Andreas Schuster1,2, Niloufar Zarinabad3, Masaki Ishida4, Matthew Sinclair5, Jeroen Phm van den Wijngaard6, Geraint Morton7, Gilion Ltf Hautvast8, Boris Bigalke9,10, Pepijn van Horssen11, Nicolas Smith12, Jos Ae Spaan13, Maria Siebes14, Amedeo Chiribiri15, Eike Nagel16. 1. Division of Imaging Sciences and Biomedical Engineering; King's College London British Heart Foundation (BHF) Centre of Excellence; National Institute of Health Research (NIHR) Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust; Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC) Medical Engineering Centre, The Rayne Institute, St. Thomas´ Hospital, London, UK. andreas_schuster@gmx.net. 2. Department of Cardiology and Pneumology and German Centre for Cardiovascular Research (DZHK, Partner Site Göttingen), Georg-August-University, Göttingen, Germany. andreas_schuster@gmx.net. 3. Division of Imaging Sciences and Biomedical Engineering; King's College London British Heart Foundation (BHF) Centre of Excellence; National Institute of Health Research (NIHR) Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust; Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC) Medical Engineering Centre, The Rayne Institute, St. Thomas´ Hospital, London, UK. niloufar.zarinabad@kcl.ac.uk. 4. Division of Imaging Sciences and Biomedical Engineering; King's College London British Heart Foundation (BHF) Centre of Excellence; National Institute of Health Research (NIHR) Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust; Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC) Medical Engineering Centre, The Rayne Institute, St. Thomas´ Hospital, London, UK. ishidamasaki1@gmail.com. 5. Division of Imaging Sciences and Biomedical Engineering; King's College London British Heart Foundation (BHF) Centre of Excellence; National Institute of Health Research (NIHR) Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust; Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC) Medical Engineering Centre, The Rayne Institute, St. Thomas´ Hospital, London, UK. matthew.sinclair@kcl.ac.uk. 6. Department of Biomedical Engineering & Physics, Academic Medical Centre, Amsterdam, The Netherlands. J.P.vandenWijngaard@amc.uva.nl. 7. Division of Imaging Sciences and Biomedical Engineering; King's College London British Heart Foundation (BHF) Centre of Excellence; National Institute of Health Research (NIHR) Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust; Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC) Medical Engineering Centre, The Rayne Institute, St. Thomas´ Hospital, London, UK. geraintmorton@gmail.com. 8. Philips Healthcare, Imaging Systems - MR, Best, The Netherlands. gilion.hautvast@philips.com. 9. Division of Imaging Sciences and Biomedical Engineering; King's College London British Heart Foundation (BHF) Centre of Excellence; National Institute of Health Research (NIHR) Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust; Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC) Medical Engineering Centre, The Rayne Institute, St. Thomas´ Hospital, London, UK. dr.bigalke@gmx.de. 10. Medizinische Klinik III, Kardiologie und Kreislauferkrankungen, Eberhard-Karls-Universität Tübingen, Tübingen, Germany. dr.bigalke@gmx.de. 11. Department of Biomedical Engineering & Physics, Academic Medical Centre, Amsterdam, The Netherlands. phorssen@gmail.com. 12. Division of Imaging Sciences and Biomedical Engineering; King's College London British Heart Foundation (BHF) Centre of Excellence; National Institute of Health Research (NIHR) Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust; Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC) Medical Engineering Centre, The Rayne Institute, St. Thomas´ Hospital, London, UK. nicolas.smith@kcl.ac.uk. 13. Department of Biomedical Engineering & Physics, Academic Medical Centre, Amsterdam, The Netherlands. j.a.spaan@amc.uva.nl. 14. Department of Biomedical Engineering & Physics, Academic Medical Centre, Amsterdam, The Netherlands. m.siebes@amc.uva.nl. 15. Division of Imaging Sciences and Biomedical Engineering; King's College London British Heart Foundation (BHF) Centre of Excellence; National Institute of Health Research (NIHR) Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust; Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC) Medical Engineering Centre, The Rayne Institute, St. Thomas´ Hospital, London, UK. amedeo.chiribiri@kcl.ac.uk. 16. Division of Imaging Sciences and Biomedical Engineering; King's College London British Heart Foundation (BHF) Centre of Excellence; National Institute of Health Research (NIHR) Biomedical Research Centre at Guy's and St. Thomas' NHS Foundation Trust; Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC) Medical Engineering Centre, The Rayne Institute, St. Thomas´ Hospital, London, UK. eike.nagel@kcl.ac.uk.
