Ventricular arrhythmias are the primary cause of sudden cardiac death and one of the leading causes of mortality worldwide. Whole-heart computational modeling offers a unique approach for studying ventricular arrhythmias, offering vast potential for developing both a mechanistic understanding of ventricular arrhythmias and clinical applications for treatment. In this review, the fundamentals of whole-heart ventricular modeling and current methods of personalizing models using clinical data are presented. From this foundation, the authors summarize recent advances in whole-heart ventricular arrhythmia modeling. Efforts in gaining mechanistic insights into ventricular arrhythmias are discussed, in addition to other applications of models such as the assessment of novel therapeutics. The review emphasizes the unique benefits of computational modeling that allow for insights that are not obtainable by contemporary experimental or clinical means. Additionally, the clinical impact of modeling is explored, demonstrating how patient care is influenced by the information gained from ventricular arrhythmia models. The authors conclude with future perspectives about the direction of whole-heart ventricular arrhythmia modeling, outlining how advances in neural network methodologies hold the potential to reduce computational expense and permit for efficient whole-heart modeling.

1.
S. M.
Narayan
,
P. J.
Wang
, and
J. P.
Daubert
, “
New concepts in sudden cardiac arrest to address an intractable epidemic: JACC state-of-the-art review
,”
J. Am. Coll. Cardiol.
73
(
1
),
70
88
(
2019
).
2.
A. L.
Hodgkin
and
A. F.
Huxley
, “
A quantitative description of membrane current and its application to conduction and excitation in nerve
,”
J. Physiol.
117
(
4
),
500
544
(
1952
).
3.
K. H. W. J.
ten Tusscher
and
A. V.
Panfilov
, “
Alternans and spiral breakup in a human ventricular tissue model
,”
Am. J. Physiol.—Heart Circ. Physiol.
291
(
3
),
H1088
(
2006
).
4.
T.
O'Hara
,
L.
Virág
,
A.
Varró
, and
Y.
Rudy
, “
Simulation of the undiseased human cardiac ventricular action potential: Model formulation and experimental validation
,”
PLoS Comput. Biol.
7
(
5
),
e1002061
(
2011
).
5.
E. J.
Vigmond
,
F.
Aguel
, and
N. A.
Trayanova
, “
Computational techniques for solving the bidomain equations in three dimensions
,”
IEEE Trans. Biomed. Eng.
49
(
11
),
1260
1269
(
2002
).
6.
M. J.
Bishop
and
G.
Plank
, “
Representing cardiac bidomain bath-loading effects by an augmented monodomain approach: Application to complex ventricular models
,”
IEEE Trans. Biomed. Eng.
58
(
4
),
1066
1075
(
2011
).
7.
E. J.
Vigmond
and
C.
Clements
, “
Construction of a computer model to investigate sawtooth effects in the Purkinje system
,”
IEEE Trans. Biomed. Eng.
54
(
3
),
389
399
(
2007
).
8.
S.
Mahida
,
F.
Sacher
,
R.
Dubois
 et al, “
Cardiac imaging in patients with ventricular tachycardia
,”
Circulation
136
(
25
),
2491
2507
(
2017
).
9.
A.
Schmidt
,
C. F.
Azevedo
 et al, “
Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia susceptibility in patients with left ventricular dysfunction
,”
Circulation
115
,
2006
(
2007
).
10.
D. R.
Okada
,
J.
Miller
,
J.
Chrispin
 et al, “
Substrate spatial complexity analysis for the prediction of ventricular arrhythmias in patients with ischemic cardiomyopathy
,”
Circ.: Arrhythmia Electrophysiol.
13
(
4
),
281
290
(
2020
).
11.
M.
Disertori
,
M.
Rigoni
,
N.
Pace
 et al, “
Myocardial fibrosis assessment by LGE is a powerful predictor of ventricular tachyarrhythmias in ischemic and nonischemic LV dysfunction: A meta-analysis
,”
JACC: Cardiovasc. Imaging
9
,
1046
(
2016
).
