Quantifying Atrial Anatomy Uncertainty from Clinical Data and its Impact on Electro-physiology Simulation Predictions

TY - JOUR

T1 - Quantifying Atrial Anatomy Uncertainty from Clinical Data and its Impact on Electro-physiology Simulation Predictions

AU - Corrado, Cesare

AU - Razeghi, Orod

AU - Roney, Caroline Helen

AU - Coveney, Sam

AU - Williams, Steven Edwin

AU - Sim, Iain James Wyness

AU - O'Neill, Mark

AU - Wilkinson, Richard

AU - Oakley, Jeremy

AU - Clayton, Richard

AU - Niederer, Steven Alexander

PY - 2020/4

Y1 - 2020/4

N2 - Patient-specific computational models of structure and function are increasingly being used to diagnose disease and predict how a patient will respond to therapy. Models of anatomy are often derived after segmentation of clinical images or from mapping systems which are affected by image artefacts, resolution and contrast. Quantifying the impact of uncertain anatomy on model predictions is important, as models are increasingly used in clinical practice where decisions need to be made regardless of image quality. We use a Bayesian probabilistic approach to estimate the anatomy and to quantify the uncertainty about the shape of the left atrium derived from Cardiac Magnetic Resonance images. We show that we can quantify uncertain shape, encode uncertainty about the left atrial shape due to imaging artefacts, and quantify the effect of uncertain shape on simulations of left atrial activation times.

AB - Patient-specific computational models of structure and function are increasingly being used to diagnose disease and predict how a patient will respond to therapy. Models of anatomy are often derived after segmentation of clinical images or from mapping systems which are affected by image artefacts, resolution and contrast. Quantifying the impact of uncertain anatomy on model predictions is important, as models are increasingly used in clinical practice where decisions need to be made regardless of image quality. We use a Bayesian probabilistic approach to estimate the anatomy and to quantify the uncertainty about the shape of the left atrium derived from Cardiac Magnetic Resonance images. We show that we can quantify uncertain shape, encode uncertainty about the left atrial shape due to imaging artefacts, and quantify the effect of uncertain shape on simulations of left atrial activation times.

UR - https://www.sciencedirect.com/science/article/pii/S1361841519301628

U2 - 10.1016/j.media.2019.101626

DO - 10.1016/j.media.2019.101626

M3 - Article

SN - 1361-8415

VL - 61

JO - Medical Image Analysis

JF - Medical Image Analysis

M1 - 101626

ER -