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A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices

Cardiac magnetic resonance (CMR) imaging is a valuable modality in the diagnosis and characterization of cardiovascular diseases, since it can identify abnormalities in structure and function of the myocardium non-invasively and without the need for ionizing radiation. However, in clinical practice,...

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Detalles Bibliográficos
Autores principales: Banerjee, Abhirup, Camps, Julià, Zacur, Ernesto, Andrews, Christopher M., Rudy, Yoram, Choudhury, Robin P., Rodriguez, Blanca, Grau, Vicente
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543046/
https://www.ncbi.nlm.nih.gov/pubmed/34689630
http://dx.doi.org/10.1098/rsta.2020.0257
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author Banerjee, Abhirup
Camps, Julià
Zacur, Ernesto
Andrews, Christopher M.
Rudy, Yoram
Choudhury, Robin P.
Rodriguez, Blanca
Grau, Vicente
author_facet Banerjee, Abhirup
Camps, Julià
Zacur, Ernesto
Andrews, Christopher M.
Rudy, Yoram
Choudhury, Robin P.
Rodriguez, Blanca
Grau, Vicente
author_sort Banerjee, Abhirup
collection PubMed
description Cardiac magnetic resonance (CMR) imaging is a valuable modality in the diagnosis and characterization of cardiovascular diseases, since it can identify abnormalities in structure and function of the myocardium non-invasively and without the need for ionizing radiation. However, in clinical practice, it is commonly acquired as a collection of separated and independent 2D image planes, which limits its accuracy in 3D analysis. This paper presents a completely automated pipeline for generating patient-specific 3D biventricular heart models from cine magnetic resonance (MR) slices. Our pipeline automatically selects the relevant cine MR images, segments them using a deep learning-based method to extract the heart contours, and aligns the contours in 3D space correcting possible misalignments due to breathing or subject motion first using the intensity and contours information from the cine data and next with the help of a statistical shape model. Finally, the sparse 3D representation of the contours is used to generate a smooth 3D biventricular mesh. The computational pipeline is applied and evaluated in a CMR dataset of 20 healthy subjects. Our results show an average reduction of misalignment artefacts from 1.82 ± 1.60 mm to 0.72 ± 0.73 mm over 20 subjects, in terms of distance from the final reconstructed mesh. The high-resolution 3D biventricular meshes obtained with our computational pipeline are used for simulations of electrical activation patterns, showing agreement with non-invasive electrocardiographic imaging. The automatic methodologies presented here for patient-specific MR imaging-based 3D biventricular representations contribute to the efficient realization of precision medicine, enabling the enhanced interpretability of clinical data, the digital twin vision through patient-specific image-based modelling and simulation, and augmented reality applications. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.
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spelling pubmed-85430462022-02-02 A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices Banerjee, Abhirup Camps, Julià Zacur, Ernesto Andrews, Christopher M. Rudy, Yoram Choudhury, Robin P. Rodriguez, Blanca Grau, Vicente Philos Trans A Math Phys Eng Sci Articles Cardiac magnetic resonance (CMR) imaging is a valuable modality in the diagnosis and characterization of cardiovascular diseases, since it can identify abnormalities in structure and function of the myocardium non-invasively and without the need for ionizing radiation. However, in clinical practice, it is commonly acquired as a collection of separated and independent 2D image planes, which limits its accuracy in 3D analysis. This paper presents a completely automated pipeline for generating patient-specific 3D biventricular heart models from cine magnetic resonance (MR) slices. Our pipeline automatically selects the relevant cine MR images, segments them using a deep learning-based method to extract the heart contours, and aligns the contours in 3D space correcting possible misalignments due to breathing or subject motion first using the intensity and contours information from the cine data and next with the help of a statistical shape model. Finally, the sparse 3D representation of the contours is used to generate a smooth 3D biventricular mesh. The computational pipeline is applied and evaluated in a CMR dataset of 20 healthy subjects. Our results show an average reduction of misalignment artefacts from 1.82 ± 1.60 mm to 0.72 ± 0.73 mm over 20 subjects, in terms of distance from the final reconstructed mesh. The high-resolution 3D biventricular meshes obtained with our computational pipeline are used for simulations of electrical activation patterns, showing agreement with non-invasive electrocardiographic imaging. The automatic methodologies presented here for patient-specific MR imaging-based 3D biventricular representations contribute to the efficient realization of precision medicine, enabling the enhanced interpretability of clinical data, the digital twin vision through patient-specific image-based modelling and simulation, and augmented reality applications. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’. The Royal Society 2021-12-13 2021-10-25 /pmc/articles/PMC8543046/ /pubmed/34689630 http://dx.doi.org/10.1098/rsta.2020.0257 Text en © 2021 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Banerjee, Abhirup
Camps, Julià
Zacur, Ernesto
Andrews, Christopher M.
Rudy, Yoram
Choudhury, Robin P.
Rodriguez, Blanca
Grau, Vicente
A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices
title A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices
title_full A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices
title_fullStr A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices
title_full_unstemmed A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices
title_short A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices
title_sort completely automated pipeline for 3d reconstruction of human heart from 2d cine magnetic resonance slices
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543046/
https://www.ncbi.nlm.nih.gov/pubmed/34689630
http://dx.doi.org/10.1098/rsta.2020.0257
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