@inproceedings {43140,
	title = {Electrophysiological Simulation of Maternal-Fetal ECG on a 3D Maternal Torso Model},
	journal = {Computing in Cardiology},
	volume = {49},
	year = {2023},
	publisher = {Computing in Cardiology},
	address = {Computing in Cardiology},
	abstract = {Congenital heart disease (CHD) is a leading cause of infant death. To diagnose CHD, recordings from abdomi- nal fetal electrocardiograms (fECG) can be used as a non- invasive tool. However, it is challenging to extract the fetal signal from fECG recordings partly due to the lack of data combining fECG recordings with a ground truth for the fetal signal, which can be obtained by using a scalp elec- trode during delivery. In this study, we present a computa- tional model of a pregnant female torso, in which we simu- late fetal and maternal ventricular excitation during sinus rhythm to derive fECGs, so as to enable isolated measure- ment of the fetal and maternal signal contributions.},
	keywords = {Non-invasive fetal electrocardiogram},
	issn = {2325-887X},
	doi = {10.22489/CinC.2022.136},
	url = {https://cinc.org/archives/2022/pdf/CinC2022-136.pdf},
	author = {Julie Johanne {Uv} and Arevalo, Hermenegild}
}
@article {43078,
	title = {simcardems: A FEniCS-based cardiac electro-mechanicssolver},
	journal = {Journal of Open Source Software},
	volume = {88442954520810717},
	year = {2023},
	month = {Jan-01-2023},
	pages = {4753},
	publisher = {JOSS},
	doi = {10.21105/joss.04753},
	url = {https://joss.theoj.org/papers/10.21105/joss.04753},
	author = {Finsberg, Henrik and Ilsbeth Gerarda Ma {van Herck} and Daversin-Catty, C{\'e}cile and Arevalo, Hermenegild and Wall, Samuel}
}
@inbook {42469,
	title = {3D Simulations of Fetal and Maternal Ventricular Excitation for Investigating the Abdominal ECG},
	booktitle = {Computational Physiology - Simula Summer School 2021 - Student Reports},
	volume = {12},
	year = {2022},
	month = {05/2022},
	pages = {13 - 24},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-031-05163-0},
	issn = {2512-1677},
	doi = {10.1007/978-3-031-05164-7_2},
	url = {https://doi.org/10.1007/978-3-031-05164-7_2},
	author = {Julie Johanne {Uv} and Myklebust, Lena and Hamid Khoshfekr {Rudsari} and Welle, Hannes and Arevalo, Hermenegild},
	editor = {Kimberly J {McCabe}}
}
@misc {42849,
	title = {Computational 3D cardiac electromechanical models for functional and pharmacological analysis},
	howpublished = {9th International Workshop on Cardiac Mechano-Electric Coupling and Arrhythmias},
	year = {2022},
	author = {Ilsbeth Gerarda Ma {van Herck} and Llopis-Lorente, Jordi and Finsberg, Henrik and Daversin-Catty, C{\'e}cile and Mora, Maria and Saiz, Javier and Trenor, Beatriz and Arevalo, Hermenegild and Wall, Samuel}
}
@misc {43093,
	title = {Development of a Biventricular Coordinate System with Representation of an Anatomically Detailed Base},
	howpublished = {Tampere },
	year = {2022},
	type = {Conference},
	author = {Pankewitz, Lisa and Govil, Sachin and Kristian Gregorius {Hustad} and James C {Perry} and Hedge, Sanjeet and Tang, Renxiang and Andrew D. {McCulloch} and Arevalo, Hermenegild}
}
@misc {42561,
	title = {Functional Analysis of Healthy and Heart Failure Tissue Populations using 3D Cardiac Electromechanical Models},
	howpublished = {15th World Congress on Computational Mechanics (virtual)},
	year = {2022},
	abstract = {Computer modelling and simulation of the beating heart must reflect on electrical activation of cells and tissue, mechanical properties of tissue, and their interaction. Electrophysiological properties of the heart have been simulated abundantly and applied to increase mechanistic understanding of disease and improve treatment development. Recent maturity in cardiac mechanical modelling increased quantitative predictive power. However, tight coupling of cardiac mechanics with the underlying electrophysiological properties of the tissue makes modelling clinical mechanical phenomena such as cardiac disease and drug effects difficult. A combination of uncertainty quantification through populations of models and fully coupled electromechanics models provide greater predictive power on cardiac mechanisms and aid treatment development for cardiac diseases.\&nbsp;A fully coupled electromechanics model of ventricular tissue is developed by coupling the O{\textquoteright}Hara-Rudy[1] electrophysiology model and the Land[2] mechanics model. A population of models was created by varying 16 electrophysiological and 11 mechanical parameters at the cell level. The population was calibrated from 1000 to 187 models based on biomarkers derived from the action potential shape, calcium transient and active tension. This calibrated population was altered in 11 parameters of the cell model to represent heart failure (based on [3]). A geometry of 20x7x3 mm is simulated with 1.0 or 0.5 mm spatial resolution for mechanics or electrophysiology respectively, with free movement in the fibre direction on one side. Results are extracted at the centre of the tissue, as well as tissue shortening on the free-contracting side.\&nbsp;Relative to the healthy population, heart failure manifests in both electrophysiological and mechanics biomarkers. The action potential takes ~20\% longer to recover from activation and peak calcium concentration in the cell is reduced by 50.9\%. In a similar trend, mechanical biomarkers show 42.2\% reduction in peak active force, slower contraction and more variation in recovery times across the heart failure tissue population. The peak tissue shortening in the fibre direction as a result of free contraction is reduced from 0.68{\textpm}0.07mm to 0.45{\textpm}0.07mm (-33.2\%) and its peak is delayed by 112ms (38.6\%) in heart failure.\&nbsp;These simulations indicate strong effects on electrophysiological and mechanical heart function by population variation and disease such as heart failure. Therefore, strongly coupled 3D models are necessary to assess the impact of biological variation, cardiac vulnerability as well as safe and effective treatment development.REFERENCES[1]\&nbsp;\&nbsp;\&nbsp;\&nbsp; O{\textquoteright}Hara, T. et al.; PLoS Comput. Biol. 2011 [doi:10.1371/journal.pcbi.1002061][2]\&nbsp;\&nbsp;\&nbsp;\&nbsp; Land, S. et al.; J. Mol. Cell. Cardiol. 2017 [doi:10.1016/J.YJMCC.2017.03.008][3]\&nbsp;\&nbsp;\&nbsp;\&nbsp; Gomez, J. et al; PLoS ONE 2014 [doi:10.1371/journal.pone.0106602]},
