Artificial Intelligence for Computational Modeling of the Heart

✍ Scribed by Tommaso Mansi (editor), Tiziano Passerini (editor), Dorin Comaniciu (editor)

• Publisher: Academic Press
• Year: 2019
• Tongue: English
• Leaves: 266
• Edition: 1
• Category: Library

✦ Synopsis

Artificial Intelligence for Computational Modeling of the Heart presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient’s heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications.

• Presents recent advances in computational modeling of heart function and artificial intelligence technologies for subject-specific applications
• Discusses AI-based technologies for robust anatomical modeling from medical images, data-driven reduction of multi-scale cardiac models, and estimations of physiological parameters from clinical data
• Illustrates the technology through concrete clinical applications and discusses potential impacts and next steps needed for clinical translation

✦ Table of Contents

Cover
Title
Copyright
Contents
List of figures
List of contributors
Foreword
Preface
Computational modeling of the heart: from physiology understanding to patient-specific simulations
The new age of artificial intelligence
Computational modeling meets statistical learning
Book organization
Part 1: Modeling the beating heart: approaches and implementation
Part 2: Artificial intelligence methods for cardiac modeling
Outlook
List of abbreviations
1
1 Multi-scale models of the heart for patient-specific simulations
1.1 Models of cardiac anatomy
1.2 Electrophysiology modeling
1.2.1 Cellular electrophysiology
An example: the Mitchell-Schaeffer model
1.2.2 Tissue electrophysiology
One example: a monodomain model
Modeling the electrical conduction system
1.2.3 Body surface potential modeling
Bidomain modeling of the coupled heart-body system
1.3 Biomechanics modeling
1.3.1 The passive myocardium
An overview of the Holzapfel-Ogden model
1.3.2 The active myocardium
Ionic models
Phenomenological models
Lumped models
1.3.3 The virtual heart in its environment: boundary conditions
Endocardial pressure
Attachment to neighboring vessels and tissue
1.4 Hemodynamics modeling
1.4.1 Reduced order hemodynamics
Ventricular model
Valve model
Arterial model
Atrium model
Venous circulation
1.4.2 3D hemodynamics
1.4.2.1 Modeling intra-cardiac blood flow
1.4.2.2 Fluid-structure interaction
Valve models and FSI
1.5 Current approaches to parameter estimation
1.5.1 Inverse optimization
1.5.2 Data assimilation
1.5.3 Machine learning
1.5.4 Stochastic methods
1.5.5 Streamlined whole-heart personalization
1.6 Summary
2
2 Implementation of a patient-specific cardiac model
2.1 Anatomical modeling
2.1.1 Medical image segmentation
2.1.2 Meshing and tagging
2.1.3 Computational model of the cardiac fiber architecture
2.1.4 Torso modeling
2.2 Electrophysiology modeling
2.2.1 LBM-EP: efficient solver for the monodomain problem
LBM-EP evaluation
2.2.2 Efficient modeling of the electrical conduction system
2.2.3 Graph-EP: fast computation of tissue activation time
2.2.4 Body surface potential modeling
Extracellular potentials computation
Boundary element model of torso potentials
2.2.4.1 ECG calculation
2.3 Biomechanics modeling
2.3.1 Passive stress component
2.3.2 Active stress component
2.3.3 Myocardial boundary conditions
Endocardial pressure
Attachment to atria and arteries
Modeling the effect of the pericardium bag
2.3.4 Putting it all together: a fast computational framework for cardiac biomechanics
Description of the TLED finite elements algorithm
2.3.5 Evaluation of the TLED algorithm
Validation against analytical solution
Numerical stability analysis
Bi-ventricular simulation
2.4 Hemodynamics modeling
2.4.1 3D hemodynamics using the lattice Boltzmann method
The Lattice Boltzmann Method
Turbulence modeling
2.4.2 3D fluid structure interaction
Preparatory step
Step 1
Step 2
