Fast-track virtual reality for cardiac imaging in congenital heart disease.


Journal

Journal of cardiac surgery
ISSN: 1540-8191
Titre abrégé: J Card Surg
Pays: United States
ID NLM: 8908809

Informations de publication

Date de publication:
Jul 2021
Historique:
received: 07 01 2021
accepted: 03 02 2021
pubmed: 25 3 2021
medline: 11 6 2021
entrez: 24 3 2021
Statut: ppublish

Résumé

We sought to evaluate the appropriateness of cardiac anatomy renderings by a new virtual reality (VR) technology, entitled DIVA, directly applicable to raw magnetic resonance imaging (MRI) data without intermediate segmentation steps in comparison to standard three-dimensional (3D) rendering techniques (3D PDF and 3D printing). Differences in post-processing times were also evaluated. We reconstructed 3D (STL, 3D-PDF, and 3D printed ones) and VR models of three patients with different types of complex congenital heart disease (CHD). We then asked a senior pediatric heart surgeon to compare and grade the results obtained. All anatomical structures were well visualized in both VR and 3D PDF/printed models. Ventricular-arterial connections and their relationship with the great vessels were better visualized with the VR model (Case 2); aortic arch anatomy and details were also better visualized by the VR model (Case 3). The median post-processing time to get VR models using DIVA was 5 min in comparison to 8 h (range 8-12 h including printing time) for 3D models (PDF/printed). VR directly applied to non-segmented 3D-MRI data set is a promising technique for 3D advanced modeling in CHD. It is systematically more consistent and faster when compared to standard 3D-modeling techniques.

Sections du résumé

BACKGROUND AND AIM OF THE STUDY OBJECTIVE
We sought to evaluate the appropriateness of cardiac anatomy renderings by a new virtual reality (VR) technology, entitled DIVA, directly applicable to raw magnetic resonance imaging (MRI) data without intermediate segmentation steps in comparison to standard three-dimensional (3D) rendering techniques (3D PDF and 3D printing). Differences in post-processing times were also evaluated.
METHODS METHODS
We reconstructed 3D (STL, 3D-PDF, and 3D printed ones) and VR models of three patients with different types of complex congenital heart disease (CHD). We then asked a senior pediatric heart surgeon to compare and grade the results obtained.
RESULTS RESULTS
All anatomical structures were well visualized in both VR and 3D PDF/printed models. Ventricular-arterial connections and their relationship with the great vessels were better visualized with the VR model (Case 2); aortic arch anatomy and details were also better visualized by the VR model (Case 3). The median post-processing time to get VR models using DIVA was 5 min in comparison to 8 h (range 8-12 h including printing time) for 3D models (PDF/printed).
CONCLUSIONS CONCLUSIONS
VR directly applied to non-segmented 3D-MRI data set is a promising technique for 3D advanced modeling in CHD. It is systematically more consistent and faster when compared to standard 3D-modeling techniques.

Identifiants

pubmed: 33760302
doi: 10.1111/jocs.15508
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2598-2602

Informations de copyright

© 2021 Wiley Periodicals LLC.

Références

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Forte MNV, Hussain T, Roest A, et al. Living the heart in three dimensions: applications of 3D printing in CHD. Cardiol Young. 2019;29:733-743.
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Ong CS, Krishnan A, Huang CY, et al. Role of virtual reality in congenital heart disease. Congenit Heart Dis. 2018;13:357-361.
El Beheiry M, Doutreligne S, Caporal C, Ostrertag C, Daham M, Masson JB. Virtual reality: beyond visualization. J Mol Biol. 2019;431:1315-1321.
El Beheiry M, Godard C, Caporal C, et al. DIVA: natural navigation inside 3D images using virtual reality. J Mol Biol. 2020;432:4745-4749.
Goo HW, Park SJ, Yoo SJ. Advanced medical use of three dimensional imaging in congenital heart disease: augmentes reality, mixed reality, virtual reality and three dimensional printing. Korean J Radiol. 2020;21:133-145.

Auteurs

Francesca Raimondi (F)

Unité médico-chirurgicale de cardiologie congénitale et pédiatrique, centre de référence des maladies cardiaques congénitales complexes-M3C, Hôpital universitaire Necker-Enfants Malades, Université de Paris, France.
Decision and Bayesian Computation, Computation Biology Department, CNRS, URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris, France.
Pediatric Radiology Unit, Hôpital universitaire Necker-Enfants Malades, Université de Paris, France.

Vladimiro Vida (V)

Pediatric and Congenital Cardiac Surgery Unit, University of Padua, Italy.

Charlotte Godard (C)

Decision and Bayesian Computation, Computation Biology Department, CNRS, URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris, France.

Francesco Bertelli (F)

Pediatric and Congenital Cardiac Surgery Unit, University of Padua, Italy.

Elena Reffo (E)

Pediatric Cardiology Unit, University of Padua, Italy.

Nathalie Boddaert (N)

Pediatric Radiology Unit, Hôpital universitaire Necker-Enfants Malades, Université de Paris, France.

Mohamed El Beheiry (M)

Decision and Bayesian Computation, Computation Biology Department, CNRS, URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris, France.

Jean-Baptiste Masson (JB)

Decision and Bayesian Computation, Computation Biology Department, CNRS, URS 3756, Neuroscience Department, CNRS UMR 3571, Institut Pasteur, Paris, France.

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