TY - JOUR
T1 - Multi-modal characterization of the left atrium by a fully automated integration of pre-procedural cardiac imaging and electro-anatomical mapping
AU - Hermans, Ben J.M.
AU - Bijvoet, Geertruida P.
AU - Holtackers, Robert J.
AU - Mihl, Casper
AU - Luermans, Justin G.L.M.
AU - Maesen, Bart
AU - Vernooy, Kevin
AU - Linz, Dominik
AU - Chaldoupi, Sevasti Maria
AU - Schotten, Ulrich
N1 - Funding Information:
This work was supported by the Netherlands Heart Foundation (CVON2014-09, RACE V Reappraisal of Atrial Fibrillation: Interaction between hyperCoagulability, Electrical remodeling, and Vascular Destabilisation in the Progression of AF, and Grant number 01-002-2022-0118, EmbRACE: Electro-Molecular Basis and the theRapeutic management of Atrial Cardiomyopathy, fibrillation and associated outcomEs), the European Union (ITN Network Personalize AF: Personalized Therapies for Atrial Fibrillation: a translational network, grant number 860974; MAESTRIA: Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation, grant number 965286).
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Background: The combination of information obtained from pre-procedural cardiac imaging and electro-anatomical mapping (EAM) can potentially help to locate new ablation targets. In this study we developed and evaluated a fully automated technique to align left atrial (LA) anatomies obtained from CT- and MRI-scans with LA anatomies obtained from EAM. Methods: Twenty-one patients scheduled for a pulmonary vein (PV) isolation with a pre-procedural MRI were enrolled. Additionally, a recent computed tomography (CT) scan was available in 12 patients. LA anatomies were segmented from MRI-scans using ADAS-AF (Galgo Medical, Barcelona) and from the CT-scans using Slicer3D. MRI and CT anatomies were aligned with the EAM anatomy using an iterative closest plane-to-plane algorithm. Initially, the algorithm included the PVs, LA appendage and mitral valve anulus as they are the most distinctive landmarks. Subsequently, the algorithm was applied again, excluding these structures, with only three iterative steps to refine the alignment of the true LA surface. The result of the alignments was quantified by the Euclidian distance between the aligned anatomies after excluding PVs, LA appendage and mitral anulus. Results: Our algorithm successfully aligned 20/21 MRI anatomies and 11/12 CT anatomies with the corresponding EAM anatomies. The average median residual distances were 1.9 ± 0.6 mm and 2.5 ± 0.8 mm for MRI and CT anatomies respectively. The average LA surface with a residual distance less than 5.00 mm was 89 ± 9% and 89 ± 10% for MRI and CT anatomies respectively. Conclusion: An iterative closest plane-to-plane algorithm is a reliable method to automatically align pre-procedural cardiac images with anatomies acquired during ablation procedures.
AB - Background: The combination of information obtained from pre-procedural cardiac imaging and electro-anatomical mapping (EAM) can potentially help to locate new ablation targets. In this study we developed and evaluated a fully automated technique to align left atrial (LA) anatomies obtained from CT- and MRI-scans with LA anatomies obtained from EAM. Methods: Twenty-one patients scheduled for a pulmonary vein (PV) isolation with a pre-procedural MRI were enrolled. Additionally, a recent computed tomography (CT) scan was available in 12 patients. LA anatomies were segmented from MRI-scans using ADAS-AF (Galgo Medical, Barcelona) and from the CT-scans using Slicer3D. MRI and CT anatomies were aligned with the EAM anatomy using an iterative closest plane-to-plane algorithm. Initially, the algorithm included the PVs, LA appendage and mitral valve anulus as they are the most distinctive landmarks. Subsequently, the algorithm was applied again, excluding these structures, with only three iterative steps to refine the alignment of the true LA surface. The result of the alignments was quantified by the Euclidian distance between the aligned anatomies after excluding PVs, LA appendage and mitral anulus. Results: Our algorithm successfully aligned 20/21 MRI anatomies and 11/12 CT anatomies with the corresponding EAM anatomies. The average median residual distances were 1.9 ± 0.6 mm and 2.5 ± 0.8 mm for MRI and CT anatomies respectively. The average LA surface with a residual distance less than 5.00 mm was 89 ± 9% and 89 ± 10% for MRI and CT anatomies respectively. Conclusion: An iterative closest plane-to-plane algorithm is a reliable method to automatically align pre-procedural cardiac images with anatomies acquired during ablation procedures.
KW - Alignment
KW - Atrial fibrillation
KW - clinicaltrials.gov NCT04342312
KW - Imaging
KW - Integration
KW - Mapping
KW - Netherlands Trial Register NL7894
U2 - 10.1016/j.ijcha.2023.101276
DO - 10.1016/j.ijcha.2023.101276
M3 - Article
SN - 2352-9067
VL - 49
JO - IJC Heart and Vasculature
JF - IJC Heart and Vasculature
IS - 1
M1 - 101276
ER -