Literature DB >> 33383331

Estimating cardiomyofiber strain in vivo by solving a computational model.

Luigi E Perotti1, Ilya A Verzhbinsky2, Kévin Moulin3, Tyler E Cork4, Michael Loecher3, Daniel Balzani5, Daniel B Ennis3.   

Abstract

Since heart contraction results from the electrically activated contraction of millions of cardiomyocytes, a measure of cardiomyocyte shortening mechanistically underlies cardiac contraction. In this work we aim to measure preferential aggregate cardiomyocyte ("myofiber") strains based on Magnetic Resonance Imaging (MRI) data acquired to measure both voxel-wise displacements through systole and myofiber orientation. In order to reduce the effect of experimental noise on the computed myofiber strains, we recast the strains calculation as the solution of a boundary value problem (BVP). This approach does not require a calibrated material model, and consequently is independent of specific myocardial material properties. The solution to this auxiliary BVP is the displacement field corresponding to assigned values of myofiber strains. The actual myofiber strains are then determined by minimizing the difference between computed and measured displacements. The approach is validated using an analytical phantom, for which the ground-truth solution is known. The method is applied to compute myofiber strains using in vivo displacement and myofiber MRI data acquired in a mid-ventricular left ventricle section in N=8 swine subjects. The proposed method shows a more physiological distribution of myofiber strains compared to standard approaches that directly differentiate the displacement field.
Copyright © 2020. Published by Elsevier B.V.

Entities:  

Keywords:  Analytical phantom; Cardiac kinematics; In vivo cardiomyofiber strains; Model based image analysis

Mesh:

Year:  2020        PMID: 33383331      PMCID: PMC7956226          DOI: 10.1016/j.media.2020.101932

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  46 in total

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Journal:  IEEE Trans Med Imaging       Date:  1999-08       Impact factor: 10.048

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Journal:  Radiology       Date:  2000-07       Impact factor: 11.105

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Authors:  Martin R Pfaller; Julia M Hörmann; Martina Weigl; Andreas Nagler; Radomir Chabiniok; Cristóbal Bertoglio; Wolfgang A Wall
Journal:  Biomech Model Mechanobiol       Date:  2018-12-10

5.  Equilibrated warping: Finite element image registration with finite strain equilibrium gap regularization.

Authors:  M Genet; C T Stoeck; C von Deuster; L C Lee; S Kozerke
Journal:  Med Image Anal       Date:  2018-08-22       Impact factor: 8.545

6.  TIME RESOLVED DISPLACEMENT-BASED REGISTRATION OF IN VIVO CDTI CARDIOMYOCYTE ORIENTATIONS.

Authors:  Ilya A Verzhbinsky; Patrick Magrath; Eric Aliotta; Daniel B Ennis; Luigi E Perotti
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2018-04

7.  Regional segmentation of ventricular models to achieve repolarization dispersion in cardiac electrophysiology modeling.

Authors:  L E Perotti; S Krishnamoorthi; N P Borgstrom; D B Ennis; W S Klug
Journal:  Int J Numer Method Biomed Eng       Date:  2015-04-28       Impact factor: 2.747

8.  Linear invariant tensor interpolation applied to cardiac diffusion tensor MRI.

Authors:  Jin Kyu Gahm; Nicholas Wisniewski; Gordon Kindlmann; Geoffrey L Kung; William S Klug; Alan Garfinkel; Daniel B Ennis
Journal:  Med Image Comput Comput Assist Interv       Date:  2012

9.  Effects of barium-induced cardiac contraction on large- and small-vessel intramyocardial blood volume.

Authors:  R M Judd; B I Levy
Journal:  Circ Res       Date:  1991-01       Impact factor: 17.367

10.  A computational framework for the statistical analysis of cardiac diffusion tensors: application to a small database of canine hearts.

Authors:  Jean-Marc Peyrat; Maxime Sermesant; Xavier Pennec; Hervé Delingette; Chenyang Xu; Elliot R McVeigh; Nicholas Ayache
Journal:  IEEE Trans Med Imaging       Date:  2007-11       Impact factor: 10.048

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  3 in total

1.  Native-resolution myocardial principal Eulerian strain mapping using convolutional neural networks and Tagged Magnetic Resonance Imaging.

Authors:  Inas A Yassine; Ahmed M Ghanem; Nader S Metwalli; Ahmed Hamimi; Ronald Ouwerkerk; Jatin R Matta; Michael A Solomon; Jason M Elinoff; Ahmed M Gharib; Khaled Z Abd-Elmoniem
Journal:  Comput Biol Med       Date:  2021-11-18       Impact factor: 4.589

2.  Using synthetic data generation to train a cardiac motion tag tracking neural network.

Authors:  Michael Loecher; Luigi E Perotti; Daniel B Ennis
Journal:  Med Image Anal       Date:  2021-09-10       Impact factor: 8.545

Review 3.  Myocardial mesostructure and mesofunction.

Authors:  Alexander J Wilson; Gregory B Sands; Ian J LeGrice; Alistair A Young; Daniel B Ennis
Journal:  Am J Physiol Heart Circ Physiol       Date:  2022-06-03       Impact factor: 5.125

  3 in total

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