Page 263 - Artificial Intelligence for Computational Modeling of the Heart

Index 237

modeling, 6, 50 motion, 159 L
models, 17, 24, 89 QRS duration, 166 Lattice Boltzmann Method
parameters, 89 (LBM), 79, 196
simulations, 139 H Learning
ventricular, 12, 33 Handcrafted features, 99, 141, deep, 97, 106, 114, 115, 117,
Endocardial 167 118, 133–135, 154, 157, 160
pressure, 67 Heart environment, 107
surface, 9, 18, 25, 58, 59, 82, 86 anatomy, 43, 185 models, 115, 171
surface normal, 71 beat, 24, 26, 89, 143, 144, 186 problem, 99
Endocardium, 5, 9, 14, 25, 48, 49, chambers, 27, 78, 97, 104, 116 process, 176
57–59, 86 conduction system, 14 strategy, 100
Epicardial cycle, 36, 93, 206 Left bundle branch block
motion, 26 diseases, 136 (LBBB), 164, 184, 185
potentials, 16, 162 function, 3, 25, 26, 28, 41 Left ventricular (LV), 184, 203
Epicardium, 5, 9, 45, 48, 64, 65 isolated, 13 endocardium, 90
cardiac, 71 model, 97, 203 Localized fibrosis, 24
surfaces, 49 motion, 40, 84 Locally linear embedding (LLE),
Extracellular potentials, 63, 64, physiology, 3 139, 140
66 rate, 89, 138, 185, 206 Lumped
segmentation, 112 models, 22, 24, 32
tissue, 14 parameter, 123, 203
F
Features, anatomical, 117, 118, ventricles, 26 resistance, 31
120, 133, 134 Heart failure (HF), 183, 190 valve, 29
Hemodynamics, 3, 25, 28, 33, 34,
Fiber, 5, 9, 19, 21, 48, 62 41, 43, 78, 79, 92 M
direction, 13, 20, 48, 62, 69 atrial, 43 Machine learning (ML), 39, 40,
sheets, 19, 21, 48 cardiac, 78, 117 120, 130, 150, 162, 180, 191,
Fibrosis, 6, 47, 91 coronary, 125–127, 133, 134, 192, 201, 202
Finite difference method (FDM), 154 models, 120, 125, 134, 135
51
diseases, 26 Magnetic resonance imaging
Finite element method (FEM),
global, 28 (MRI), 5, 165, 191
18, 51, 67, 154, 191
local, 28 Magnetic resonance (MR), 97
Flow rate, 27, 28, 36, 71, 86, 87,
modeling, 26, 78 Manifold learning techniques,
123, 127, 135, 197–199, 205
models, 18 138, 139, 146, 147
aortic, 36, 204, 205
parameters, 92 Marginal space deep learning
inlet, 87
ventricular, 26 (MSDL), 97, 98, 101, 103,
outlet, 87
Holzapfel–Ogden (HO) model, 105
Fluid Structure Interaction (FSI), 19–21 Marginal space learning (MSL),
38, 82 45, 97, 98, 101
Forward model, 161, 164, 165, I Markov Decision Process
167, 171, 173, 175, 176, 181 Inlet (MDP), 106, 170
Fractional Flow Reserve (FFR), area, 198, 199 Mechanistic models, 138, 139
119 flow rates, 197 Medical images, 43, 44, 46, 115,
invasive, 120, 130, 133, 135 Reynolds number, 197 117, 119, 135, 169, 190, 202
Intracardiac flow, 43 Mitral
G Intracardiac pressure, 27 annulus, 45
Ground truth Invasive coronary angiography outlet, 83
action potential profiles, 150 (ICA), 119 valve, 4, 29, 33, 38, 39
diffusivity coefficients, 168 Ionic models, 10 valve models, 38
FFR values, 120, 134 Ischemic weight, 126, 127 vortex, 87