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Constructing a Human Atrial Fibre Atlas

Research output: Contribution to journalArticle

Caroline Roney, Rokas Bendikas, Farhad Pashakhanloo, Cesare Corrado, Edward Vigmond, E McVeigh, Natalia A Trayanova, Steven Niederer

Original languageEnglish
JournalAnnals of Biomedical Engineering
Accepted/In press26 Apr 2020

King's Authors

Abstract

Introduction:
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.
Methods:
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.
Results:
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: 0.14-0.39), while the average fibre field had the highest correlation for the RA bilayer simulations (0.61 compared to the other correlations, ranging from: 0.37-0.56).
Conclusions:
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.

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