Why the study?
Image artifact, resolution, and contrast create uncertainty in quantifying anatomy, which can lead to uncertainty in patient-specific model predictions.
A novel PCA-based algorithm efficiently quantifies how anatomical measurement uncertainty affects local activation time predictions in patient-specific left atrial electrophysiology models.
May streamline sampling in atrial models; leaves open clinical validation.
Image artefact, resolution and contrast all impact our ability to quantify anatomy. In the context of patientspecific simulations this uncertainty in shape can lead to uncertainty in model predictions. We propose and apply a method for quantifying uncertainty in shape and demonstrate the impact of uncertain shape on activation time predictions in a model of cardiac electrophysiology activation.
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Corrado et al. (2018) studied this question.
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