nach oben

European Journal of Nuclear Medicine and Molecular Imaging

Erschienen in:

05.01.2022 | Original Article

Prediction of myocardial blood flow under stress conditions by means of a computational model

verfasst von: Simone Di Gregorio, Christian Vergara, Giovanni Montino Pelagi, Andrea Baggiano, Paolo Zunino, Marco Guglielmo, Laura Fusini, Giuseppe Muscogiuri, Alexia Rossi, Mark G. Rabbat, Alfio Quarteroni, Gianluca Pontone

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 6/2022

Einloggen, um Zugang zu erhalten

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 (MBF_CTP) maps. The relative error between the computational MBF under stress conditions (MBF_COMP) and MBF_CTP 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 MBF_COMP and MBF_CTP. 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.

Pontone G, Andreini D, Guaricci AI, Baggiano A, Fazzari F, Guglielmo M, et al. Incremental diagnostic value of stress computed tomography myocardial perfusion with whole-heart coverage CT scanner in intermediate- to high-risk symptomatic patients suspected of coronary artery disease. JACC Cardiovasc Imaging. 2019;12:338–49. https://doi.org/10.1016/j.jcmg.2017.10.025.CrossRefPubMed

Pontone G, Andreini D, Guaricci AI, Guglielmo M, Baggiano A, Muscogiuri G, et al. Quantitative vs. qualitative evaluation of static stress computed tomography perfusion to detect haemodynamically significant coronary artery disease. Eur Heart J Cardiovasc Imaging 2018;19:1244–52. doi: https://doi.org/10.1093/ehjci/jey111

Pontone G, Baggiano A, Andreini D, Guaricci AI, Guglielmo M, Muscogiuri G, et al. Diagnostic accuracy of simultaneous evaluation of coronary arteries and myocardial perfusion with single stress cardiac computed tomography acquisition compared to invasive coronary angiography plus invasive fractional flow reserve. Int J Cardiol. 2018;273:263–8. https://doi.org/10.1016/j.ijcard.2018.09.065.CrossRefPubMed

Pontone G, Baggiano A, Andreini D, Guaricci AI, Guglielmo M, Muscogiuri G, et al. Stress computed tomography perfusion versus fractional flow reserve CT derived in suspected coronary artery disease: The PERFECTION Study. JACC Cardiovasc Imaging. 2019;12:1487–97. https://doi.org/10.1016/j.jcmg.2018.08.023.CrossRefPubMed

Pontone G, Baggiano A, Andreini D, Guaricci AI, Guglielmo M, Muscogiuri G, et al. Dynamic stress computed tomography perfusion with a whole-heart coverage scanner in addition to coronary computed tomography angiography and fractional flow reserve computed tomography derived. JACC Cardiovasc Imaging. 2019;12:2460–71. https://doi.org/10.1016/j.jcmg.2019.02.015.CrossRefPubMed

Baggiano A, Fusini L, Del Torto A, Vivona P, Guglielmo M, Muscogiuri G, et al. Sequential strategy including FFRCT plus stress-CTP impacts on management of patients with stable chest pain: the Stress-CTP RIPCORD Study. J Clin Med. 2020;9:2147. https://doi.org/10.3390/jcm9072147.CrossRefPubMedCentral

Alves JR, de Queiroz RAB, Bär M, dos Santos RW. Simulation of the perfusion of contrast agent used in cardiac magnetic resonance: a step toward non-invasive cardiac perfusion quantification. Front Physiol. 2019;10:177. https://doi.org/10.3389/fphys.2019.00177.CrossRefPubMedPubMedCentral

Chapelle D, Gerbeau J-F, Sainte-Marie J, Vignon-Clementel IE. A poroelastic model valid in large strains with applications to perfusion in cardiac modeling. Comput Mech. 2009;46(1):91–101. https://doi.org/10.1007/s00466-009-0452-x.CrossRef

Di Gregorio S, Fedele M, Pontone G, Corno AF, Zunino P, Vergara C, et al. A computational model applied to myocardial perfusion in the human heart: from large coronaries to microvasculature. J Comput Phys. 2021;424:109836. https://doi.org/10.1016/j.jcp.2020.109836.CrossRef

10.

