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The quantification of myocardial perfusion MRI has the potential to provide a fast, automated and user-independent assessment of myocardial ischaemia.
Hierarchical bayesian myocardial perfusion quantification
Cian M. Scannell, Amedeo Chiribiri, Adriana D. M. Villa, Marcel Breeuwer, and Jack Lee · 1906
Earlier work this paper cites.
A temporal deep learning approach for MR perfusion parameter estimation in stroke
King Chung Ho, Fabien Scalzo, Karthik V. Sarma, Suzie El-Saden, and Corey W. Arnold · 2016
Earlier work this paper cites.
A comparison of Bayesian and non-linear regression methods for robust estimation of pharmacokinetics in DCE-MRI and how it affects cancer diagnosis
Nikolaos Dikaios, David Atkinson, Chiara Tudisca, Pierpaolo Purpura, Martin Forster, Hashim Ahmed, Timothy Beale, Mark Emberton, and Shonit Punwani · 2017
Cited alongside, same era.
Prognostic Value of Quantitative Stress Perfusion Cardiac Magnetic Resonance
Eva C. Sammut, Adriana D.M. Villa, Gabriella Di Giovine, Luke Dancy, Filippo Bosio, Thomas Gibbs, Swarna Jeyabraba, Susanne Schwenke, Steven E. Williams, Michael Marber, Khaled Alfakih, Tevfik F. Ismail, Reza Razavi, and Amedeo Chiribiri · 2017
Cited alongside, same era.
Robust non-rigid motion compensation of free-breathing myocardial perfusion MRI data
Cian M. Scannell, Adriana D. M. Villa, Jack Lee, Marcel Breeuwer, and Amedeo Chiribiri · 2019
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