Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate

Figures

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  Fig. 1. LAT measurement locations (dark blue circles) connected by a Delaunay triangles (white lines), with CV vectors shown as arrows. Figure reproduced from [31]

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  Fig. 2. (A) The cosine method, (B) circular wavefront and arbitrary measurement positions. Figure reproduced from [2]

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  Fig. 3. Generalized wave fitting method. A patch of mesh is projected down into 2D coordinates, and CV is calculated assuming either a planar wave or circular wave in these local coordinates. Figure reproduced from [35]

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  Fig. 4. Posterior mean of LAT from the Gaussian process model of [26]. Measurement point sizes reflect measurement uncertainty. Figure reproduced from [26]

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  Fig. 5. CV simulation ground truth (top) and posterior prediction and uncertainty, obtained with the Gaussian process model in [65] which generalized Gaussian processes to manifolds. Figured reproduced from [65]

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  Fig. 6. Predicted LAT maps and CV maps from a physics inspired neural network (PINN), using LAT observations at the locations indicated by white points. Figure reproduced from [82]

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  Fig. 7. Ground truth fibres (top left) and fitted fibres inferred with PIEMAP (top right), along with simulation LAT and LAT observations (bottom left) and fibre angle error (bottom right). Figure reproduced from [88]