Abstract
BACKGROUND: Cardiovascular Magnetic Resonance (CMR) myocardial perfusion imaging has the potential to evolve into a method allowing full quantification of myocardial blood flow (MBF) in clinical routine. Multiple quantification pathways have been proposed. However at present it remains unclear which algorithm is the most accurate. An isolated perfused, magnetic resonance (MR) compatible pig heart model allows very accurate titration of MBF and in combination with high-resolution assessment of fluorescently-labeled microspheres represents a near optimal platform for validation. We sought to investigate which algorithm is most suited to quantify myocardial perfusion by CMR at 1.5 and 3 Tesla using state of the art CMR perfusion techniques and quantification algorithms. METHODS: First-pass perfusion CMR was performed in an MR compatible blood perfused pig heart model. We acquired perfusion images at physiological flow ("rest"), reduced flow ("ischaemia") and during adenosine-induced hyperaemia ("hyperaemia") as well as during coronary occlusion. Perfusion CMR was performed at 1.5 Tesla (n = 4 animals) and at 3 Tesla (n = 4 animals). Fluorescently-labeled microspheres and externally controlled coronary blood flow served as reference standards for comparison of different quantification strategies, namely Fermi function deconvolution (Fermi), autoregressive moving average modelling (ARMA), exponential basis deconvolution (Exponential) and B-spline basis deconvolution (B-spline). RESULTS: All CMR derived MBF estimates significantly correlated with microsphere results. The best correlation was achieved with Fermi function deconvolution both at 1.5 Tesla (r = 0.93, p < 0.001) and at 3 Tesla (r = 0.9, p < 0.001). Fermi correlated significantly better with the microspheres than all other methods at 3 Tesla (p < 0.002). B-spline performed worse than Fermi and Exponential at 1.5 Tesla and showed the weakest correlation to microspheres (r = 0.74, p < 0.001). All other comparisons were not significant. At 3 Tesla exponential deconvolution performed worst (r = 0.49, p < 0.001). CONCLUSIONS: CMR derived quantitative blood flow estimates correlate with true myocardial blood flow in a controlled animal model. Amongst the different techniques, Fermi function deconvolution was the most accurate technique at both field strengths. Perfusion CMR based on Fermi function deconvolution may therefore emerge as a useful clinical tool providing accurate quantitative blood flow assessment.
BACKGROUND: Cardiovascular Magnetic Resonance (CMR) myocardial perfusion imaging has the potential to evolve into a method allowing full quantification of myocardial blood flow (MBF) in clinical routine. Multiple quantification pathways have been proposed. However at present it remains unclear which algorithm is the most accurate. An isolated perfused, magnetic resonance (MR) compatible pig heart model allows very accurate titration of MBF and in combination with high-resolution assessment of fluorescently-labeled microspheres represents a near optimal platform for validation. We sought to investigate which algorithm is most suited to quantify myocardial perfusion by CMR at 1.5 and 3 Tesla using state of the art CMR perfusion techniques and quantification algorithms. METHODS: First-pass perfusion CMR was performed in an MR compatible blood perfused pig heart model. We acquired perfusion images at physiological flow ("rest"), reduced flow ("ischaemia") and during adenosine-induced hyperaemia ("hyperaemia") as well as during coronary occlusion. Perfusion CMR was performed at 1.5 Tesla (n = 4 animals) and at 3 Tesla (n = 4 animals). Fluorescently-labeled microspheres and externally controlled coronary blood flow served as reference standards for comparison of different quantification strategies, namely Fermi function deconvolution (Fermi), autoregressive moving average modelling (ARMA), exponential basis deconvolution (Exponential) and B-spline basis deconvolution (B-spline). RESULTS: All CMR derived MBF estimates significantly correlated with microsphere results. The best correlation was achieved with Fermi function deconvolution both at 1.5 Tesla (r = 0.93, p < 0.001) and at 3 Tesla (r = 0.9, p < 0.001). Fermi correlated significantly better with the microspheres than all other methods at 3 Tesla (p < 0.002). B-spline performed worse than Fermi and Exponential at 1.5 Tesla and showed the weakest correlation to microspheres (r = 0.74, p < 0.001). All other comparisons were not significant. At 3 Tesla exponential deconvolution performed worst (r = 0.49, p < 0.001). CONCLUSIONS: CMR derived quantitative blood flow estimates correlate with true myocardial blood flow in a controlled animal model. Amongst the different techniques, Fermi function deconvolution was the most accurate technique at both field strengths. Perfusion CMR based on Fermi function deconvolution may therefore emerge as a useful clinical tool providing accurate quantitative blood flow assessment.
