Table Of ContentA R T I F I C I A L
I N T E L L I G E N C E F O R
C O M P U TAT I O N A L
M O D E L I N G O F
T H E H E A R T
A R T I F I C I A L
I N T E L L I G E N C E F O R
C O M P U TAT I O N A L
M O D E L I N G O F
T H E H E A R T
Edited by
TOMMASO MANSI
TIZIANO PASSERINI
DORIN COMANICIU
Siemens Healthineers
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Contents v
Contents
List of figures ................................................ ix
List of contributors .......................................... xvii
Foreword ................................................... xix
Preface .................................................... xxiii
List of abbreviations....................................... xxxiii
Part 1 Modeling of the beating heart: approaches and implementation
Chapter1 Multi-scale models of the heart for patient-specific
simulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
ViorelMihalef,TizianoPasserini,TommasoMansi
1.1 Models of cardiac anatomy..................................... 3
1.2 Electrophysiology modeling.................................... 6
1.2.1 Cellular electrophysiology ................................ 9
1.2.2 Tissue electrophysiology ................................ 12
1.2.3 Body surface potential modeling ......................... 14
1.3 Biomechanics modeling ...................................... 17
1.3.1 The passive myocardium ................................ 19
1.3.2 The active myocardium.................................. 22
1.3.3 The virtual heart in its environment: boundary conditions .. 25
1.4 Hemodynamics modeling..................................... 26
1.4.1 Reduced order hemodynamics ........................... 28
1.4.2 3D hemodynamics ...................................... 33
1.5 Current approaches to parameter estimation ................... 39
1.5.1 Inverse optimization..................................... 39
1.5.2 Data assimilation........................................ 40
1.5.3 Machine learning........................................ 40
1.5.4 Stochastic methods ..................................... 41
1.5.5 Streamlined whole-heart personalization.................. 41
1.6 Summary.................................................... 41
vi Contents
Chapter2 Implementation of a patient-specific cardiac model. . . . . . . . . 43
ViorelMihalef,TommasoMansi,SaikiranRapaka,TizianoPasserini
2.1 Anatomical modeling......................................... 44
2.1.1 Medical image segmentation............................. 44
2.1.2 Meshing and tagging.................................... 46
2.1.3 Computational model of the cardiac fiber architecture...... 48
2.1.4 Torso modeling ......................................... 50
2.2 Electrophysiology modeling................................... 50
2.2.1 LBM-EP: efficient solver for the monodomain problem..... 51
2.2.2 Efficient modeling of the electrical conduction system ..... 57
2.2.3 Graph-EP: fast computation of tissue activation time....... 61
2.2.4 Body surface potential modeling ......................... 63
2.3 Biomechanics modeling ...................................... 66
2.3.1 Passive stress component ............................... 68
2.3.2 Active stress component................................. 68
2.3.3 Myocardial boundary conditions ......................... 70
2.3.4 Putting it all together: a fast computational framework for
cardiac biomechanics.................................... 73
2.3.5 Evaluation of the TLED algorithm......................... 73
2.4 Hemodynamics modeling..................................... 78
2.4.1 3D hemodynamics using the lattice Boltzmann method .... 79
2.4.2 3D fluid structure interaction ............................. 82
2.5 Parameter estimation......................................... 88
2.5.1 Windkessel parameters from pressure and volume data.... 89
2.5.2 Cardiac electrophysiology ............................... 90
2.5.3 Myocardium stiffness and maximum active stress from
images ................................................. 92
2.6 Summary.................................................... 93
Part 2 Artificial intelligence methods for cardiac modeling
Chapter3 Learning cardiac anatomy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
FlorinC.Ghesu,BogdanGeorgescu,YueZhang,SasaGrbic,
DorinComaniciu
Contents vii
3.1 Introduction.................................................. 97
3.2 Parsing of cardiac and vascular structures...................... 98
3.2.1 From shallow to deep marginal space learning ............ 98
3.2.2 Intelligent agent-driven image parsing................... 105
3.2.3 Deep image-to-image segmentation ..................... 112
3.3 Structure tracking ........................................... 113
3.4 Summary................................................... 115
Chapter4 Data-driven reduction of cardiac models . . . . . . . . . . . . . . . . . 117
LucianMihaiItu,FelixMeister,PuneetSharma,TizianoPasserini
4.1 Deep-learning model for real-time, non-invasive fractional flow
reserve ..................................................... 118
4.1.1 Introduction ........................................... 118
4.1.2 Methods............................................... 120