Lee J, Cookson A, Chabiniok R, Rivolo S, Hyde E, Sinclair M, et al. Multiscale modelling of cardiac perfusion. In: Quarteroni A (eds) Modeling the heart and the circulatory system. MS&A, 2015;14:51–96.

11.

Namani R, Lee LC, Lanir Y, Kaimovitz B, Shavik SM, Kassab GS. Effects of myocardial function and systemic circulation on regional coronary perfusion. J Appl Physiol. 2020;128:1106–22. https://doi.org/10.1152/japplphysiol.00450.2019.CrossRefPubMedPubMedCentral

12.

Papamanolis L, Kim HJ, Jaquet C, Sinclair M, Schaap M, Danad I, et al. Myocardial perfusion simulation for coronary artery disease: a coupled patient-specific multiscale model. Ann Biomed Eng. 2021;49:1432–47. https://doi.org/10.1007/s10439-020-02681-z.CrossRefPubMed

13.

So A, Hsieh J, Li YY, Hadway J, Kong HF, Lee TY. Quantitative myocardial perfusion measurement using CT perfusion: a validation study in a porcine model of reperfused acute myocardial infarction. Int J Cardiovasc Imaging. 2012;28:1237–48. https://doi.org/10.1007/s10554-011-9927-x.CrossRefPubMed

14.

Antiga L, Piccinelli M, Botti L, Ene-Iordache B, Remuzzi A, Steinman DA. An image-based modeling framework for patient-specific computational hemodynamics. Med Biol Eng Comput. 2008;46:1097–112. https://doi.org/10.1007/s11517-008-0420-1.CrossRefPubMed

15.

Fedele M. and Quarteroni A. Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function. Int J Numer Meth Biomed Engng 2021;e3435. doi: https://doi.org/10.1002/cnm.3435

16.

Michler C, Cookson AN, Chabiniok R, Hyde E, Lee J, Sinclair M, et al. A computationally efficient framework for the simulation of cardiac perfusion using a multi-compartment Darcy porous-media flow model. Int J Numer Method Biomed Eng. 2013;29:217–32. https://doi.org/10.1002/cnm.2520.CrossRefPubMed

17.

Hyde ER, Cookson AN, Lee J, Michler C, Goyal A, Sochi T, et al. Multi-scale parameterisation of a myocardial perfusion model using whole-organ arterial networks. Ann Biomed Eng. 2014;42:797–811. https://doi.org/10.1007/s10439-013-0951-y.CrossRefPubMed

18.

Ponzini R, Vergara C, Redaelli A, Veneziani A. Reliable CFD-based estimation of flow rate in haemodynamics measures. Ultrasound Med Biol. 2006;32:1545–55. https://doi.org/10.1016/j.ultrasmedbio.2006.05.022.CrossRefPubMed

19.

Tezduyar T, Sathe S. Stabilization parameters in SUPG and PSPG formulations. J Comput Appl Mech. 2003;4:71–88.

20.

Arndt D, Bangerth W, Clevenger TC, Davydov V, Fehling M, Garcia-Sanchez D, et al. The deal.II library, Version 9.1. J Numer Math 2019;27:203–13. doi: https://doi.org/10.1515/jnma-2019-0064

21.

Zhang C, A Rogers P, Merkus D, Muller-Delp JM, Tiefenbacher CP, Potter B, et al. Regulation of coronary microvascular resistance in health and disease. In: Tuma RF, Durán WN, Ley K (eds) Microcirculation 2008;521–49.

22.

Kaimovitz B, Lanir Y, Kassab GS. Large-scale 3-D geometric reconstruction of the porcine coronary arterial vasculature based on detailed anatomical data. Ann Biomed Eng. 2005;33:1517–35. https://doi.org/10.1007/s10439-005-7544-3.CrossRefPubMed

23.

SCOT-HEART Investigators, Newby DE, Adamson PD, Berry C, Boon NA, Dweck MR, et al. Coronary CT angiography and 5-year risk of myocardial infarction. N Engl J Med 2018;379:924–33. doi: https://doi.org/10.1056/NEJMoa1805971

24.