12.
A.
Zegard
,
O.
Okafor
,
J.
de Bono
 et al, “
Myocardial fibrosis as a predictor of sudden death in patients with coronary artery disease
,”
J. Am. Coll. Cardiol.
77
(
1
),
29
41
(
2021
).
13.
I.
Roca-Luque
,
A.
van Breukelen
,
F.
Alarcon
 et al, “
Ventricular scar channel entrances identified by new wideband cardiac magnetic resonance sequence to guide ventricular tachycardia ablation in patients with cardiac defibrillators
,”
EP Europace
22
(
4
),
598
606
(
2020
).
14.
K. C.
Wu
,
R. G.
Weiss
,
D. R.
Thiemann
 et al, “
Late gadolinium enhancement by cardiovascular magnetic resonance heralds an adverse prognosis in nonischemic cardiomyopathy
,”
J. Am. Coll. Cardiol.
51
(
25
),
2414
2421
(
2008
).
15.
M. A. J.
Becker
,
J. H.
Cornel
,
P. M.
van de Ven
,
A. C.
van Rossum
,
C. P.
Allaart
, and
T.
Germans
, “
The prognostic value of late gadolinium-enhanced cardiac magnetic resonance imaging in nonischemic dilated cardiomyopathy: A review and meta-analysis
,”
JACC: Cardiovasc. Imaging
11
(
9
),
1274
1284
(
2018
).
16.
D.
Muser
,
G.
Nucifora
,
S. A.
Castro
 et al, “
Myocardial substrate characterization by CMR T1 mapping in patients with NICM and no LGE undergoing catheter ablation of VT
,”
JACC: Clin. Electrophysiol.
7
,
831
(
2021
).
17.
H. J.
Chang
,
R. T.
George
,
K. H.
Schuleri
 et al, “
Prospective electrocardiogram-gated delayed enhanced multidetector computed tomography accurately quantifies infarct size and reduces radiation exposure
,”
JACC: Cardiovasc. Imaging
2
(
4
),
412
420
(
2009
).
18.
S.
Yamashita
,
F.
Sacher
,
D. A.
Hooks
 et al, “
Myocardial wall thinning predicts transmural substrate in patients with scar-related ventricular tachycardia
,”
Heart Rhythm
14
(
2
),
155
163
(
2017
).
19.
Y.
Komatsu
,
H.
Cochet
,
A.
Jadidi
 et al, “
Regional myocardial wall thinning at multidetector computed tomography correlates to arrhythmogenic substrate in postinfarction ventricular tachycardia: Assessment of structural and electrical substrate
,”
Circ.: Arrhythmia Electrophysiol.
6
(
2
),
342
350
(
2013
).
20.
M.
Takigawa
,
J.
Duchateau
,
F.
Sacher
 et al, “
Are wall thickness channels defined by computed tomography predictive of isthmuses of postinfarction ventricular tachycardia?
,”
Heart Rhythm
16
,
1661
(
2019
).
21.
M.
Takigawa
,
R.
Martin
,
G.
Cheniti
 et al, “
Detailed comparison between the wall thickness and voltages in chronic myocardial infarction
,”
J. Cardiovasc. Electrophysiol.
30
(
2
),
195
204
(
2019
).
22.
A. R.
Raney
,
F.
Saremi
,
S.
Kenchaiah
 et al, “
Multidetector computed tomography shows intramyocardial fat deposition
,”
J. Cardiovasc. Comput. Tomogr.
2
(
3
),
152
163
(
2008
).
23.
M.
Aliyari Ghasabeh
,
A. S. J. M.
te Riele
,
C. A.
James
 et al, “
Epicardial fat distribution assessed with cardiac CT in arrhythmogenic right ventricular dysplasia/cardiomyopathy
,”
Radiology
289
(
3
),
641
648
(
2018
).