	keywords = {Cardiac biomechanics, Cardiac disease, Uncertainty quantification},
	author = {Ilsbeth Gerarda Ma {van Herck} and Finsberg, Henrik and Daversin-Catty, C{\'e}cile and Mora, Maria and Trenor, Beatriz and Arevalo, Hermenegild and Wall, Samuel}
}
@misc {43047,
	title = {An image-based 3D electrophysiological torso model for simulating maternal and fetal ECG},
	howpublished = {Virtual Physiological Human Conference - VPH},
	year = {2022},
	url = {https://vph-conference.org/wp-content/uploads/sites/11/2022/09/VPH2022_Abstract_book_2.pdf},
	author = {Myklebust, Lena and Julie Johanne {Uv} and Welle, Hannes and Hamid Khoshfekr {Rudsari} and Arevalo, Hermenegild}
}
@misc {42850,
	title = {Omecamtiv Mecarbil Improves Contraction Behaviour in a 3D Electromechanical Tissue Model of Heart Failure},
	howpublished = {49th Computing in Cardiology Conference},
	year = {2022},
	author = {Ilsbeth Gerarda Ma {van Herck} and Mora, Maria and Finsberg, Henrik and Daversin-Catty, C{\'e}cile and Trenor, Beatriz and Arevalo, Hermenegild and Wall, Samuel}
}
@inproceedings {42559,
	title = {Population of Computational Cell and Tissue Cardiac Electromechanical Models for functional analysis},
	journal = {World Congress of Biomechanics},
	year = {2022},
	edition = {9th},
	abstract = {IntroductionCardiac computational models can fill important gaps in understanding the cardiomyocyte or cardiac tissue properties. Recent maturity in cardiac mechanical modelling enables quantitative predictive power across a range of applications. One challenge however is the tight coupling of cardiac mechanics with the underlying electrophysiological properties of the tissue, which makes modelling clinical mechanical phenomena such as drug effects difficult. More robust predictions can be achieved by modelling populations of models, which incorporate stochastic parameter variability to represent biological variation. For cardiac computational models to live up to their potential in developing safe and efficient treatments for increasingly prevalent cardiac disease, such populations of fully coupled electromechanics models will provide greater predictive power on mechanics and physiology of the heart.\&nbsp;MethodsA fully coupled electromechanics model of ventricular tissue is developed by coupling the O{\textquoteright}Hara-Rudy[1] electrophysiology model and the Land[2] mechanics model. A population of models was created by varying 16 electrophysiological and 11 mechanical parameters at the cell level. The population was calibrated from 1000 to 187 models based on biomarkers derived from the action potential shape, calcium transient and active tension. The electromechanics solver is implemented by combining existing solvers for electrophysiology[3] and mechanics[4], which are both based on the open-source framework FEniCS[5]. A geometry of 20x7x3 mm is simulated with 1.0 or 0.5 mm spatial resolution for mechanics or electrophysiology respectively, with free movement in the fibre direction on one side.\&nbsp;ResultsTraces from all population models were extracted at the center of the tissue in all directions (green star in top left panel) for further analysis (see figure). Electrophysiological biomarkers, such as action potential duration and systolic calcium concentration remain within calibrated and cell population model range. The maximum active tension in tissue is reduced by 65\% compared to cell simulations. This reduction can be attributed to the free contraction in tissue versus fixed stretch in calibration and cell simulations. The magnitude and especially the recovery duration of active stretch varies widely across the population (see table for average{\textpm}SD). This range of timing and force of contraction reinforce the need to assess cardiac function and drug effects in populations of electromechanical models.\&nbsp;DiscussionWe present a pipeline for creating populations of strongly coupled 3D excitation-contraction models for ventricular tissue. The tissue model includes bidirectional coupling and biological variation, facilitating further investigation into the multifactorial effect of variation and drugs on the in silico human heart. This enables simulation of both mechanical and physiological function of the heart to aid development of safe and efficient treatment based on population statistics.\&nbsp;ReferencesO{\textquoteright}Hara, T. et al.; PLoS Comput Biol 2011 [doi:10.1371/journal.pcbi.1002061]Land, S. et al.; J. Mol. Cell. Cardiol. 2017 [doi:10.1016/J.YJMCC.2017.03.008]Logg, A. et al.; Springer 2012 [doi:10.1007/978-3-642-23099-8]Rognes, M. E. et al.; Journal of Open Source Software, 2017 [doi:10.21105/joss.00224]Finsberg, H. N. T.; Journal of Open Source Software, 2019 [doi:10.21105/joss.01539]},
	author = {van Herck, Ilse and Finsberg, Henrik and Daversin-Catty, C{\'e}cile and Mora, Maite and Trenor, Beatriz and Arevalo, Hermenegild and Wall, Samuel}
}
@article {albatat2022regional,
	title = {Regional Left Ventricular Fiber Stress Analysis for Cardiac Resynchronization Therapy Response},
	journal = {Annals of Biomedical Engineering},
	volume = {51},
	year = {2022},
	month = {07/2022},
	pages = {343{\textendash}351},
	publisher = {Springer},