Authors: Marco A Costa; Steven Shoemaker; Hideki Futamatsu; Chris Klassen; Dominick J Angiolillo; Minh Nguyen; Alan Siuciak; Paul Gilmore; Martin M Zenni; Luis Guzman; Theodore A Bass; Norbert Wilke Journal: J Am Coll Cardiol Date: 2007-07-23 Impact factor: 24.094
Authors: Achim A Barmeyer; Alexander Stork; Kai Muellerleile; Claudia Tiburtius; Anne K Schofer; Thomas A Heitzer; Thomas Hofmann; Gerhard Adam; Thomas Meinertz; Gunnar K Lund Journal: Radiology Date: 2007-05 Impact factor: 11.105
Authors: Manish Motwani; Neil Maredia; Timothy A Fairbairn; Sebastian Kozerke; Aleksandra Radjenovic; John P Greenwood; Sven Plein Journal: Circ Cardiovasc Imaging Date: 2012-04-12 Impact factor: 7.792
Authors: Amedeo Chiribiri; Gilion L T F Hautvast; Timothy Lockie; Andreas Schuster; Boris Bigalke; Luca Olivotti; Simon R Redwood; Marcel Breeuwer; Sven Plein; Eike Nagel Journal: JACC Cardiovasc Imaging Date: 2013-04-10
Authors: Geraint Morton; Amedeo Chiribiri; Masaki Ishida; Shazia T Hussain; Andreas Schuster; Andreas Indermuehle; Divaka Perera; Juhani Knuuti; Stacey Baker; Erik Hedström; Paul Schleyer; Michael O'Doherty; Sally Barrington; Eike Nagel Journal: J Am Coll Cardiol Date: 2012-09-19 Impact factor: 24.094
Authors: Andreas Schuster; Matthew Sinclair; Niloufar Zarinabad; Masaki Ishida; Jeroen P H M van den Wijngaard; Matthias Paul; Pepijn van Horssen; Shazia T Hussain; Divaka Perera; Tobias Schaeffter; Jos A E Spaan; Maria Siebes; Eike Nagel; Amedeo Chiribiri Journal: Eur Heart J Cardiovasc Imaging Date: 2015-03-25 Impact factor: 6.875
Authors: Matthew Sinclair; Jack Lee; Andreas Schuster; Amedeo Chiribiri; Jeroen van den Wijngaard; Pepijn van Horssen; Maria Siebes; Jos A E Spaan; Eike Nagel; Nicolas P Smith Journal: Microvasc Res Date: 2015-05-09 Impact factor: 3.514
Authors: Christopher L Schumann; Roshin C Mathew; John-Henry L Dean; Yang Yang; Pelbreton C Balfour; Peter W Shaw; Austin A Robinson; Michael Salerno; Christopher M Kramer; Jamieson M Bourque Journal: JACC Cardiovasc Imaging Date: 2021-04-14
Authors: D J Pennell; A J Baksi; S K Prasad; C E Raphael; P J Kilner; R H Mohiaddin; F Alpendurada; S V Babu-Narayan; J Schneider; D N Firmin Journal: J Cardiovasc Magn Reson Date: 2015-11-20 Impact factor: 5.364
Authors: D J Pennell; A J Baksi; S K Prasad; R H Mohiaddin; F Alpendurada; S V Babu-Narayan; J E Schneider; D N Firmin Journal: J Cardiovasc Magn Reson Date: 2016-11-15 Impact factor: 5.364