24.
T.
Sasaki
,
H.
Calkins
,
C. F.
Miller
 et al, “
New insight into scar-related ventricular tachycardia circuits in ischemic cardiomyopathy: Fat deposition after myocardial infarction on computed tomography—A pilot study
,”
Heart Rhythm
12
(
7
),
1508
1518
(
2015
).
25.
G.
Cheniti
,
S.
Sridi
,
F.
Sacher
 et al, “
Post-myocardial infarction scar with fat deposition shows specific electrophysiological properties and worse outcome after ventricular tachycardia ablation
,”
J. Am. Heart Assoc.
8
(
15
),
e012482
(
2019
).
26.
K.
Kalisz
,
J.
Buethe
,
S. S.
Saboo
,
S.
Abbara
,
S.
Halliburton
, and
P.
Rajiah
, “
Artifacts at cardiac CT: Physics and solutions
,”
RadioGraphics
36
(
7
),
2064
2083
(
2016
).
27.
C. T.
Villongco
,
D. E.
Krummen
,
P.
Stark
,
J. H.
Omens
, and
A. D.
McCulloch
, “
Patient-specific modeling of ventricular activation pattern using surface ECG-derived vectorcardiogram in bundle branch block
,”
Prog. Biophys. Mol. Biol.
115
(
2-3
),
305
313
(
2014
).
28.
S.
Pezzuto
,
F. W.
Prinzen
,
M.
Potse
 et al, “
Reconstruction of three-dimensional biventricular activation based on the 12-lead electrocardiogram via patient-specific modelling
,”
EP Europace
23
(
4
),
640
647
(
2021
).
29.
K.
Gillette
,
M. A. F.
Gsell
,
A. J.
Prassl
 et al, “
A framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs
,”
Med. Image Anal.
71
,
102080
(
2021
).
30.
K.
Ushenin
,
V.
Kalinin
,
S.
Gitinova
,
O.
Sopov
, and
O.
Solovyova
, “
Parameter variations in personalized electrophysiological models of human heart ventricles
,”
PLoS One
16
(
4
),
e0249062
(
2021
).
31.
T.
Grandits
,
A.
Effland
,
T.
Pock
,
R.
Krause
,
G.
Plank
, and
S.
Pezzuto
, “
GEASI: Geodesic-based earliest activation sites identification in cardiac models
,”
Int. J. Numer. Methods Biomed. Eng.
37
,
e3505
(
2021
).
32.
Y.
Rudy
, “
Noninvasive mapping of repolarization with electrocardiographic imaging
,”
J. Am. Heart Assoc.
10
(
9
),
e021396
(
2021
).
33.
Y.
Rudy
, “
Noninvasive ECG imaging (ECGI): Mapping the arrhythmic substrate of the human heart
,”
Int. J. Cardiol.
237
,
13
14
(
2017
).
34.
S.
Giffard-Roisin
,
H.
Delingette
,
T.
Jackson
 et al, “
Transfer learning from simulations on a reference anatomy for ECGI in personalized cardiac resynchronization therapy
,”
IEEE Trans. Biomed. Eng.
66
(
2
),
343
353
(
2019
).
35.
S.
Giffard-Roisin
,
T.
Jackson
,
L.
Fovargue
 et al, “
Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping
,”
IEEE Trans. Biomed. Eng.
64
(
9
),
2206
2218
(
2017
).
36.
C.
Sánchez
,
G.
D'Ambrosio
,
F.
Maffessanti
 et al, “
Sensitivity analysis of ventricular activation and electrocardiogram in tailored models of heart-failure patients
,”
Med. Biol. Eng. Comput.
56
(
3
),
491
504
(
2018
).
37.
M.
Potse
,
D.
Krause
,
W.
Kroon
 et al, “
Patient-specific modelling of cardiac electrophysiology in heart-failure patients
,”
Europace
16
(
Suppl 4
),
iv56
iv61
(
2014
).