4.1.3 Results ................................................ 128
4.1.4 Discussion............................................. 130
4.2 Meta-modeling of atrial electrophysiology..................... 136
4.2.1 Methods............................................... 139
4.2.2 Experiments and results ................................ 144
4.2.3 Discussion............................................. 153
4.3 Deep learning acceleration of biomechanics................... 154
4.3.1 Motivation............................................. 154
4.3.2 Methods............................................... 154
4.3.3 Evaluation ............................................. 156
4.4 Summary................................................... 160
Chapter5 Machine learning methods for robust parameter estimation. 161
DominikNeumann,TommasoMansi
5.1 Introduction................................................. 161
5.2 A regression approach to model parameter estimation......... 163
5.2.1 Data-driven estimation of myocardial electrical diffusivity . 163
5.2.2 Experiments and results ................................ 165
5.3 Reinforcement learning method for model parameter estimation168
5.3.1 Parameter estimation as a Markov decision process ...... 170
viii Contents
5.3.2 Parameter estimation using Reinforcement Learning...... 173
5.3.3 Application to cardiac electrophysiology ................. 174
5.3.4 Application to whole-body circulation.................... 177
5.4 Summary................................................... 180
Chapter6 Additional clinical applications . . . . . . . . . . . . . . . . . . . . . . . . . 183
FelixMeister,HeleneHoule,CosminNita,AndreiPuiu,LucianMihaiItu,
SaikiranRapaka
6.1 Cardiac resynchronization therapy............................ 183
6.1.1 Introduction ........................................... 183
6.1.2 Methods............................................... 184
6.1.3 Results ................................................ 186
6.1.4 Discussion............................................. 189
6.2 Aortic coarctation ........................................... 190
6.2.1 Introduction ........................................... 190
6.2.2 Methods............................................... 192
6.2.3 Results ................................................200
6.2.4 Discussion............................................. 201
6.3 Whole-body circulation ...................................... 203
6.3.1 Introduction ........................................... 203
6.3.2 Methods............................................... 203
6.3.3 Results and discussion ................................. 206
6.4 Summary................................................... 210
Bibliography ................................................ 211
Index....................................................... 235
Listoffigures ix
List of figures
Fig.1.1 Diagramofheartanatomyandbloodflow.(Source:Wikipedia.) 4
Fig.1.2 Examplesofheartgeometries.Leftpanel:analytical,prolate
spheroidalmodelofthetwoventricles.Rightpanel:
patient-specificmodelestimatedfrommedicalimages. 5
Fig.1.3 Exampleofheartfibermodelcomputedusingrule-based
approachonapatient-specificheartanatomy. 6
Fig.1.4 ExampleofMRIwithdelayedenhancementofgadolinium
highlightingatransmuralscar(yellowarrows(lightgrayinprint
version))intheseptumandapexoftheheart.(Source:Wikipedia.) 7
Fig.1.5 Schematicdepictionoftheelectricalconductionsystemofthe
heart.1.Sinoatrialnode,2.Atrioventricularnode,3.BundleofHis,
4.Leftbundlebranch,5.Leftposteriorfascicle,6.Leftanterior
fascicle,7.Leftventricle,8.Ventricularseptum,9.Rightventricle,
10.Rightbundlebranch.(Source:Wikipedia.) 8
Fig.1.6 Relationshipsbetweenmodelparametersandshapeoftheaction
potential.Modelparameterscanbedirectlyrelatedtoclinical
parameters. 12
Fig.1.7 Left:Schematicrepresentationoftheelectrocardiographyleads
usedin12-leadECG(source:Wikipedia).Right:Idealizedmodelof
aportionofthehumantorso,colorcodedbythesurfaceelectrical
signalandwithoverlaidlocationofECGelectrodes. 15
Fig.1.8 IllustrationofanadvancedversionoftheHill–Maxwellrheological
modelofcardiacbiomechanics. 18
Fig.1.9 Diagramillustratingthestructureofasarcomere,withthe
differentmyofilaments.(Source:Wikipedia.) 22
Fig.1.10 Diagramillustratingthedifferentstepsoftheslidingmyofilaments
mechanism.(Source:Wikipedia.) 23
Fig.1.11 Lumpedparametermodelrepresentingthewholebody
circulation.Heartsystemsareinred(darkgrayinprintversion),
systemicandpulmonarycirculationsinblue(lightgrayinprint
version). 29
Fig.1.12 Computationexamplesusingthelumpedvalvetomodelpathology
likeinsufficientandstenoticvalves.Leftpanel:LVPVloopsinthe
caseofregurgitantvalves.Blue(darkgrayinprintversion)–no
regurgitations,red(lightgrayinprintversion)–mitral
regurgitation,green(midgrayinprintversion)–aortic
regurgitation.Rightpanel:LVPVloopsforaorticstenosisof
increasingdegrees.Blue(darkgrayinprintversion)–normal,
green(midgrayinprintversion)–mild,red(grayinprintversion)–
moderate,cyan(lightgrayinprintversion)–severe.Theabscissa
unitsaremm3andtheordinateunitsarekPa. 30
Fig.1.13 Fluidstructureinteractionsystemforcardiachaemodynamics
computation.Theinteractionsbetweentheelectromechanical
model,valvesandthecomputationalfluiddynamics(CFD)model
arecontrolledbytheFSIinterfacemodule. 37