	abstract = {Cardiac resynchronization therapy (CRT) is an effective treatment for a subgroup of heart failure (HF) patients, but more than 30\% of those selected do not improve after CRT implantation. Imperfect pre-procedural criteria for patient selection and optimization are the main causes of the high non-response rate. In this study, we evaluated a novel measure for assessing CRT response. We used a computational modeling framework to calculate the regional stress of the left ventricular wall of seven CRT patients and seven healthy controls. The standard deviation of regional wall stress at the time of mitral valve closure (SD_MVC) was used to quantify dyssynchrony and com- pared between patients and controls and among the patients. The results show that SD_MVC is significantly lower in controls than patients and correlates with long-term response in patients, based on end-diastolic volume reduction. In contrast to our initial hypothesis, patients with lower SD_MVC respond better to therapy. The patient with the highest SD_MVC was the only non-responder in the patient cohort. The distribution of fiber stress at the beginning of the isovolumetric phase seems to correlate with the degree of response and the use of this measurement could potentially improve selection criteria for CRT implantation. Further studies with a larger cohort of patients are needed to validate these results.},
	keywords = {Cardiac resynchronization therapy, Computational cardiology, electrophysiology, Heart failure},
	doi = {10.1007/s10439-022-03030-y},
	url = {https://doi.org/10.1007/s10439-022-03030-y},
	author = {Albatat, Mohammad and Finsberg, Henrik and Arevalo, Hermenegild and Sundnes, Joakim and Bergsland, Jacob and Balasingham, Ilangko and Hans Henrik {Odland}}
}
@misc {maleckar2021b,
	title = {B-PO02-022 Combining simulation and machine learning to accurately predict arrhythmic risk in post-infarction patients},
	howpublished = {Heart Rhythm},
	volume = {18},
	number = {8},
	year = {2021},
	pages = {S104},
	publisher = {Elsevier},
	address = {Boston, MA},
	author = {Maleckar, Molly and Per Magne {Florvaag} and Str{\o}m, Vilde and Glinge, Charlotte and Jabbari, Reza and Vejlstrup, Niels and Engstrom, Thomas and Kiril A. {Ahtarovski} and Jespersen, Thomas and Tfelt-Hansen, Jacob and Arevalo, Hermenegild}
}
@article {maleckar2021combined,
	title = {Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients},
	journal = {Frontiers in physiology},
	volume = {12},
	year = {2021},
	pages = {745349},
	publisher = {Frontiers},
	author = {Maleckar, Molly and Myklebust, Lena and Julie Johanne {Uv} and Per Magne {Florvaag} and Str{\o}m, Vilde and Glinge, Charlotte and Jabbari, Reza and Vejlstrup, Niels and Engstr{\o}m, Thomas and Ahtarovski, Kiril and Jespersen, Thomas and Tfelt-Hansen, Jacob and Arevalo, Hermenegild}
}
@article {27783,
	title = {A computational study of the effects of tachycardia-induced remodelling on calcium wave propagation in rabbit atrial myocytes},
	journal = {Frontiers in Physiology},
	volume = {12},
	year = {2021},
	publisher = {Frontiers},
	abstract = {In atrial cardiomyocytes without a well-developed T-tubule system, calcium diffuses from the periphery towards the center creating a centripetal wave pattern. During atrial fibrillation, rapid activation of atrial myocytes induces complex remodelling in diffusion properties that result in failure of calcium to propagate in a fully regenerative manner towards the center; a phenomenon termed\&nbsp; {\textquoteleft}{\textquoteleft}calcium silencing{\textquoteright}{\textquoteright}. This has been observed in rabbit atrial myocytes after exposure to prolonged rapid\&nbsp; pacing. Although experimental studies have pointed to possible mechanisms underlying calcium silencing, their individual effects and relative importance remain largely unknown.In this study we used computational modelling of the rabbit atrial cardiomyocyte to query the individual effects and combined effects of the proposed mechanisms leading to calcium\&nbsp;silencing and abnormal calcium wave propagation. We employed a population of models obtained from a newly developed model of the rabbit atrial myocyte with spatial representation of intracellular calcium handling. We selected parameters in the model that represent experimentally observed cellular remodelling which have been implicated in calcium silencing, and scaled their values in the population to match experimental observations. In particular, we changed the maximum conductances of I_CaL I_NCX, and I_NaK, \&nbsp;RyR open probability, RyR density, Serca2a density, and calcium buffering strength. We incorporated remodelling in a population of 16 models by independently varying parameters that reproduce experimentally observed cellular remodelling, and quantified the resulting alterations in calcium dynamics and wave propagation patterns.The results show a strong effect of\&nbsp; I_CaL in driving calcium silencing, with I_NCX, \&nbsp;I_NaK, and RyR density also resulting in calcium silencing in some models. Calcium alternans was observed in some models where I_NCX and Serca2a density had been changed. Simultaneously incorporating changes in all remodelled parameters resulted in calcium\&nbsp;silencing in all models, indicating the predominant role of decreasing I_CaL in the population phenotype.},
	keywords = {calcium waves, computational model, population of models, rabbit atrial cardiomyocyte, spatial calcium dynamics, tachypacing},
	author = {Vagos, Marcia and Arevalo, Hermenegild and Heijman, Jordi and Schotten, Ulrich and Sundnes, Joakim}
}
@article {ALBATAT2021104159,
	title = {Optimal pacing sites in cardiac resynchronization by left ventricular activation front analysis},
	journal = {Computers in Biology and Medicine},
	volume = {128},
	year = {2021},
	pages = {104159},
	publisher = {Elsevier},
	abstract = {Cardiac resynchronization therapy (CRT) can substantially improve dyssynchronous heart failure and reduce mortality. However, about one-third of patients who are implanted, derive no measurable benefit from CRT. Non-response may partly be due to suboptimal activation of the left ventricle (LV) caused by electrophysiological heterogeneities. The goal of this study is to investigate the performance of a newly developed method used to analyze electrical wavefront propagation in a heart model including myocardial scar and compare this to clinical benchmark studies. We used computational models to measure the maximum activation front (MAF) in the LV during different pacing scenarios. Different heart geometries and scars were created based on cardiac MR images of three patients. The right ventricle (RV) was paced from the apex and the LV was paced from 12 different sites, single site, dual-site and triple site. Our results showed that for single LV site pacing, the pacing site with the largest MAF corresponded with the latest activated regions of the LV demonstrated during RV pacing, which also agrees with previous markers used for predicting optimal single-site pacing location. We then demonstrated the utility of MAF in predicting optimal electrode placements in more complex scenarios including scar and multi-site LV pacing. This study demonstrates the potential value of computational simulations in understanding and planning CRT.},
