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Evaluating Passive Myocardial Stiffness Using in vivo cine, cDTI, and Tagged MRI

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Functional Imaging and Modeling of the Heart (FIMH 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13958))

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Abstract

Increased passive myocardial stiffness is implicated in the pathophysiology of many cardiac diseases, and its in vivo estimation can improve management of heart disease. MRI-driven computational constitutive modeling has been used extensively to evaluate passive myocardial stiffness. This approach requires subject-specific data that is best acquired with different MRI sequences: conventional cine (e.g. bSSFP), tagged MRI (or DENSE), and cardiac diffusion tensor imaging. However, due to the lack of comprehensive datasets and the challenge of incorporating multi-phase and single-phase disparate MRI data, no studies have combined in vivo cine bSSFP, tagged MRI, and cardiac diffusion tensor imaging to estimate passive myocardial stiffness. The objective of this work was to develop a personalized in silico left ventricular model to evaluate passive myocardial stiffness by integrating subject-specific geometric data derived from cine bSSFP, regional kinematics extracted from tagged MRI, and myocardial microstructure measured using in vivo cardiac diffusion tensor imaging. To demonstrate the feasibility of using a complete subject-specific imaging dataset for passive myocardial stiffness estimation, we calibrated a bulk stiffness parameter of a transversely isotropic exponential constitutive relation to match the local kinematic field extracted from tagged MRI. This work establishes a pipeline for developing subject-specific biomechanical ventricular models to probe passive myocardial mechanical behavior, using comprehensive cardiac imaging data from multiple in vivo MRI sequences.

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Acknowledgements

This work was supported by NSF 2205103 and NIH R01 HL131823 to DBE.

Author information

Authors and Affiliations

  1. Radiology, Stanford University, Stanford, CA, 94305, USA

    Fikunwa O. Kolawole, Vicky Y. Wang, Michael Loecher, Tyler E. Cork & Daniel B. Ennis

  2. Radiology, Veterans Administration Health Care System, Palo Alto, CA, 94304, USA

    Fikunwa O. Kolawole, Vicky Y. Wang, Michael Loecher, Tyler E. Cork & Daniel B. Ennis

  3. Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA

    Fikunwa O. Kolawole & Ellen Kuhl

  4. Cardiovascular Institute, Stanford University, Stanford, CA, USA

    Daniel B. Ennis

  5. Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand

    Vicky Y. Wang

  6. Univ. Grenoble Alpes, CNRS, TIMC UMR 5525, 38000, Grenoble, France

    Bianca Freytag & Martyn P. Nash

  7. Bioengineering, Stanford University, Stanford, CA, 94305, USA

    Tyler E. Cork

Authors
  1. Fikunwa O. Kolawole
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  2. Vicky Y. Wang
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  3. Bianca Freytag
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  4. Michael Loecher
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  5. Tyler E. Cork
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  6. Martyn P. Nash
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  7. Ellen Kuhl
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  8. Daniel B. Ennis
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Corresponding author

Correspondence to Fikunwa O. Kolawole .

Editor information

Editors and Affiliations

  1. CREATIS, INSA-Lyon, University of Lyon, Lyon, France

    Olivier Bernard

  2. CREATIS, CNRS, University of Lyon, Lyon, France

    Patrick Clarysse

  3. CREATIS, IUF, University of Lyon, Lyon, France

    Nicolas Duchateau

  4. TIMC, Savoie Mont Blanc University, Chambéry, France

    Jacques Ohayon

  5. CREATIS, Jean Monnet University, Saint-Etienne, France

    Magalie Viallon

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Kolawole, F.O. et al. (2023). Evaluating Passive Myocardial Stiffness Using in vivo cine, cDTI, and Tagged MRI. In: Bernard, O., Clarysse, P., Duchateau, N., Ohayon, J., Viallon, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2023. Lecture Notes in Computer Science, vol 13958. Springer, Cham. https://doi.org/10.1007/978-3-031-35302-4_54

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  • DOI: https://doi.org/10.1007/978-3-031-35302-4_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35301-7

  • Online ISBN: 978-3-031-35302-4

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