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Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes

Abstract : Integrating spatial information about atrial physiology and anatomy in a single patient from multimodal datasets, as well as generalizing these data across patients, requires a common coordinate system. In the atria, this is challenging due to the complexity and variability of the anatomy. We aimed to develop and validate a Universal Atrial Coordinate (UAC) system for the following applications: combination and assessment of multimodal data; comparison of spatial data across patients; 2D visualization; and construction of patient specific geometries to test mechanistic hypotheses. Left and right atrial LGE-MRI data were segmented and meshed. Two coordinates were calculated for each atrium by solving Laplace's equation, with boundary conditions assigned using five landmark points. The coordinate system was used to map spatial information between atrial meshes, including scalar fields measured using different mapping modalities, and atrial anatomic structures and fibre directions from a reference geometry. Average error in point transfer from a source mesh to a destination mesh and back again was less than 0.1 mm for the left atrium and 0.02 mm for the right atrium. Patient specific meshes were constructed using the coordinate system and phase singularity density maps from arrhythmia simulations were visualised in 2D. In conclusion, we have developed a universal atrial coordinate system allowing automatic registration of imaging and electroanatomic mapping data, 2D visualisation, and patient specific model creation.
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https://hal.archives-ouvertes.fr/hal-02885620
Contributor : Edward Vigmond <>
Submitted on : Tuesday, June 30, 2020 - 7:51:42 PM
Last modification on : Wednesday, July 1, 2020 - 11:00:25 AM

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Caroline Roney, Ali Pashaei, Marianna Meo, Rémi Dubois, Patrick Boyle, et al.. Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes. Medical Image Analysis, Elsevier, 2019, 55, pp.65-75. ⟨10.1016/j.media.2019.04.004⟩. ⟨hal-02885620⟩

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