Step 3
Step 4
Tests of the FSI module
Output of the FSI system with patient-specific data
2.5 Parameter estimation
2.5.1 Windkessel parameters from pressure and volume data
2.5.2 Cardiac electrophysiology
2.5.3 Myocardium stiffness and maximum active stress from images
2.6 Summary
3
3 Learning cardiac anatomy
3.1 Introduction
3.2 Parsing of cardiac and vascular structures
3.2.1 From shallow to deep marginal space learning
3.2.1.1 Problem formulation
3.2.1.2 Traditional feature engineering
3.2.1.3 Sparse adaptive deep neural networks
3.2.1.4 Marginal space deep learning
3.2.1.5 Nonrigid parametric deformation estimation
3.2.1.6 Experiments
3.2.2 Intelligent agent-driven image parsing
3.2.2.1 Learning to search for anatomical objects
3.2.2.2 Extending to multi-scale search
3.2.2.3 Learning multi-scale navigation strategies
3.2.2.4 Robust spatially-coherent landmark detection
3.2.2.5 Experiments
3.2.3 Deep image-to-image segmentation
3.3 Structure tracking
3.4 Summary
4
4 Data-driven reduction of cardiac models
4.1 Deep-learning model for real-time, non-invasive fractional flow reserve
4.1.1 Introduction
4.1.2 Methods
4.1.2.1 Generating synthetic coronary arterial trees
4.1.2.2 CFD-based hemodynamic computations
4.1.2.3 Machine-learning based FFR computation
4.1.2.4 Local features
4.1.2.5 Features defined based on the proximal and distal vasculature
4.1.3 Results
4.1.3.1 Validation on synthetic anatomical models
4.1.3.2 Validation on patient specific anatomical models
4.1.3.3 Validation against invasively measured FFR
4.1.4 Discussion
4.1.4.1 Use of synthetic data
4.1.4.2 Limitations
4.2 Meta-modeling of atrial electrophysiology
4.2.1 Methods
4.2.1.1 Atrial electrophysiology models
4.2.1.2 Learning the action potential manifold for dimensionality reduction
4.2.1.3 Statistical learning
4.2.1.4 Application to tissue-level cardiac EP modeling
4.2.2 Experiments and results
4.2.2.1 Model parameter selection and sampling
4.2.2.2 PCA versus LLE
4.2.2.3 Physical regression model construction
4.2.2.4 Application in tissue-level EP modeling
4.2.3 Discussion
4.3 Deep learning acceleration of biomechanics
4.3.1 Motivation
4.3.2 Methods
4.3.3 Evaluation
4.3.3.1 Discussion
4.4 Summary
5
5 Machine learning methods for robust parameter estimation
5.1 Introduction
5.2 A regression approach to model parameter estimation
5.2.1 Data-driven estimation of myocardial electrical diffusivity
5.2.2 Experiments and results
5.2.2.1 Setup and uncertainty analysis
5.2.2.2 Verification on synthetic data
5.2.2.3 Evaluation on patient data
5.3 Reinforcement learning method for model parameter estimation
5.3.1 Parameter estimation as a Markov decision process
5.3.1.1 Reformulation of model personalization into an MDP
5.3.1.2 Learning model behavior through exploration
5.3.1.3 From computed objectives to representative MDP state
5.3.1.4 Transition function as probabilistic model representation
5.3.2 Parameter estimation using Reinforcement Learning
5.3.2.1 Data-driven initialization
5.3.2.2 Probabilistic personalization
5.3.3 Application to cardiac electrophysiology
5.3.4 Application to whole-body circulation
5.4 Summary
6
6 Additional clinical applications
6.1 Cardiac resynchronization therapy
6.1.1 Introduction
6.1.2 Methods
6.1.2.1 Data acquisition
6.1.2.2 Computational modeling
6.1.3 Results
6.1.3.1 Electrophysiological results
6.1.4 Discussion
6.2 Aortic coarctation
6.2.1 Introduction
6.2.2 Methods
6.2.2.1 Generation of a synthetic training database
6.2.2.2 Three-dimensional flow computations
6.2.2.3 Pressure drop model for aortic coarctation
6.2.3 Results
6.2.3.1 Evaluation of the pressure drop model
6.2.4 Discussion
6.3 Whole-body circulation
6.3.1 Introduction
6.3.2 Methods
6.3.2.1 Traditional personalization framework
6.3.2.2 Deep learning based personalization
6.3.3 Results and discussion
6.4 Summary
7
Bibliography
Index
Index
Back Cover