	keywords = {Cardiac resynchronization therapy, Cardiology, Computational modeling, electrophysiology, Heart failure},
	issn = {0010-4825},
	doi = {https://doi.org/10.1016/j.compbiomed.2020.104159},
	url = {http://www.sciencedirect.com/science/article/pii/S001048252030490X},
	author = {Albatat, Mohammad and Arevalo, Hermenegild and Bergsland, Jacob and Str{\o}m, Vilde and Balasingham, Ilangko and Hans Henrik {Odland}}
}
@misc {27210,
	title = {Efficient simulations of patient-specific electrical heart activity on the DGX-2},
	year = {2020},
	month = {03/2020},
	publisher = {Nvidia},
	address = {GPU Technology Conference (GTC) 2020, Silicon Valley, USA},
	abstract = {Patients who have suffered a heart attack have an elevated risk of developing arrhythmia. The use of computer simulations of the electrical activity in the hearts of these patients, is emerging as an alternative to traditional, more invasive examinations performed by doctors today. Recent advances in personalised arrhythmia risk prediction show that computational models can provide not only safer but also more accurate results than invasive procedures. However, biophysically accurate simulations of the electrical activity in the heart require solving linear systems over fine meshes and time resolutions, which can take hours or even days. This limits the use of such simulations in the clinic where diagnosis and treatment planning can be time sensitive, even if it is just for the reason of operation schedules. Furthermore, the non-interactive, non-intuitive way of accessing simulations and their results makes it hard to study these collaboratively. Overcoming these limitations requires speeding up computations from hours to seconds, which requires a massive increase in computational capabilities.We have developed a code that is capable of performing highly efficient heart simulations on the DGX-2, making use of all 16 V100 GPUs. Using a patient-specific unstructured tetrahedral mesh with 11.7 million cells, we are able to simulate the electrical heart activity at 1/30 of real-time. Moreover, we are able to show that the throughput achieved using all 16 GPUs in the DGX-2 is 77.6\% of the theoretical maximum.We achieved this through extensive optimisations of the two kernels constituting the body of the main loop in the simulator. In the kernel solving the diffusion equation (governing the spread of the electrical signal), constituting of a sparse matrix-vector multiplication, we minimise the memory traffic by reordering the mesh (and matrix) elements into clusters that fit in the V100{\textquoteright}s L2 cache. In the kernel solving the cell model (describing the complex interactions of ion channels in the cell membrane), we apply sophisticated domain-specific optimisations to reduce the number of floating point operations to the point where the kernel becomes memory bound. After optimisation, both kernels are memory bound, and we derive the minimum memory traffic, which we then divide by the aggregate memory bandwidth to obtain a lower bound on the execution time.Topics discussed include optimisations for sparse matrix-vector multiplications, strategies for handling inter-device communication for unstructured meshes, and lessons we learnt while programming the DGX-2.},
	author = {Kristian Gregorius {Hustad} and Cai, Xing and Langguth, Johannes and Arevalo, Hermenegild}
}
@article {DHAMALA2020101670,
	title = {Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models},
	journal = {Medical Image Analysis},
	volume = {62},
	year = {2020},
	pages = {101670},
	publisher = {Elsevier},
	abstract = {The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. Because tissue properties are spatially varying across the underlying geometrical model, it presents a significant challenge of high-dimensional (HD) optimization at the presence of limited measurement data. A common solution to reduce the dimension of the parameter space is to explicitly partition the geometrical mesh. In this paper, we present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space. The presented method is applied to estimating tissue excitability in a cardiac electrophysiological model in a range of synthetic and real-data experiments, through which we demonstrate its improved accuracy and substantially reduced computational cost in comparison to existing methods that rely on geometry-based reduction of the HD parameter space.},
	keywords = {High-dimensional Bayesian optimization, personalized modeling, variational autoencoder},
	issn = {1361-8415},
	doi = {https://doi.org/10.1016/j.media.2020.101670},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841520300360},
	author = {Dhamala, Jwala and Bajracharya, Pradeep and Arevalo, Hermenegild and John L. {Sapp} and Hor{\'a}cek, Milan and Katherine C. {Wu} and Natalia A. {Trayanova} and Wang, Linwei}
}
@article {27176,
	title = {Multisite pacing and myocardial scars: a computational study},
	journal = {Computer Methods in Biomechanics and Biomedical Engineering},
	volume = {23},
	year = {2020},
	month = {01/2020},
	publisher = {Taylor and Francis},
	issn = {1025-5842},
	doi = {10.1080/10255842.2020.1711885},
	url = {https://www.tandfonline.com/doi/full/10.1080/10255842.2020.1711885},
	author = {Albatat, Mohammad and Bergsland, Jacob and Arevalo, Hermenegild and Hans Henrik {Odland} and Wall, Samuel and Sundnes, Joakim and Balasingham, Ilangko}
}
@article {27450,
	title = {A novel computational model of the rabbit atrial cardiomyocyte with spatial calcium dynamics},
	journal = {Frontiers in Physiology},
	volume = {11},
	year = {2020},
	pages = {1205},
	publisher = {Frontiers},
