An Image Registration Framework to Estimate 3D Myocardial Strains from Cine Cardiac MRI in Mice

Maziyar Keshavarzian; Elizabeth Fugate; Saurabh Chavan; Vy Chu; Mohammed Arif; Diana Lindquist; Sakthivel Sadayappan; Reza Avazmohammadi

doi:10.1007/978-3-030-78710-3_27

An Image Registration Framework to Estimate 3D Myocardial Strains from Cine Cardiac MRI in Mice

Maziyar Keshavarzian, Elizabeth Fugate, Saurabh Chavan, Vy Chu, Mohammed Arif, Diana Lindquist, Sakthivel Sadayappan, Reza Avazmohammadi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

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.

Original language	English (US)
Title of host publication	Functional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings
Editors	Daniel B. Ennis, Luigi E. Perotti, Vicky Y. Wang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	273-284
Number of pages	12
ISBN (Print)	9783030787097
DOIs	https://doi.org/10.1007/978-3-030-78710-3_27
State	Published - 2021
Event	11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021 - Virtual, Online Duration: Jun 21 2021 → Jun 25 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12738 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021
City	Virtual, Online
Period	6/21/21 → 6/25/21

Keywords

Cardiac magnetic resonance imaging
Left ventricle
Small animals
Wall strain

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-78710-3_27

Cite this

Keshavarzian, M., Fugate, E., Chavan, S., Chu, V., Arif, M., Lindquist, D., Sadayappan, S., & Avazmohammadi, R. (2021). An Image Registration Framework to Estimate 3D Myocardial Strains from Cine Cardiac MRI in Mice. In D. B. Ennis, L. E. Perotti, & V. Y. Wang (Eds.), Functional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings (pp. 273-284). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12738 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-78710-3_27

An Image Registration Framework to Estimate 3D Myocardial Strains from Cine Cardiac MRI in Mice. / Keshavarzian, Maziyar; Fugate, Elizabeth; Chavan, Saurabh et al.

Functional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings. ed. / Daniel B. Ennis; Luigi E. Perotti; Vicky Y. Wang. Springer Science and Business Media Deutschland GmbH, 2021. p. 273-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12738 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Keshavarzian, M, Fugate, E, Chavan, S, Chu, V, Arif, M, Lindquist, D, Sadayappan, S & Avazmohammadi, R 2021, An Image Registration Framework to Estimate 3D Myocardial Strains from Cine Cardiac MRI in Mice. in DB Ennis, LE Perotti & VY Wang (eds), Functional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12738 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 273-284, 11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021, Virtual, Online, 6/21/21. https://doi.org/10.1007/978-3-030-78710-3_27

Keshavarzian M, Fugate E, Chavan S, Chu V, Arif M, Lindquist D et al. An Image Registration Framework to Estimate 3D Myocardial Strains from Cine Cardiac MRI in Mice. In Ennis DB, Perotti LE, Wang VY, editors, Functional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 273-284. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-78710-3_27

Keshavarzian, Maziyar ; Fugate, Elizabeth ; Chavan, Saurabh et al. / An Image Registration Framework to Estimate 3D Myocardial Strains from Cine Cardiac MRI in Mice. Functional Imaging and Modeling of the Heart - 11th International Conference, FIMH 2021, Proceedings. editor / Daniel B. Ennis ; Luigi E. Perotti ; Vicky Y. Wang. Springer Science and Business Media Deutschland GmbH, 2021. pp. 273-284 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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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note = "Funding Information: 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. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 11th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2021 ; Conference date: 21-06-2021 Through 25-06-2021",

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An Image Registration Framework to Estimate 3D Myocardial Strains from Cine Cardiac MRI in Mice

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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