Modeling left-ventricle mechanics

Transcription

1 Modeling left-ventricle mechanics Towards automatic construction of population-based simulations Paolo Di Achille 1, J. Jeremy Rice 1, Colin B. Compas 2, Viatcheslav Gurev 1, 1 Healthcare and Life Sciences Research, IBM TJ Watson Research Center 2 IBM Research Almaden

2 Simulation of heart mechanics Myocardium is incompressible, anisotropic and contracts actively, requiring special numerical treatment 4 Gurev, Pathmanathan, Fattebert, Wen, Magerlein, Gray, Richards, Rice BMMB (2015)

3 High-resolution models of mechanics model of canine ventricles model of human left ventricle segmentation by S. Niederer and R. Rajani 5

4 Databases of cardiac MRI images MESA (asymptomatic patients) DETERMINE (myocardial infarct) SUNNYBROOK (segmentation challenge) Need to build capabilities for modeling tools to interface with and be informed by large (but less than ideal ) datasets of patient populations 8

5 Goals Construct accurate models of cardiac mechanics based on imaging datasets of patients Develop automatic pipeline to interface databases of cardiac MRI to our mechanical modeling capabilities Build machine learning models of passive and active mechanics trained on 3D models, ideal for solving inverse problems and estimating cardiac indices

6 Estimation of unloaded configuration One of the obstacles to generate patient-specific models from images is the unknown stress-free configuration (VV OO ) Common assumption is to assume VV OO VV AA. Difference could be large especially in pathologies with low ejection fraction

7 Methods to find unloaded configuration Fixed point iteration (solving inverse problem) Iterative method (Sullier 2011, Bols et al. 2013) progressive correction of reference configuration Particularly effective for isotropic materials need to redefine fibers if anisotropy Ideal for single patient geometry when internal pressure is known geometry directly from images Machine-learning based on Kriging (solving forward problem + regression) Non-iterative method computational expense made upfront, then very fast Equally suitable for isotropic and anisotropic materials Ideal when dealing with many geometries or when pressure can be only estimated parameterized geometry based on images

8 Parameterization of LV geometry ξ = {RR bb, LL, ZZ, HH, ee, Ψ 0 } Pravdin et al. 2014

9 Parameterization of LV geometry ξ = {RR bb, LL, ZZ, HH, ee, Ψ 0 } Sphericity ee Pravdin et al. 2014

10 Training dataset: Sunnybrook Cardiac Database Presented as segmentation challenge (Radau et al. 2009) 45 cardiac MRI datasets 20 snapshots per cycle multiple views 4 groups of patients N: normal HYP: hypertrophic HF-I & HF-NI: heart failure w/ and w/out infarct Manual segmentation from challenge 16

11 Automatic processing of cardiac MRI MRI MRI+segmentation Parameterization Long Axis Short Axis

12 6-parameter model of LV geometry and motion ξ = {RR bb, LL, ZZ, HH, ee, Ψ 0 } Top View Overlap JJ CC = CC ξ CC MMMM CC ξ CC MMMM Lateral View JJ WW = WW ξ WW MMMM WW ξ WW MMMM C LVcavity W LV wall

13 Average motion by category N HF-NI HYP HF-I

14 Generation of training dataset for Kriging (1) latin hypercube sampling FEM model LV inflation

15 Generation of training dataset for Kriging (1) parameterization of simulation results FEM model LV inflation

16 Generation of training datasets for Kriging (2) - Database - Unloaded (V O ) - Simulation We use simulation results to optimize gaussian process regressions (Kriging) for each of the geometric parameters After training, any configuration can be retrieved ( interpolated ) in seconds

17 Criteria for selecting end-diastolic pressure Targeting sarcomere length at end-diastole (e.g. 10% stretch for normal heart, 20% dilated geometries) Solving optimization problem to obtain bestfit of LV geometries during filling phase

18 Identification of LV behavior during filling Complex optimization problem, to find unloaded configuration that best matches geometries during filling observed in images provides estimation of unloaded configuration and filling pressures Idealized PV loop Predicted MRI

19 Simulation of cardiac cycle on different geometries Reduced model of LV mechanics trained on data from 3D models Simulation of contractile index (ESPVR) for representative normal and hypertrophic ventricles Framework suitable for integrating imaging and blood pressure measurements for realistic predictions of ESPVR normal hypertrophic

20 Conclusions Increasing availability of databases of medical images motivates building new tools to interface and inform mechanical models with datasets of large patient populations automatic pipeline to process and parameterize ventricular shape from large number of images Statistical models of passive mechanics trained on 3D simulations are valuable for solving inverse problems alternative method based on Kriging to estimate unloaded configurations, ideal when LV pressure is unknown (estimation from matching image snapshots during filling) Machine learning-based models of active mechanics provide framework to estimate cardiac contractility indices

21 Acknowledgments Dr. Jaimit Parikh Cardioid Team, IBM Research Dr. Ahmed El-Harouni Medical Sieve, IBM Almaden Cardiac Atlas Project Sunnybrook Cardiac Database Healthcare and Life Sciences Research IBM TJ Watson Res. Cen.