc.date.issued","2022"],["dc.description.sponsorship"," http://dx.doi.org/10.13039/100010447 Deutsches Zentrum für Herz-Kreislaufforschung"],["dc.identifier.doi","10.1016/j.jcmg.2021.11.013"],["dc.identifier.pii","S1936878X21008421"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112270"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-581"],["dc.relation.issn","1936-878X"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","RT-CMR Imaging for Noninvasive Characterization of HFpEF"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","46"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of Cardiovascular Magnetic Resonance"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Backhaus, Sören Jan"],["dc.contributor.author","Lange, Torben"],["dc.contributor.author","Beuthner, Bo Eric"],["dc.contributor.author","Topci, Rodi"],["dc.contributor.author","Wang, Xiaoqing"],["dc.contributor.author","Kowallick, Johannes Tammo"],["dc.contributor.author","Lotz, Joachim"],["dc.contributor.author","Seidler, Tim"],["dc.contributor.author","Toischer, Karl"],["dc.contributor.author","Zeisberg, Elisabeth M."],["dc.contributor.author","Puls, Miriam"],["dc.contributor.author","Jacobshagen, Claudius"],["dc.contributor.author","Uecker, Martin"],["dc.contributor.author","Hasenfuß, Gerd P."],["dc.contributor.author","Schuster, Andreas"],["dc.date.accessioned","2021-03-08T07:13:57Z"],["dc.date.available","2021-03-08T07:13:57Z"],["dc.date.issued","2020"],["dc.description.abstract","Myocardial fibrosis is a major determinant of outcome in aortic stenosis (AS). Novel fast real-time (RT) cardiovascular magnetic resonance (CMR) mapping techniques allow comprehensive quantification of fibrosis but have not yet been compared against standard techniques and histology."],["dc.identifier.doi","10.1186/s12968-020-00632-0"],["dc.identifier.pmid","32564773"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17418"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/80477"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/50"],["dc.identifier.url","https://sfb1002.med.uni-goettingen.de/production/literature/publications/359"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation","SFB 1002: Modulatorische Einheiten bei Herzinsuffizienz"],["dc.relation","SFB 1002 | D01: Erholung aus der Herzinsuffizienz – Einfluss von Fibrose und Transkriptionssignatur"],["dc.relation.issn","1532-429X"],["dc.relation.workinggroup","RG Hasenfuß"],["dc.relation.workinggroup","RG Uecker"],["dc.relation.workinggroup","RG E. Zeisberg (Kardiales Stroma)"],["dc.relation.workinggroup","RG Backhaus"],["dc.relation.workinggroup","RG Toischer (Kardiales Remodeling)"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Real-time cardiovascular magnetic resonance T1 and extracellular volume fraction mapping for tissue characterisation in aortic stenosis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

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