title: Robust image-based estimation of cardiac tissue parameters and their uncertainty from noisy data.
abstract: Clinical applications of computational cardiac models require precise personalization, i.e. fitting model parameters to capture patient's physiology. However, due to parameter non-identifiability, limited data, uncertainty in the clinical measurements, and modeling assumptions, various combinations of parameter values may exist that yield the same quality of fit. Hence, there is a need for quantifying the uncertainty in estimated parameters and to ascertain the uniqueness of the found solution. This paper presents a stochastic method to estimate the parameters of an image-based electromechanical model of the heart and their uncertainty due to noise in measurements. First, Bayesian inference is applied to fully estimate the posterior probability density function (PDF) of the model. To that end, Markov Chain Monte Carlo sampling is used, which is made computationally tractable by employing a fast surrogate model based on Polynomial Chaos Expansion, instead of the true forward model. Then, we use the mean-shift algorithm to automatically find the modes of the PDF and select the most likely one while being robust to noise. The approach is used to estimate global active stress and passive stiffness from invasive pressure and image-based volume quantification. Experiments on eight patients showed that not only our approach yielded goodness of fits equivalent to a well-established deterministic method, but we could also demonstrate the non-uniqueness of the problem and report uncertainty estimates, crucial information for subsequent clinical assessments of the personalized models.
authors.forename: Dominik
authors.initials: D
authors.lastname: Neumann
authors.forename: Tommaso
authors.initials: T
authors.lastname: Mansi
authors.forename: Bogdan
authors.initials: B
authors.lastname: Georgescu
authors.forename: Ali
authors.initials: A
authors.lastname: Kamen
authors.forename: Elham
authors.initials: E
authors.lastname: Kayvanpour
authors.forename: Ali
authors.initials: A
authors.lastname: Amr
authors.forename: Farbod
authors.initials: F
authors.lastname: Sedaghat-Hamedani
authors.forename: Jan
authors.initials: J
authors.lastname: Haas
authors.forename: Hugo
authors.initials: H
authors.lastname: Katus
authors.forename: Benjamin
authors.initials: B
authors.lastname: Meder
authors.forename: Joachim
authors.initials: J
authors.lastname: Hornegger
authors.forename: Dorin
authors.initials: D
authors.lastname: Comaniciu
mesh_terms: D002311:Cardiomyopathy, Dilated / Q000150:complications / Q000175:diagnosis / Q000503:physiopathology*; D003198:Computer Simulation; D006329:Heart Conduction System / Q000473:pathology / Q000503:physiopathology*; D006352:Heart Ventricles / Q000473:pathology / Q000503:physiopathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008955:Models, Cardiovascular; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio; D013318:Stroke Volume; D018487:Ventricular Dysfunction, Left / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology*