Simone Di Gregorio1, Christian Vergara2, Giovanni Montino Pelagi1, Andrea Baggiano3,4, Paolo Zunino1, Marco Guglielmo3, Laura Fusini3,5, Giuseppe Muscogiuri3, Alexia Rossi6,7, Mark G Rabbat8,9, Alfio Quarteroni1,10, Gianluca Pontone11. 1. Dipartimento Di Matematica, MOX, Politecnico Di Milano, Milan, Italy. 2. LABS, Dipartimento Di Chimica, Materiali E Ingegneria Chimica, Politecnico Di Milano, Milan, Italy. 3. Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy. 4. Department of Clinical Science and Community Health, University of Milan, Milan, Italy. 5. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy. 6. Department of Nuclear Medicine, University Hospital, Zurich, Switzerland. 7. Center for Molecular Cardiology, University of Zurich, Zurich, Switzerland. 8. Loyola University of Chicago, Chicago, IL, USA. 9. Edward Hines Jr. VA Hospital, Hines, IL, USA. 10. Institute of Mathematics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. 11. Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCSS, Via C. Parea 4, 20138, Milan, Italy. gianluca.pontone@ccfm.it.
Abstract
PURPOSE: Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps. METHODS: A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBFCTP) maps. The relative error between the computational MBF under stress conditions (MBFCOMP) and MBFCTP was evaluated to assess the accuracy of the proposed computational model. RESULTS: Applying our method to 9 patients (4 control subjects without ischemia vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBFCOMP and MBFCTP. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%. CONCLUSION: The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e., the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.
PURPOSE: Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps. METHODS: A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBFCTP) maps. The relative error between the computational MBF under stress conditions (MBFCOMP) and MBFCTP was evaluated to assess the accuracy of the proposed computational model. RESULTS: Applying our method to 9 patients (4 control subjects without ischemia vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBFCOMP and MBFCTP. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%. CONCLUSION: The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e., the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.
Authors: Luca Antiga; Marina Piccinelli; Lorenzo Botti; Bogdan Ene-Iordache; Andrea Remuzzi; David A Steinman Journal: Med Biol Eng Comput Date: 2008-11-11 Impact factor: 2.602
Authors: C Michler; A N Cookson; R Chabiniok; E Hyde; J Lee; M Sinclair; T Sochi; A Goyal; G Vigueras; D A Nordsletten; N P Smith Journal: Int J Numer Method Biomed Eng Date: 2012-10-18 Impact factor: 2.747
Authors: Gianluca Pontone; Andrea Baggiano; Daniele Andreini; Andrea I Guaricci; Marco Guglielmo; Giuseppe Muscogiuri; Laura Fusini; Margherita Soldi; Alberico Del Torto; Saima Mushtaq; Edoardo Conte; Giuseppe Calligaris; Stefano De Martini; Cristina Ferrari; Stefano Galli; Luca Grancini; Paolo Olivares; Paolo Ravagnani; Giovanni Teruzzi; Daniela Trabattoni; Franco Fabbiocchi; Piero Montorsi; Mark G Rabbat; Antonio L Bartorelli; Mauro Pepi Journal: Int J Cardiol Date: 2018-09-20 Impact factor: 4.164
Authors: Gianluca Pontone; Andrea Baggiano; Daniele Andreini; Andrea I Guaricci; Marco Guglielmo; Giuseppe Muscogiuri; Laura Fusini; Margherita Soldi; Alberico Del Torto; Saima Mushtaq; Edoardo Conte; Giuseppe Calligaris; Stefano De Martini; Cristina Ferrari; Stefano Galli; Luca Grancini; Paolo Olivares; Paolo Ravagnani; Giovanni Teruzzi; Daniela Trabattoni; Franco Fabbiocchi; Piero Montorsi; Mark G Rabbat; Antonio L Bartorelli; Mauro Pepi Journal: JACC Cardiovasc Imaging Date: 2019-04-17
Authors: Gianluca Pontone; Daniele Andreini; Andrea I Guaricci; Andrea Baggiano; Fabio Fazzari; Marco Guglielmo; Giuseppe Muscogiuri; Claudio Maria Berzovini; Annalisa Pasquini; Saima Mushtaq; Edoardo Conte; Giuseppe Calligaris; Stefano De Martini; Cristina Ferrari; Stefano Galli; Luca Grancini; Paolo Ravagnani; Giovanni Teruzzi; Daniela Trabattoni; Franco Fabbiocchi; Alessandro Lualdi; Piero Montorsi; Mark G Rabbat; Antonio L Bartorelli; Mauro Pepi Journal: JACC Cardiovasc Imaging Date: 2018-02-14
Authors: Gianluca Pontone; Andrea Baggiano; Daniele Andreini; Andrea I Guaricci; Marco Guglielmo; Giuseppe Muscogiuri; Laura Fusini; Fabio Fazzari; Saima Mushtaq; Edoardo Conte; Giuseppe Calligaris; Stefano De Martini; Cristina Ferrari; Stefano Galli; Luca Grancini; Paolo Ravagnani; Giovanni Teruzzi; Daniela Trabattoni; Franco Fabbiocchi; Alessandro Lualdi; Piero Montorsi; Mark G Rabbat; Antonio L Bartorelli; Mauro Pepi Journal: JACC Cardiovasc Imaging Date: 2018-10-17
Authors: Lazaros Papamanolis; Hyun Jin Kim; Clara Jaquet; Matthew Sinclair; Michiel Schaap; Ibrahim Danad; Pepijn van Diemen; Paul Knaapen; Laurent Najman; Hugues Talbot; Charles A Taylor; Irene Vignon-Clementel Journal: Ann Biomed Eng Date: 2020-12-01 Impact factor: 3.934