Propagation of Myocardial Fibre Architecture Uncertainty on Electromechanical Model Parameter Estimation: A Case Study

Computer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive power. However the personalisation step to go from a generic model to a patient-specific one is still a scientific challenge. In particular it is still difficult to quantify the uncertainty on the estimated parameters and predicted values. In this manuscript we present a new pipeline to evaluate the impact of fibre uncertainty on the personalisation of an electromechanical model of the heart from ECG and medical images. We detail how we estimated the variability of the fibre architecture among a given population and how the uncertainty generated by this variability impacts the following personalisation. We first show the variability of the personalised simulations, with respect to the principal variations of the fibres. Then discussed how the variations in this (small) healthy population of fibres impact the parameters of the personalised simulations.

[1]  Hervé Delingette,et al.  A Computational Framework for the Statistical Analysis of Cardiac Diffusion Tensors: Application to a Small Database of Canine Hearts , 2007, IEEE Transactions on Medical Imaging.

[2]  Nicholas Ayache,et al.  A Fast and Log-Euclidean Polyaffine Framework for Locally Linear Registration , 2009, Journal of Mathematical Imaging and Vision.

[3]  Tom Vercauteren,et al.  Diffeomorphic demons: Efficient non-parametric image registration , 2009, NeuroImage.

[4]  Hervé Delingette,et al.  Statistical Analysis of the Human Cardiac Fiber Architecture from DT-MRI , 2011, FIMH.

[5]  L. Younes,et al.  Ex vivo 3D diffusion tensor imaging and quantification of cardiac laminar structure , 2005, Magnetic resonance in medicine.

[6]  P. Tallec,et al.  An energy-preserving muscle tissue model: formulation and compatible discretizations , 2012 .

[7]  Hervé Delingette,et al.  Efficient probabilistic model personalization integrating uncertainty on data and parameters: Application to eikonal-diffusion models in cardiac electrophysiology. , 2011, Progress in biophysics and molecular biology.

[8]  Jan Haas,et al.  Estimation of Regional Electrical Properties of the Heart from 12-Lead ECG and Images , 2014, STACOM.

[9]  Adarsh Krishnamurthy,et al.  Patient-specific models of cardiac biomechanics , 2013, J. Comput. Phys..

[10]  Hugo A. Katus,et al.  Robust Image-Based Estimation of Cardiac Tissue Parameters and Their Uncertainty from Noisy Data , 2014, MICCAI.

[11]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[12]  Hugo A. Katus,et al.  Automatic image-to-model framework for patient-specific electromechanical modeling of the heart , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[13]  Sébastien Ourselin,et al.  The estimation of patient-specific cardiac diastolic functions from clinical measurements , 2012, Medical Image Anal..

[14]  Hervé Delingette,et al.  Noname manuscript No. (will be inserted by the editor) Fast Parameter Calibration of a Cardiac Electromechanical Model from Medical Images based on the Unscented Transform , 2012 .

[15]  Dorin Comaniciu,et al.  Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach , 2013 .

[16]  Nassir Navab,et al.  Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals , 2014, Medical Image Anal..