Abstract
Accurate and efficient quantification of cardiac motion offers promising biomarkers for non-invasive diagnosis and prognosis of structural heart diseases. Cine cardiac magnetic resonance imaging remains one of the most advanced imaging tools to provide image acquisitions needed to assess and quantify in-vivo heart kinematics. The majority of cardiac motion studies are focused on human data, and there remains a need to develop and implement an image-registration pipeline to quantify full three-dimensional (3D) cardiac motion in mice where ideal image acquisition is challenged by the subject size and heart rate and the possibility of traditional tagged imaging is hampered. In this study, we used diffeomorphic image registration to estimate strains in the left ventricular wall in two wild-type mice and one diabetic mouse. Our pipeline resulted in a continuous and fully 3D strain map over one cardiac cycle. The estimation of 3D regional and transmural variations of strains is a critical step towards identifying mechanistic biomarkers for improved diagnosis and phenotyping of structural left heart diseases including heart failure with reduced or preserved ejection fraction.
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Acknowledgements
This work was supported by the National Institutes of Health R00HL138288 to R.A. Dr. Sadayappan has received support from National Institutes of Health grants R01 HL130356, R01 HL105826, R01 AR078001, and R01 HL143490; American Heart Association 2019 Institutional Undergraduate Student (19UFEL34380251) and transformation (19TPA34830084) awards.
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Keshavarzian, M. et al. (2021). An Image Registration Framework to Estimate 3D Myocardial Strains from Cine Cardiac MRI in Mice. In: Ennis, D.B., Perotti, L.E., Wang, V.Y. (eds) Functional Imaging and Modeling of the Heart. FIMH 2021. Lecture Notes in Computer Science(), vol 12738. Springer, Cham. https://doi.org/10.1007/978-3-030-78710-3_27
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DOI: https://doi.org/10.1007/978-3-030-78710-3_27
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