Cargando…

Constructing a Human Atrial Fibre Atlas

Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challengin...

Descripción completa

Detalles Bibliográficos
Autores principales: Roney, Caroline H., Bendikas, Rokas, Pashakhanloo, Farhad, Corrado, Cesare, Vigmond, Edward J., McVeigh, Elliot R., Trayanova, Natalia A., Niederer, Steven A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773625/
https://www.ncbi.nlm.nih.gov/pubmed/32458222
http://dx.doi.org/10.1007/s10439-020-02525-w
_version_ 1783630083534094336
author Roney, Caroline H.
Bendikas, Rokas
Pashakhanloo, Farhad
Corrado, Cesare
Vigmond, Edward J.
McVeigh, Elliot R.
Trayanova, Natalia A.
Niederer, Steven A.
author_facet Roney, Caroline H.
Bendikas, Rokas
Pashakhanloo, Farhad
Corrado, Cesare
Vigmond, Edward J.
McVeigh, Elliot R.
Trayanova, Natalia A.
Niederer, Steven A.
author_sort Roney, Caroline H.
collection PubMed
description Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. We extended an atrial coordinate system to map the pulmonary veins, vena cava and appendages to standardised positions in the coordinate system corresponding to the average location across the anatomies. We then expressed each fibre field in this atrial coordinate system and calculated an average fibre field. To assess the effects of fibre field on patient-specific modelling predictions, we calculated paced activation time maps and electrical driver locations during AF. In total, 756 activation time maps were calculated (7 anatomies with 9 fibre maps and 2 pacing locations, for the endocardial, epicardial and bilayer surface models of the LA and RA). Patient-specific fibre fields had a relatively small effect on average paced activation maps (range of mean local activation time difference for LA fields: 2.67–3.60 ms, and for RA fields: 2.29–3.44 ms), but had a larger effect on maximum LAT differences (range for LA 12.7–16.6%; range for RA 11.9–15.0%). A total of 126 phase singularity density maps were calculated (7 anatomies with 9 fibre maps for the LA and RA bilayer models). The fibre field corresponding to anatomy 1 had the highest median PS density map correlation coefficient for LA bilayer simulations (0.44 compared to the other correlations, ranging from 0.14 to 0.39), while the average fibre field had the highest correlation for the RA bilayer simulations (0.61 compared to the other correlations, ranging from 0.37 to 0.56). For sinus rhythm simulations, average activation time is robust to fibre field direction; however, maximum differences can still be significant. Patient specific fibres are more important for arrhythmia simulations, particularly in the left atrium. We propose using the fibre field corresponding to DTMRI dataset 1 for LA simulations, and the average fibre field for RA simulations as these optimally predicted arrhythmia properties. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10439-020-02525-w) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-7773625
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Springer International Publishing
record_format MEDLINE/PubMed
spelling pubmed-77736252021-01-04 Constructing a Human Atrial Fibre Atlas Roney, Caroline H. Bendikas, Rokas Pashakhanloo, Farhad Corrado, Cesare Vigmond, Edward J. McVeigh, Elliot R. Trayanova, Natalia A. Niederer, Steven A. Ann Biomed Eng Original Article Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. We extended an atrial coordinate system to map the pulmonary veins, vena cava and appendages to standardised positions in the coordinate system corresponding to the average location across the anatomies. We then expressed each fibre field in this atrial coordinate system and calculated an average fibre field. To assess the effects of fibre field on patient-specific modelling predictions, we calculated paced activation time maps and electrical driver locations during AF. In total, 756 activation time maps were calculated (7 anatomies with 9 fibre maps and 2 pacing locations, for the endocardial, epicardial and bilayer surface models of the LA and RA). Patient-specific fibre fields had a relatively small effect on average paced activation maps (range of mean local activation time difference for LA fields: 2.67–3.60 ms, and for RA fields: 2.29–3.44 ms), but had a larger effect on maximum LAT differences (range for LA 12.7–16.6%; range for RA 11.9–15.0%). A total of 126 phase singularity density maps were calculated (7 anatomies with 9 fibre maps for the LA and RA bilayer models). The fibre field corresponding to anatomy 1 had the highest median PS density map correlation coefficient for LA bilayer simulations (0.44 compared to the other correlations, ranging from 0.14 to 0.39), while the average fibre field had the highest correlation for the RA bilayer simulations (0.61 compared to the other correlations, ranging from 0.37 to 0.56). For sinus rhythm simulations, average activation time is robust to fibre field direction; however, maximum differences can still be significant. Patient specific fibres are more important for arrhythmia simulations, particularly in the left atrium. We propose using the fibre field corresponding to DTMRI dataset 1 for LA simulations, and the average fibre field for RA simulations as these optimally predicted arrhythmia properties. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10439-020-02525-w) contains supplementary material, which is available to authorized users. Springer International Publishing 2020-05-26 2021 /pmc/articles/PMC7773625/ /pubmed/32458222 http://dx.doi.org/10.1007/s10439-020-02525-w Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Article
Roney, Caroline H.
Bendikas, Rokas
Pashakhanloo, Farhad
Corrado, Cesare
Vigmond, Edward J.
McVeigh, Elliot R.
Trayanova, Natalia A.
Niederer, Steven A.
Constructing a Human Atrial Fibre Atlas
title Constructing a Human Atrial Fibre Atlas
title_full Constructing a Human Atrial Fibre Atlas
title_fullStr Constructing a Human Atrial Fibre Atlas
title_full_unstemmed Constructing a Human Atrial Fibre Atlas
title_short Constructing a Human Atrial Fibre Atlas
title_sort constructing a human atrial fibre atlas
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773625/
https://www.ncbi.nlm.nih.gov/pubmed/32458222
http://dx.doi.org/10.1007/s10439-020-02525-w
work_keys_str_mv AT roneycarolineh constructingahumanatrialfibreatlas
AT bendikasrokas constructingahumanatrialfibreatlas
AT pashakhanloofarhad constructingahumanatrialfibreatlas
AT corradocesare constructingahumanatrialfibreatlas
AT vigmondedwardj constructingahumanatrialfibreatlas
AT mcveighelliotr constructingahumanatrialfibreatlas
AT trayanovanataliaa constructingahumanatrialfibreatlas
AT niedererstevena constructingahumanatrialfibreatlas