38.
J. M.
Lubrecht
,
T.
Grandits
,
A.
Gharaviri
 et al, “
Automatic reconstruction of the left atrium activation from sparse intracardiac contact recordings by inverse estimate of fibre structure and anisotropic conduction in a patient-specific model
,”
Europace
23
(
Supplement_1
),
I63
I70
(
2021
).
39.
K. N.
Aronis
,
A.
Prakosa
,
T.
Bergamaschi
 et al, “
Characterization of the electrophysiologic remodeling of patients with ischemic cardiomyopathy by clinical measurements and computer simulations coupled with machine learning
,”
Front. Physiol.
12
,
1079
(
2021
).
40.
E.
Sung
,
A.
Prakosa
,
K. N.
Aronis
 et al, “
Personalized digital-heart technology for ventricular tachycardia ablation targeting in hearts with infiltrating adiposity
,”
Circ.: Arrhythmia Electrophysiol.
13
,
e008912
(
2020
).
41.
A.
Prakosa
,
H. J.
Arevalo
,
D.
Deng
 et al, “
Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia
,”
Nat. Biomed. Eng.
2
(
10
),
732
740
(
2018
).
42.
H. J.
Arevalo
,
F.
Vadakkumpadan
,
E.
Guallar
 et al, “
Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models
,”
Nat. Commun.
7
,
11437
(
2016
).
43.
D.
Deng
,
A.
Prakosa
,
J.
Shade
,
P.
Nikolov
, and
N. A.
Trayanova
, “
Sensitivity of ablation targets prediction to electrophysiological parameter variability in image-based computational models of ventricular tachycardia in post-infarction patients
,”
Front. Physiol.
10
,
628
(
2019
).
44.
J. M. T.
de Bakker
,
F. J. L.
van Capelle
,
M. J.
Janse
 et al, “
Reentry as a cause of ventricular tachycardia in patients with chronic ischemic heart disease: Electrophysiology and anatomic correlation
,”
Circulation
77
(
3
),
589
606
(
1988
).
45.
J. D.
Bayer
,
G. G.
Lalani
,
E. J.
Vigmond
,
S. M.
Narayan
, and
N. A.
Trayanova
, “
Mechanisms linking electrical alternans and clinical ventricular arrhythmia in human heart failure
,”
Heart Rhythm
13
(
9
),
1922
1931
(
2016
).
46.
F. O.
Campos
,
Y.
Shiferaw
,
R.
Weber dos Santos
,
G.
Plank
, and
M. J.
Bishop
, “
Microscopic isthmuses and fibrosis within the border zone of infarcted hearts promote calcium-mediated ectopy and conduction block
,”
Front. Phys.
6
,
57
(
2018
).
47.
F. O.
Campos
,
J.
Whitaker
,
R.
Neji
 et al, “
Factors Promoting conduction slowing as substrates for block and reentry in infarcted hearts
,”
Biophys. J.
117
(
12
),
2361
2374
(
2019
).
48.
M.
Deo
,
P. M.
Boyle
,
A. M.
Kim
, and
E. J.
Vigmond
, “
Arrhythmogenesis by single ectopic beats originating in the Purkinje system
,”
Am. J. Physiol.: Heart Circ. Physiol.
299
(
4
),
1002
1011
(
2010
).
49.
M.
Deo
,
P.
Boyle
,
G.
Plank
, and
E.
Vigmond
, “
Arrhythmogenic mechanisms of the Purkinje system during electric shocks: A modeling study
,”
Heart Rhythm
6
(
12
),
1782
1789
(
2009
).
50.
F.
Pashakhanloo
,
D. A.
Herzka
,
H.
Halperin
,
E. R.
McVeigh
, and
N. A.
Trayanova
, “
Role of 3-dimensional architecture of scar and surviving tissue in ventricular tachycardia: Insights from high-resolution ex vivo porcine models
,”
Circ.: Arrhythmia Electrophysiol.
