Literature DB >> 31432042

High-Resolution Ex Vivo Microstructural MRI After Restoring Ventricular Geometry via 3D Printing.

Tyler E Cork1,2, Luigi E Perotti3, Ilya A Verzhbinsky1, Michael Loecher1, Daniel B Ennis1.   

Abstract

Computational modeling of the heart requires accurately incorporating both gross anatomical detail and local microstructural information. Together, these provide the necessary data to build 3D meshes for simulation of cardiac mechanics and electrophysiology. Recent MRI advances make it possible to measure detailed heart motion in vivo, but in vivo microstructural imaging of the heart remains challenging. Consequently, the most detailed measurements of microstructural organization and microanatomical infarct details are obtained ex vivo. The objective of this work was to develop and evaluate a new method for restoring ex vivo ventricular geometry to match the in vivo configuration. This approach aids the integration of high-resolution ex vivo microstructural information with in vivo motion measurements. The method uses in vivo cine imaging to generate surface meshes, then creates a 3D printed left ventricular (LV) blood pool cast and a pericardial mold to restore the ex vivo cardiac geometry to a mid-diastasis reference configuration. The method was evaluated in healthy (N = 7) and infarcted (N = 3) swine. Dice similarity coefficients were calculated between in vivo and ex vivo images for the LV cavity (0.93 ± 0.01), right ventricle (RV) cavity (0.80 ± 0.05), and the myocardium (0.72 ± 0.04). The R 2 coefficient between in vivo and ex vivo LV and RV cavity volumes were 0.95 and 0.91, respectively. These results suggest that this method adequately restores ex vivo geometry to match in vivo geometry. This approach permits a more precise incorporation of high-resolution ex vivo anatomical and microstructural data into computational models that use in vivo data for simulation of cardiac mechanics and electrophysiology.

Entities:  

Keywords:  3D printing; Cardiac electromechanics; Computational modeling; Magnetic resonance imaging

Year:  2019        PMID: 31432042      PMCID: PMC6701689          DOI: 10.1007/978-3-030-21949-9_20

Source DB:  PubMed          Journal:  Funct Imaging Model Heart


  16 in total

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Authors:  J D Bayer; R C Blake; G Plank; N A Trayanova
Journal:  Ann Biomed Eng       Date:  2012-05-31       Impact factor: 3.934

2.  The medical imaging interaction toolkit.

Authors:  Ivo Wolf; Marcus Vetter; Ingmar Wegner; Thomas Böttger; Marco Nolden; Max Schöbinger; Mark Hastenteufel; Tobias Kunert; Hans-Peter Meinzer
Journal:  Med Image Anal       Date:  2005-12       Impact factor: 8.545

3.  Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images.

Authors:  Daniel B Ennis; Gordon Kindlmann
Journal:  Magn Reson Med       Date:  2006-01       Impact factor: 4.668

4.  Imaging three-dimensional myocardial mechanics using navigator-gated volumetric spiral cine DENSE MRI.

Authors:  Xiaodong Zhong; Bruce S Spottiswoode; Craig H Meyer; Christopher M Kramer; Frederick H Epstein
Journal:  Magn Reson Med       Date:  2010-10       Impact factor: 4.668

5.  Convex optimized diffusion encoding (CODE) gradient waveforms for minimum echo time and bulk motion-compensated diffusion-weighted MRI.

Authors:  Eric Aliotta; Holden H Wu; Daniel B Ennis
Journal:  Magn Reson Med       Date:  2016-02-22       Impact factor: 4.668

6.  Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function.

Authors:  Vicky Y Wang; H I Lam; Daniel B Ennis; Brett R Cowan; Alistair A Young; Martyn P Nash
Journal:  Med Image Anal       Date:  2009-07-16       Impact factor: 8.545

7.  Phase-sensitive inversion recovery for detecting myocardial infarction using gadolinium-delayed hyperenhancement.

Authors:  Peter Kellman; Andrew E Arai; Elliot R McVeigh; Anthony H Aletras
Journal:  Magn Reson Med       Date:  2002-02       Impact factor: 4.668

8.  Assessment of murine brain tissue shrinkage caused by different histological fixatives using magnetic resonance and computed tomography imaging.

Authors:  Hans F Wehrl; Ilja Bezrukov; Stefan Wiehr; Mareike Lehnhoff; Kerstin Fuchs; Julia G Mannheim; Leticia Quintanilla-Martinez; Ursula Kohlhofer; Manfred Kneilling; Bernd J Pichler; Alexander W Sauter
Journal:  Histol Histopathol       Date:  2014-12-11       Impact factor: 2.303

9.  Dual-phase cardiac diffusion tensor imaging with strain correction.

Authors:  Christian T Stoeck; Aleksandra Kalinowska; Constantin von Deuster; Jack Harmer; Rachel W Chan; Markus Niemann; Robert Manka; David Atkinson; David E Sosnovik; Choukri Mekkaoui; Sebastian Kozerke
Journal:  PLoS One       Date:  2014-09-05       Impact factor: 3.240

10.  Electrophysiology of Heart Failure Using a Rabbit Model: From the Failing Myocyte to Ventricular Fibrillation.

Authors:  Aditya V S Ponnaluri; Luigi E Perotti; Michael Liu; Zhilin Qu; James N Weiss; Daniel B Ennis; William S Klug; Alan Garfinkel
Journal:  PLoS Comput Biol       Date:  2016-06-23       Impact factor: 4.475

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  3 in total

1.  A groupwise registration and tractography framework for cardiac myofiber architecture description by diffusion MRI: An application to the ventricular junctions.

Authors:  Julie Magat; Maxime Yon; Yann Bihan-Poudec; Valéry Ozenne
Journal:  PLoS One       Date:  2022-07-18       Impact factor: 3.752

2.  Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data.

Authors:  Tanjib Rahman; Kévin Moulin; Luigi E Perotti
Journal:  Appl Sci (Basel)       Date:  2022-03-30       Impact factor: 2.838

3.  Estimating cardiomyofiber strain in vivo by solving a computational model.

Authors:  Luigi E Perotti; Ilya A Verzhbinsky; Kévin Moulin; Tyler E Cork; Michael Loecher; Daniel Balzani; Daniel B Ennis
Journal:  Med Image Anal       Date:  2020-12-05       Impact factor: 8.545

  3 in total

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