	author = {Vagos, Marcia and Arevalo, Hermenegild and Heijman, Jordi and Schotten, Ulrich and Sundnes, Joakim}
}
@inproceedings {27742,
	title = {Potential Roles of Purkinje Fibers in Ischemia-Induced Arrhythmias},
	journal = {Computing in Cardiology},
	volume = {47},
	year = {2020},
	publisher = {IEEE},
	author = {Teo Puig {Walz} and Azzolin, Luca and Berg, Lucas and Chleilat, Enaam and Arevalo, Hermenegild}
}
@article {27666,
	title = {Regional Left Ventricular Fiber Stress Analysis for Cardiac Resynchronization Therapy Response},
	journal = {Journal of Cardiovascular Electrophysiology},
	year = {2020},
	publisher = { Wiley},
	author = {Albatat, Mohammad and Finsberg, Henrik and Arevalo, Hermenegild and Sundnes, Joakim and Bergsland, Jacob and Balasingham, Ilangko and Hans Henrik {Odland}}
}
@misc {28271,
	title = {System and method for personalized cardiac arrhythmia risk assessment by simulating arrhythmia inducibility},
	year = {2020},
	note = {US Patent 10,827,983},
	author = {Vadakkumpadan, Fijoy and Arevalo, Hermenegild and Natalia A. {Trayanova} and Wu, Katherine}
}
@misc {28272,
	title = {System and method for planning a patient-specific cardiac procedure},
	year = {2020},
	note = {US Patent 10,765,336},
	author = {Natalia A {Trayanova} and Henry R {Halperin} and Arevalo, Hermenegild and Constantino, Jason}
}
@article {27156,
	title = {Technological and Clinical Challenges in Lead Placement for Cardiac Rhythm Management Devices},
	journal = {Annals of Biomedical Engineering},
	volume = {48},
	year = {2020},
	month = {01/2020},
	pages = {26 - 46},
	publisher = {Springer Link},
	issn = {0090-6964},
	doi = {10.1007/s10439-019-02376-0},
	author = {Albatat, Mohammad and Bergsland, Jacob and Arevalo, Hermenegild and Hans Henrik {Oddland} and Bose, Pritam and Per Steinar {Halvorsen} and Balasingham, Ilangko}
}
@misc {27565,
	title = {Towards detailed Organ-Scale Simulations in Cardiac Electrophysiology},
	year = {2020},
	type = {Poster},
	address = {GPU Technology Conference (GTC), Silicon Valley, San Jose, USA},
	author = {Langguth, Johannes and Gaur, Namit and Arevalo, Hermenegild and Jarvis, Chad and Altanaite, Neringa and Lan, Qiang and Cai, Xing}
}
@misc {27158,
	title = {A Combined In-Silico and Machine Learning Approach towards Predicting Arrhythmic Risk in Post-Infarction Patients},
	howpublished = {Computing in Cardiology, Singapore},
	year = {2019},
	author = {Per Magne {Florvaag} and Str{\o}m, Vilde and Glinge, Charlotte and Jabbari, Reza and Vejlstrup, Niels and Engstrom, Thomas and Kiril A. {Ahtarovski} and Jespersen, Thomas and Tfelt-Hansen, Jacob and Naumova, Valeriya and Arevalo, Hermenegild}
}
@inproceedings {27157,
	title = {Investigating the Pro- and Anti-Arrhythmic Properties of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes in Post-Infarction Patient Hearts: A Modeling Study},
	journal = {6th International Conference on Computational and Mathematical Biomedical Engineering},
	volume = {1},
	year = {2019},
	pages = {357-360},
	publisher = {Zeta Computational Resources Ltd.},
	issn = {2227-9385},
	author = {Arevalo, Hermenegild and Karoline Horgmo {J{\ae}ger} and Wall, Samuel}
}
@inproceedings {27195,
	title = {Towards Detailed Real-Time Simulations of Cardiac Arrhythmia},
	journal = {Computing in Cardiology},
	volume = {46},
	year = {2019},
	month = {12/2019},
	publisher = {IEEE},
	abstract = {Recent advances in personalized arrhythmia risk pre- diction show that computational models can provide not only safer but also more accurate results than invasive pro- cedures. However, biophysically accurate simulations re- quire solving linear systems over fine meshes and time res- olutions, which can take hours or even days. This limits the use of such simulations in the clinic where diagnosis and treatment planning can be time sensitive, even if it is just for the reason of operation schedules. Furthermore, the non-interactive, non-intuitive way of accessing simula- tions and their results makes it hard to study these collab- oratively. Overcoming these limitations requires speeding up computations from hours to seconds, which requires a massive increase in computational capabilities.Fortunately, the cost of computing has fallen dramati- cally in the past decade. A prominent reason for this is the recent introduction of manycore processors such as GPUs, which by now power the majority of the world{\textquoteright}s leading supercomputers. These devices owe their success to the fact that they are optimized for massively parallel work- loads, such as applying similar ODE kernel computations to millions of mesh elements in scientific computing ap- plications. Unlike CPUs, which are typically optimized for sequential performance, this allows GPU architectures to dedicate more transistors to performing computations, thereby increasing parallel speed and energy efficiency.},
	author = {Langguth, Johannes and Arevalo, Hermenegild and Cai, Xing}
}
@misc {26853,
	title = {Towards Detailed Real-Time Simulations of Cardiac Arrhythmia},
	year = {2019},
	month = {09/2019},
	address = { International Conference in Computing in Cardiology, Singapore},
	abstract = {Recent advances in personalized arrhythmia risk prediction show that computational models can provide not only safer but also more accurate results than invasive procedures.\&nbsp; However, biophysically accurate simulations require solving linear systems over fine meshes and time resolutions, which can take hours or even days. This limits the use of such simulations in the clinic where diagnosis and treatment planning can be time sensitive, even if it is just for the reason of operation schedules. Furthermore, the non-interactive, non-intuitive way of accessing simulations and their results makes it hard to study these collaboratively.Overcoming these limitations requires speeding up computations from hours to seconds, which requires a massive increase in computational capabilities.Fortunately, the cost of computing has fallen dramatically in the past decade. A prominent reason for this is the recent introduction of manycore processors such as GPUs, which by now power the majority of the world{\textquoteright}s leading supercomputers. These devices owe their success to the fact that they are optimized for massively parallel workloads, such as applying similar ODE kernel computations to millions of mesh elements in scientific computing applications. Unlike CPUs, which are typically optimized for sequential performance, this allows GPU architectures to dedicate more transistors to performing computations, thereby increasing parallel speed and energy efficiency.In this poster, we present ongoing work on the parallelization of finite volume computations over an unstructured mesh as well as the challenges involved in building scalable simulation codes and discuss the steps needed to close the gap to accurate real-time computations.\&nbsp;},