11
(
6
),
e006131
(
2018
).
51.
D.
Deng
,
A.
Prakosa
,
J.
Shade
,
P.
Nikolov
, and
N. A.
Trayanova
, “
Characterizing conduction channels in postinfarction patients using a personalized virtual heart
,”
Biophys. J.
117
,
2287
(
2019
).
52.
H.
Arevalo
,
G.
Plank
,
P.
Helm
,
H.
Halperin
, and
N.
Trayanova
, “
Tachycardia in post-infarction hearts: Insights from 3D image-based ventricular models
,”
PLoS One
8
,
e68872
(
2013
).
53.
J.
Ringenberg
,
M.
Deo
,
D.
Filgueiras-Rama
 et al, “
Effects of fibrosis morphology on reentrant ventricular tachycardia inducibility and simulation fidelity in patient-derived models
,”
Clin. Med. Insights: Cardiol.
8
(
Suppl 1
),
1
13
(
2014
).
54.
C.
Liang
,
K.
Wang
,
Q.
Li
,
J.
Bai
, and
H.
Zhang
, “
Influence of the distribution of fibrosis within an area of myocardial infarction on wave propagation in ventricular tissue
,”
Sci. Rep.
9
(
1
),
14151
(
2019
).
55.
P.
Colli-Franzone
,
V.
Gionti
,
L. F.
Pavarino
,
S.
Scacchi
, and
C.
Storti
, “
Role of infarct scar dimensions, border zone repolarization properties and anisotropy in the origin and maintenance of cardiac reentry
,”
Math. Biosci.
315
,
108228
(
2019
).
56.
G.
Balaban
,
C. M.
Costa
,
B.
Porter
 et al, “
3D electrophysiological modeling of interstitial fibrosis networks and their role in ventricular arrhythmias in non-ischemic cardiomyopathy
,”
IEEE Trans. Biomed. Eng.
67
(
11
),
3125
3133
(
2020
).
57.
H.
Martinez-Navarro
,
X.
Zhou
,
A.
Bueno-Orovio
, and
B.
Rodriguez
, “
Electrophysiological and anatomical factors determine arrhythmic risk in acute myocardial ischaemia and its modulation by sodium current availability
,”
Interface Focus
11
(
1
),
20190124
(
2021
).
58.
H.
Martinez-Navarro
,
A.
Mincholé
,
A.
Bueno-Orovio
, and
B.
Rodriguez
, “
High arrhythmic risk in antero-septal acute myocardial ischemia is explained by increased transmural reentry occurrence
,”
Sci. Rep.
9
(
1
),
16803
(
2019
).
59.
Y.
Zheng
,
D.
Wei
,
X.
Zhu
,
W.
Chen
,
K.
Fukuda
, and
H.
Shimokawa
, “
Ventricular fibrillation mechanisms and cardiac restitutions: An investigation by simulation study on whole-heart model
,”
Comput. Biol. Med.
63
,
261
268
(
2015
).
60.
N.
Vandersickel
,
M.
Watanabe
,
Q.
Tao
,
J.
Fostier
,
K.
Zeppenfeld
, and
A. v
Panfilov
, “
Dynamical anchoring of distant arrhythmia sources by fibrotic regions via restructuring of the activation pattern
,”
PLoS Comput. Biol.
14
(
12
),
e1006637
(
2018
).
61.
N.
Vandersickel
,
T. P.
de Boer
,
M. A.
Vos
, and
A. v
Panfilov
, “
Perpetuation of torsade de pointes in heterogeneous hearts: Competing foci or re-entry?
,”
J. Physiol.
594
(
23
),
6865
6878
(
2016
).
62.
M. R.
Rivaud
,
J. D.
Bayer
,
M.
