posted on 2021-09-13, 18:33authored byCaroline Roney, Rokas Bendikas, Farhad Pashakhanloo, Cesare Corrado, Edmond Vigmond, Elliot McVeigh, Natalia Trayanova, Steven Niederer
Background: 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.
Dataset Description:
We include endocardial and epicardial left and right atrial surfaces for each of the 7 anatomies included in our study (Constructing a Human Atrial Fibre Atlas, ABME, 2020), together with their fibre fields. We also include the average fibre field for each of the atrial surfaces displayed on anatomy number 6 (named *_A).
For each of the surfaces, we also include universal atrial coordinate fields alpha and beta, which are a lateral-septal coordinate and posterior-anterior coordinate for the LA (IVC-SVC coordinate for the RA). More details on the coordinate construction are given in our manuscript and https://www.ncbi.nlm.nih.gov/pubmed/31026761. These coordinates can be used for registering datasets.
These meshes are in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the fibre field defined on the elements.
We have also included mesh files for the Cardiac Arrhythmia Research Package simulator. These are a list of nodal coordinates (.pts file), a list of triangular elements (.elem file), and a fibre file (.lon). More details on this file format and the carpentry simulator are available at: https://carpentry.medunigraz.at/carputils/index.html.
Funding
Predicting Atrial Fibrillation Mechanisms Through Deep Learning
Kings Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre; Leducq Foundation (16 CVD 02)
History
Temporal coverage
Unlimited
Geospatial coverage
Unlimited
Collection method
These data are left and right atrial endocardial and epicardial surfaces with fibre fields extracted from 7 human atrial DTMRI datasets (from Pashakhanloo F et al Circ A&E 2016, https://www.ncbi.nlm.nih.gov/pubmed/27071829). We also include the average fibre field calculated across the 7 anatomies.
Language
English
Copyright owner
Caroline Roney - School of Biomedical Engineering and Imaging Sciences, King's College London; Steven Niederer - School of Biomedical Engineering and Imaging Sciences, King's College London; Rokas Bendikas - School of Biomedical Engineering and Imaging Sciences, King's College London; Farhad Pashakhanloo - Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, USA; Cesare Corrado - School of Biomedical Engineering and Imaging Sciences, King’s College London; Edmond Vigmond - LIRYC Electrophysiology and Heart Modeling Institute, Bordeaux Fondation, avenue du Haut-Leveque, Pessac 33600, France; Elliot McVeigh - Department of Bioengineering, UC San Diego School of Engineering,USA; Natalia Trayanova - Department of Biomedical Engineering, Johns Hopkins University, USA