	author = {Langguth, Johannes and Arevalo, Hermenegild and Kristian Gregorius {Hustad} and Cai, Xing}
}
@article {26116,
	title = {Computational Modeling of Electrophysiology and Pharmacotherapy of Atrial Fibrillation: Recent Advances and Future Challenges},
	journal = {Frontiers in Physiology},
	volume = {9},
	year = {2018},
	month = {Apr-09-2018},
	pages = {1221},
	publisher = {Frontiers in Physiology},
	doi = {10.3389/fphys.2018.01221},
	url = {https://www.frontiersin.org/article/10.3389/fphys.2018.01221/fullhttps://www.frontiersin.org/article/10.3389/fphys.2018.01221/full},
	author = {Vagos, Marcia and Ilsbeth Gerarda Ma {van Herck} and Sundnes, Joakim and Arevalo, Hermenegild and Andrew G {Edwards} and Koivum{\"a}ki, Jussi}
}
@inproceedings {26370,
	title = {Electromechanical Model to Predict Cardiac Resynchronization Therapy},
	journal = {2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
	year = {2018},
	pages = {5446-5459},
	publisher = {IEEE},
	doi = {10.1109/EMBC.2018.8513539},
	author = {Albatat, Mohammad and King, Ryan and Unger, Laura and Arevalo, Hermenegild and Wall, Samuel and Sundnes, Joakim and Bergsland, Jacob and Balasingham, Ilangko}
}
@misc {43144,
	title = {Model development of SK channel gating incorporating calcium sensitivity and drug interaction},
	year = {2018},
	month = {02/2018},
	type = {Conference Poster},
	address = {Biophysical Society 62nd Annual Meeting, San Francisco CA},
	abstract = {Small conductance calcium activated potassium (SK) channels are predominantly expressed in heart atria and contribute to late repolarization of the action potential. Inhibition of SK current has been proposed as a therapy for atrial fibrillation (AF). We aim to develop an accurate model of SK channel gating and drug interaction that can be used to probe the role of SK channel in atrial health and disease.\&nbsp;The model, based on Hirschberg et al{\textquoteright}s (1998) SK2 model, incorporates 4 closed and 2 open states. To generate a model with utility at physiologic temperature, we performed inside-out macropatch voltage clamp ramps at 23{\textdegree} and 37{\textdegree}C, and observed a pronounced leftward EC50 shift at 37{\textdegree}C (0.53 {\textpm} 0.07\&nbsp;{\textmu}M and 0.38 {\textpm} 0.02\&nbsp;{\textmu}M to\&nbsp;0.23 {\textpm} 0.02\&nbsp;{\textmu}M and 0.28 {\textpm} 0.01\&nbsp;{\textmu}M for hSK3 and hSK2, respectively). Thus, taking calcium activation of the major hSK isoforms into the diastolic calcium range. To provide a first-principles basis for constraining the kinetics of multi-step calcium-dependent activation, and binding of known and novel pharmacologic inhibitors, we performed single0channel recordings of SK2 at 23{\textdegree}C. Opening events without inhibitors were best fit with two major open-state conductances (low: 10.9 {\textpm} 1.6 pS, 60 {\textpm} 41\% of events; high: 14.9 {\textpm} 1.4 pS, 40 {\textpm} 41\% of events), justifying the multiple open states included in the model and to be further analysed regarding state specific kinetics. We will also present single channel data on known inhibitor apamin and novel modulator AP14145 constraining a pharmacological model. In conclusion, these experiments provided novel findings on temperature sensitivity of SK channel. These will be incorporated into an improved Markov model of SK channel dynamics to elucidate the roles of SK channels in health and disease.\&nbsp;},
	author = {Ilsbeth Gerarda Ma {van Herck} and Bo H {Bentzen} and Seutin, Vincent and Arevalo, Hermenegild and Maleckar, Molly and Neil V {Marrion} and Andrew G {Edwards}}
}
@article {van2018model,
	title = {Model development of sk channel gating incorporating calcium sensitivity and drug interaction},
	journal = {Biophysical Journal},
	volume = {114},
	number = {3},
	year = {2018},
	pages = {306a},
	publisher = {{Elsevier},
	author = {van Herck, Ilse and Bo H {Bentzen} and Seutin, Vincent and Arevalo, Hermenegild and Maleckar, Molly and Neil V {Marrion} and Andrew G {Edwards}}
}
@article {vagos2018novel,
	title = {A Novel Computational Model of the Rabbit Atrial Myocyte Offers Insight into Calcium Wave Propagation Failure},
	journal = {Biophysical Journal},
	volume = {114},
	number = {3},
	year = {2018},
	pages = {119a},
	publisher = {{Elsevier},
	author = {M{\'a}rcia R {Vagos} and Heijman, Jordi and Arevalo, Hermenegild and Maleckar, Molly and Bernardo Lino {de Oliveira} and Schotten, Ulrich and Sundnes, Joakim}
}
@misc {26368,
	title = {A Novel Computational Model of the Rabbit Atrial Myocyte Offers Insight into Calcium Wave Propagation Failure},
	howpublished = {Biophysical Journal},
	year = {2018},
	month = {Jan-02-2018},
	address = {Cambridge, USA},
	abstract = {Atrial cardiomyocytes have a less well-developed T-tubule system than ventricular cells, resulting in intracellular calcium waves propagating from the membrane to the center via centripetal calcium diffusion. Failure of centripetal calcium-wave propagation ({\textquoteleft}calcium silencing{\textquoteright}) has been observed in a rabbit model of rapid atrial pacing and in patients with atrial fibrillation, but the underlying mechanisms remain incompletely understood. The goal of this study was to develop a novel computational model of the rabbit atrial cardiomyocyte that incorporates detailed compartmentalization of intracellular calcium dynamics, which can be used to investigate the mechanisms underpinning calcium silencing.