Cluitmans
 et al, “
Critical repolarization gradients determine the induction of reentry-based torsades de pointes arrhythmia in models of long QT syndrome
,”
Heart Rhythm
18
(
2
),
278
287
(
2021
).
63.
J. K.
Yu
,
W.
Franceschi
,
Q.
Huang
,
F.
Pashakhanloo
,
P. M.
Boyle
, and
N. A.
Trayanova
, “
A comprehensive, multiscale framework for evaluation of arrhythmias arising from cell therapy in the whole post-myocardial infarcted heart
,”
Sci. Rep.
9
(
1
),
9238
(
2019
).
64.
J. K.
Yu
,
J. A.
Liang
,
W. H.
Franceschi
 et al, “
Assessment of arrhythmia mechanism and burden of the infarcted ventricles following remuscularization with pluripotent stem cell-derived cardiomyocyte patches using patient-derived models
,”
Cardiovasc. Res.
(published online) (
2021
).
65.
P. M.
Boyle
,
T. V.
Karathanos
, and
N. A.
Trayanova
, “
Cardiac Optogenetics 2018
,”
JACC: Clin. Electrophysiol.
4
(
2
),
155
167
(
2018
).
66.
P. M.
Boyle
,
T. V.
Karathanos
,
E.
Entcheva
, and
N. A.
Trayanova
, “
Computational modeling of cardiac optogenetics: Methodology overview & review of findings from simulations
,”
Comput. Biol. Med.
65
,
200
208
(
2015
).
67.
T. V.
Karathanos
,
J. D.
Bayer
,
D.
Wang
,
P. M.
Boyle
, and
N. A.
Trayanova
, “
Opsin spectral sensitivity determines the effectiveness of optogenetic termination of ventricular fibrillation in the human heart: A simulation study
,”
J. Physiol.
594
(
23
),
6879
6891
(
2016
).
68.
E.
Ukwatta
,
H.
Arevalo
,
M.
Rajchl
 et al, “
Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology
,”
Med. Phys.
42
(
8
),
4579
4590
(
2015
).
69.
E.
Ukwatta
,
P.
Nikolov
,
F.
Zabihollahy
,
N. A.
Trayanova
, and
G. A.
Wright
, “
Virtual electrophysiological study as a tool for evaluating efficacy of MRI techniques in predicting adverse arrhythmic events in ischemic patients
,”
Phys. Med. Biol.
63
(
22
),
225008
(
2018
).
70.
D.
Deng
,
H. J.
Arevalo
,
A.
Prakosa
,
D. J.
Callans
, and
N. A.
Trayanova
, “
A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction
,”
Europace
18
(
suppl_4
),
iv60
iv66
(
2016
).
71.
M. J.
Cartoski
,
P. P.
Nikolov
,
A.
Prakosa
,
P. M.
Boyle
,
P. J.
Spevak
, and
N. A.
Trayanova
, “
Computational identification of ventricular arrhythmia risk in pediatric myocarditis
,”
Pediatr. Cardiol.
40
(
4
),
857
864
(
2019
).
72.
J. K.
Shade
,
M. J.
Cartoski
,
P.
Nikolov
 et al, “
Ventricular arrhythmia risk prediction in repaired tetralogy of Fallot using personalized computational cardiac models
,”
Heart Rhythm
17
(
3
),
408
414
(
2020
).
73.
A.
Lyon
,
A.
Mincholé
,
A.
Bueno-Orovio
, and
B.
Rodriguez
, “
Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study
,”
Morphologie
103
(
343
),
169
179
(
2019
).
74.
J. K.
Shade
,
R. L.
Ali
,
D.
Basile
 et al, “
Preprocedure application of machine learning and mechanistic simulations predicts likelihood of paroxysmal atrial fibrillation recurrence following pulmonary vein isolation
,”
Circ.: Arrhythmia Electrophysiol.
13
(
7
),
617
627
(
2020
).
75.
H.
Ashikaga
,
H.