Timmis A, Roobottom CA. National Institute for Health and Care Excellence updates the stable chest pain guideline with radical changes to the diagnostic paradigm. Heart. 2017;103:982–6. https://doi.org/10.1136/heartjnl-2015-308341.CrossRefPubMed

25.

Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020;41:407–77.CrossRefPubMed

26.

Pontone G, Bertella E, Mushtaq S, Loguercio M, Cortinovis S, Baggiano A, et al. Coronary artery disease: diagnostic accuracy of CT coronary angiography- - a comparison of high and standard spatial resolution scanning. Radiology. 2014;271:688–94. https://doi.org/10.1148/radiol.13130909.CrossRefPubMed

27.

Nørgaard BL, Leipsic J, Gaur S, Seneviratne S, Ko BS, Ito H, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease. J Am Coll Cardiol. 2014;63:1145–55. https://doi.org/10.1016/j.jacc.2013.11.043.CrossRefPubMed

28.

Hlatky MA, De Bruyne B, Pontone G, Patel M, Norgaard BL, Byrne RA, et al. Quality-of-life and economic outcomes of assessing fractional flow reserve with computed tomography angiography: PLATFORM. J Am Coll Cardiol. 2015;66:2315–23. https://doi.org/10.1016/j.jacc.2015.09.051.CrossRefPubMed

29.

Fairbairn TA, Nieman K, Akasaka T, Norgaard BL, Berman DS, Raff G, et al. Real-world clinical utility and impact on clinical decision-making of coronary computed tomography angiography-derived fractional flow reserve: lessons from the ADVANCE Registry. Eur Heart J. 2018;39:3701–11. https://doi.org/10.1093/eurheartj/ehy530.CrossRefPubMedPubMedCentral

Titel: Prediction of myocardial blood flow under stress conditions by means of a computational model
verfasst von: Simone Di Gregorio
Christian Vergara
Giovanni Montino Pelagi
Andrea Baggiano
Paolo Zunino
Marco Guglielmo
Laura Fusini
Giuseppe Muscogiuri
Alexia Rossi
Mark G. Rabbat
Alfio Quarteroni
Gianluca Pontone
Publikationsdatum: 05.01.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 6/2022
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-021-05667-8

13 N-ammonia PET-derived right ventricular longitudinal strain and myocardial flow reserve in right coronary artery disease

Original Article

First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients

Original Article

Targeted Hsp70 fluorescence molecular endoscopy detects dysplasia in Barrett’s esophagus

Open Access
COVID-19
Original Article

Within-patient comparison between [68Ga]Ga-tilmanocept PET/CT lymphoscintigraphy and [99mTc]Tc-tilmanocept lymphoscintigraphy for sentinel lymph node detection in oral cancer: a pilot study

Original Article

Correction to: Imaging of neuroinflammation due to repetitive head injury in currently active kickboxers

Correction

Correction to: EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3

Open Access
Correction

Springer Medizin

Prediction of myocardial blood flow under stress conditions by means of a computational model

Abstract

Purpose

Methods

Results

Conclusion

Weitere Artikel der Ausgabe 6/2022

13 N-ammonia PET-derived right ventricular longitudinal strain and myocardial flow reserve in right coronary artery disease

First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients

Targeted Hsp70 fluorescence molecular endoscopy detects dysplasia in Barrett’s esophagus

Within-patient comparison between [68Ga]Ga-tilmanocept PET/CT lymphoscintigraphy and [99mTc]Tc-tilmanocept lymphoscintigraphy for sentinel lymph node detection in oral cancer: a pilot study

Correction to: Imaging of neuroinflammation due to repetitive head injury in currently active kickboxers

Correction to: EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusion

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

13 N-ammonia PET-derived right ventricular longitudinal strain and myocardial flow reserve in right coronary artery disease

First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients

Targeted Hsp70 fluorescence molecular endoscopy detects dysplasia in Barrett’s esophagus

Within-patient comparison between [68Ga]Ga-tilmanocept PET/CT lymphoscintigraphy and [99mTc]Tc-tilmanocept lymphoscintigraphy for sentinel lymph node detection in oral cancer: a pilot study

Correction to: Imaging of neuroinflammation due to repetitive head injury in currently active kickboxers

Correction to: EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3