We incorporated ion-current formulations reflecting rabbit electrophysiology into a previously published human atrial cardiomyocyte model. The model was validated with published experimental data and the effects of altered rate of calcium diffusion between the calcium-release-unit space and the cytosol (τdiff) were investigated. τdiff modulates local calcium levels available to activate neighboring calcium-release sites, affecting wave propagation.
Simulation results showed that calcium-wave propagation was highly sensitive to τdiff during normal pacing at 2 Hz. We observed impaired calcium-wave propagation for a range of values of τdiff, with full calcium-wave propagation for values of τdiff exceeding 12.7 ms, and calcium silencing for values of τdiff below 10.6 ms due to insufficient local positive feedback for calcium-induced calcium release to maintain centripetal wave propagation. We also observed calcium alternans between propagating and non-propagating waves for intermediate values of τdiff.
This study provided new insight into the mechanisms of calcium-wave propagation failure in rabbit atrial cardiomyocytes and motivates further investigation of the effects of altered calcium diffusion on wave-propagation abnormalities and calcium-dependent arrhythmogenesis. Moreover, the newly developed model will be a useful tool for studying conditions which permit restoration of normal calcium wave propagation.},
	issn = {00063495},
	doi = {10.1016/j.bpj.2017.11.680},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0006349517319124https://api.elsevier.com/content/article/PII:S0006349517319124?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0006349517319124?httpAccept=text/plain},
	author = {Vagos, Marcia and Heijman, Jordi and Arevalo, Hermenegild and Maleckar, Molly and Bernardo Lino {de Oliveira} and Schotten, Ulrich and Sundnes, Joakim}
}
@article {26369,
	title = {Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models},
	journal = {Computers in biology and medicine},
	volume = {102},
	year = {2018},
	pages = {426-432},
	publisher = {Pergamon},
	author = {Deng, Dongdong and Plamen P. {Nikolov} and Arevalo, Hermenegild and Natalia A. {Trayanova}}
}
@article {26115,
	title = {Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia},
	journal = {Nature Biomedical Engineering},
	volume = {2},
	year = {2018},
	month = {09/2018},
	pages = {732{\textendash}740},
	publisher = {Nature Research},
	doi = {10.1038/s41551-018-0282-2},
	url = {http://www.nature.com/articles/s41551-018-0282-2},
	author = {Prakosa, Adityo and Arevalo, Hermenegild and Deng, Dongdong and Patrick M. {Boyle} and Plamen P. {Nikolov} and Ashikaga, Hiroshi and Joshua J. E. {Blauer} and Ghafoori, Elyar and Carolyn J. {Park} and Robert C. {Blake} and Frederick T. {Han} and Rob S. {MacLeod} and Henry R. {Halperin} and David J. {Callans} and Ranjan, Ravi and Chrispin, Jonathan and Nazarian, Saman and Natalia A. {Trayanova}}
}
@article {26117,
	title = {Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology},
	journal = {Medical Image Analysis},
	volume = {48},
	year = {2018},
	month = {Jan-08-2018},
	pages = {43 - 57},
	publisher = {Medical Image Analysis},
	issn = {13618415},
	doi = {10.1016/j.media.2018.05.007},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1361841518302925https://api.elsevier.com/content/article/PII:S1361841518302925?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1361841518302925?httpAccept=text/plain},
	author = {Dhamala, Jwala and Arevalo, Hermenegild and Sapp, John and Hor{\'a}cek, Milan and Katherine C {Wu} and Natalia A. {Trayanova} and Wang, Linwei}
}
@misc {26131,
	title = {Towards Detailed Organ-Scale Simulations in Cardiac Electrophysiology},
	year = {2018},
	month = {09/2018},
	type = {Poster},
	address = {International Symposium on Computational Science at Scale (CoSaS), Erlangen, Germany},
	abstract = {We present implementations of tissue-scale 3D simulations of the human cardiac ventricle using a physiologically realistic cell model. Computational challenges in such simulations arise from two factors, the\&nbsp; first of which is the sheer amount of computation when simulating a large number of cardiac cells in a detailed model containing 10^4 calcium release units, 10^6 stochastically changing ryanodine receptors and 1.5 {\texttimes} 10^5 L-type calcium channels per cell.Additional challenges arise from the fact that the computational tasks have various levels of arithmetic intensity and control complexity, which require careful adaptation of the simulation code to the target device. By exploiting the strengths of GPUs and manycore accelerators, we obtain a performance that is far superior to that of the basic CPU implementation, thus paving the way for detailed whole-heart simulations in future generations of leadership class supercomputers.},