Arevalo
,
F.
Vadakkumpadan
 et al, “
Feasibility of image-based simulation to estimate ablation target in human ventricular arrhythmia
,”
Heart Rhythm
10
(
8
),
1109
1116
(
2013
).
76.
A.
Prakosa
,
M. K.
Southworth
,
J. N.
Avari Silva
,
J. R.
Silva
, and
N. A.
Trayanova
, “
Impact of augmented-reality improvement in ablation catheter navigation as assessed by virtual-heart simulations of ventricular tachycardia ablation
,”
Comput. Biol. Med.
133
,
104366
(
2021
).
77.
Z.
Chen
,
R.
Cabrera-Lozoya
,
J.
Relan
 et al, “
Biophysical modeling predicts ventricular tachycardia inducibility and circuit morphology: A combined clinical validation and computer modeling approach
,”
J. Cardiovasc. Electrophysiol.
27
(
7
),
851
860
(
2016
).
78.
A.
Lopez-Perez
,
R.
Sebastian
,
M.
Izquierdo
,
R.
Ruiz
,
M.
Bishop
, and
J. M.
Ferrero
, “
Personalized cardiac computational models: From clinical data to simulation of infarct-related ventricular tachycardia
,”
Front. Physiol.
10
,
580
(
2019
).
79.
S.
Zhou
,
E.
Sung
,
A.
Prakosa
 et al, “
Feasibility study shows concordance between image‐based virtual‐heart ablation targets and predicted ECG‐based arrhythmia exit‐sites
,”
Pacing Clin. Electrophysiol.
44
(
3
),
432
441
(
2021
).
80.
S.
Monaci
,
M.
Strocchi
,
C.
Rodero
 et al, “
In-silico pace-mapping using a detailed whole torso model and implanted electronic device electrograms for more efficient ablation planning
,”
Comput. Biol. Med.
125
,
104005
(
2020
).
81.
R.
Cabrera-Lozoya
,
B.
Berte
,
H.
Cochet
,
P.
Jaïs
,
N.
Ayache
, and
M.
Sermesant
, “
Image-based biophysical simulation of intracardiac abnormal ventricular electrograms
,”
IEEE Trans. Biomed. Eng.
64
(
7
),
1446
1454
(
2017
).
82.
R. C.
Lozoya
,
B.
Berte
,
H.
Cochet
,
P.
Jaïs
,
N.
Ayache
, and
M.
Sermesant
, “
Model-based feature augmentation for cardiac ablation target learning from images
,”
IEEE Trans. Biomed. Eng.
66
(
1
),
30
40
(
2019
).
83.
N.
Cedilnik
,
J.
Duchateau
,
R.
Dubois
 et al, “
Fast personalized electrophysiological models from computed tomography images for ventricular tachycardia ablation planning
,”
EP Europace
20
(
suppl_3
),
iii94
iii101
(
2018
).
84.
F. O.
Campos
,
M.
Orini
,
P.
Taggart
 et al, “
Characterizing the clinical implementation of a novel activation-repolarization metric to identify targets for catheter ablation of ventricular tachycardias using computational models
,”
Comput. Biol. Med.
108
,
263
275
(
2019
).
85.
F. O.
Campos
,
M.
Orini
,
R.
Arnold
 et al, “
Assessing the ability of substrate mapping techniques to guide ventricular tachycardia ablation using computational modelling
,”
Comput. Biol. Med.
130
,
104214
(
2021
).
86.
N. A.
Trayanova
,
D. M.
Popescu
, and
J. K.
Shade
, “
Machine learning in arrhythmia and electrophysiology
,”
Circ. Res.
128
,
544
566
(
2021
).
87.
S.
Fresca
,
A.
Manzoni
,
L.
Dedè
, and
A.
Quarteroni
, “
Deep learning-based reduced order models in cardiac electrophysiology
,”
PLoS One
15
,
e0239416
(
2020
).
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