Meta menu:

From here, you can access the Emergencies page, Contact Us page, Accessibility Settings, Language Selection, and Search page.

Open Menu

B06 - Imaging structure/function relations in HFpEF – from pathomechanisms to artificial intelligence-based classification

You are here:

Project summary

Cardiovascular MRI (CMR) is the gold standard for quantitative assessment of anatomy, function, and hemodynamics, as well as myocardial tissue changes such as fibrosis, inflammatory response, and fatty infiltration.  B06 investigates the hypothesis that innovative CMR imaging combined with artificial intelligence will enable improved differentiation of prevalent HFpEF pathologies. To this end, a CMR-based Heart Failure Remodeling Index (CMR-HRI) will be developed and pathomechanisms of HFpEF phenotypes will be analyzed.


Graphical Abstract: To improve patient stratification we will use a multimodal imaging approach to reflect cardiac function, structure, and systems level input (exercise, extracardiac triggers). Using AI feature extraction, we derive a Heart failure remodeling index to facilitate the integration with clinical data from Z02 (Pieske, Edelmann, Rauch, Tschöpe) and molecular data from B05 (Pieske, Mertins) in Z03 (Eils, Kuehne). On the mechanistic level, we investigate  the crosstalk of HFpEF and exercise at the level of tissue hypoxia and microvascular dysfunction.


Univ.-Professor Dr.-Ing Anja Hennemuth

Project leader B06

CVK: Campus Virchow-Klinikum

Univ.-Professor Dr. med. Sebastian Kelle

Project leader B06

CVK: Campus Virchow-Klinikum

Additional group members

Postdoc: Lars Walczak






Doeblin P, Steinbeis F, Scannell CM, Goetze C, Al-Tabatabaee S, Erley J, Faragli A, Pröpper F, Witzenrath M, Zoller T, Stehning C, Gerhardt H, Sánchez-González J, Alskaf E, Kühne T, Pieske B, Tschöpe C, Chiribiri A, Kelle S. Brief Research Report: Quantitative Analysis of Potential Coronary Microvascular Disease in Suspected Long-COVID Syndrome. Front Cardiovasc Med. 2022 May 31;9:877416. doi: 10.3389/fcvm.2022.877416. PMID: 35711381; PMCID: PMC9197432.

Bingel A, Messroghli D, Weimar A, Runte K, Salcher-Konrad M, Kelle S, Pieske B, Berger F, Kuehne T, Goubergrits L, Fuerstenau D, Kelm M. Hemodynamic Changes During Physiological and Pharmacological Stress Testing in Patients With Heart Failure: A Systematic Review and Meta-Analysis. Front Cardiovasc Med. 2022 Apr 19;9:718114. doi: 10.3389/fcvm.2022.718114. PMID: 35514442; PMCID: PMC9062977.

Minhas AS, Goerlich E, Corretti MC, Arbab-Zadeh A, Kelle S, Leucker T, Lerman A, Hays AG. Imaging Assessment of Endothelial Function: An Index of Cardiovascular Health. Front Cardiovasc Med. 2022 Apr 15;9:778762. doi: 10.3389/fcvm.2022.778762. PMID: 35498006; PMCID: PMC9051238.

Chen W, Doeblin P, Al-Tabatabaee S, Klingel K, Tanacli R, Jakob Weiß K, Stehning C, Patel AR, Pieske B, Zou J, Kelle S. Synthetic Extracellular Volume in Cardiac Magnetic Resonance Without Blood Sampling: a Reliable Tool to Replace Conventional Extracellular Volume. Circ Cardiovasc Imaging. 2022 Apr;15(4):e013745. doi: 10.1161/CIRCIMAGING.121.013745. Epub 2022 Apr 1. PMID: 35360924; PMCID: PMC9015035.

Huellebrand M, Ivantsits M, Tautz L, Kelle S, Hennemuth A. A Collaborative Approach for the Development and Application of Machine Learning Solutions for CMR-Based Cardiac Disease Classification. Front Cardiovasc Med. 2022 Mar 10;9:829512. doi: 10.3389/fcvm.2022.829512. PMID: 35360025; PMCID: PMC8960112.