	keywords = {Cardiac electrophysiology, GPU, Scientific Computing, Xeon Phi},
	author = {Langguth, Johannes and Arevalo, Hermenegild and Jarvis, Chad and Cai, Xing}
}
@article {25392,
	title = {A computational framework for testing arrhythmia marker sensitivities to model parameters in functionally calibrated populations of atrial cells},
	journal = {Chaos},
	volume = {27},
	year = {2017},
	publisher = {AIP},
	doi = {10.1063/1.4999476},
	author = {Vagos, Marcia and Arevalo, Hermenegild and Bernardo Lino {de Oliveira} and Sundnes, Joakim and Maleckar, Molly}
}
@article {vagos2017computational,
	title = {A computational framework for testing arrhythmia marker sensitivities to model parameters in functionally calibrated populations of atrial cells},
	journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
	volume = {27},
	number = {9},
	year = {2017},
	pages = {093941},
	publisher = {{AIP Publishing LLC},
	author = {M{\'a}rcia R {Vagos} and Arevalo, Hermenegild and Bernardo Lino {de Oliveira} and Sundnes, Joakim and Maleckar, Molly}
}
@misc {25393,
	title = {A data-driven framework uncovers arrhythmogenic mechanisms in a {\textquoteright}functionally calibrated{\textquoteright} population of models},
	year = {2017},
	address = {Biophysical Society 61st Annual Meeting, New Orleans, Louisiana, USA},
	abstract = {Human atrial cell models have been used to establish electrophysiological properties in normal sinus rhythm (nSR) or in atrial fibrillation (AF). However, the complexity of these models makes elucidation of the mechanisms underlying arrhythmigenic behavior difficult. This study presents a data-driven methodology that can be used to identify novel arrhythmogenic mechanisms in a {\textquoteright}functionally calibrated{\textquoteright} populations of models.},
	author = {Vagos, Marcia and Arevalo, Hermenegild and Sacco, Federica and Maleckar, Molly}
}
@inproceedings {25226,
	title = {A multiple kernel learning framework to investigate the relationship between ventricular fibrillation and first myocardial infarction},
	journal = {Functional Imaging and Modelling of the Heart},
	year = {2017},
	publisher = { Springer},
	isbn = {978-3-319-59448-4},
	doi = {10.1007/978-3-319-59448-4_16},
	author = {Marciniak, Maciej and Arevalo, Hermenegild and Tfelt-Hansen, Jacob and Kiril A. {Ahtarovski} and Jespersen, Thomas and Jabbari, Reza and Glinge, Charlotte and Vejlstrup, Niels and Engstrom, Thomas and Maleckar, Molly and Kristin Sarah {Mcleod}},
	editor = {Pop, Mihaela and Wright, Graham}
}
@inproceedings {marciniak2017multiple,
	title = {A multiple kernel learning framework to investigate the relationship between ventricular fibrillation and first myocardial infarction},
	journal = {International Conference on Functional Imaging and Modeling of the Heart},
	year = {2017},
	pages = {161{\textendash}171},
	publisher = {{Springer, Cham},
	author = {Marciniak, Maciej and Arevalo, Hermenegild and Tfelt-Hansen, Jacob and Kiril A {Ahtarovski} and Jespersen, Thomas and Jabbari, Reza and Glinge, Charlotte and Vejlstrup, Niels and Engstrom, Thomas and Maleckar, Molly and , }
}
@misc {43145,
	title = {Small conductance calcium activated potassium channel: pharmacology and binding scheme for in silico model},
	year = {2017},
	month = {06/2017},
	type = {Poster},
	address = {Copenhagen Meeting on Cardiac Arrhythmia},
	author = {Ilsbeth Gerarda Ma {van Herck} and Rafel Sim{\'o} {Vicens} and Arevalo, Hermenegild and Koivum{\"a}ki, Jussi and Bo H {Bentzen} and Andrew G {Edwards}}
}
@article {26118,
	title = {Spatially Adaptive Multi-Scale Optimization for Local Parameter Estimation in Cardiac Electrophysiology},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	month = {Jan-09-2017},
	pages = {1966 - 1978},
	publisher = {IEEE Transactions on Medical Imaging},
	issn = {0278-0062},
	doi = {10.1109/TMI.2017.2697820},
	url = {http://ieeexplore.ieee.org/document/7911242/http://ieeexplore.ieee.org/ielam/42/8022994/7911242-aam.pdfhttp://xplorestaging.ieee.org/ielx7/42/8022994/07911242.pdf?arnumber=7911242},
	author = {Dhamala, Jwala and Arevalo, Hermenegild and Sapp, John and Horacek, Milan and Katherine C. {Wu} and Natalia A. {Trayanova} and Wang, Linwei}
}
@article {24514,
	title = {Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models},
	journal = {Nature Communications},
	volume = {7},
	year = {2016},
	month = {05/2016},
	publisher = {Nature Publishing Group},
	doi = {10.1038/ncomms11437},
	author = {Arevalo, Hermenegild and Vadakkumpadan, Fijoy and Guallar, Eliseo and Jebb, Alexander and Malamas, Peter and Katherine C {Wu} and Natalia A. {Trayanova}}
}
@article {24515,
	title = {Computational rabbit models to investigate the initiation, perpetuation, and termination of ventricular arrhythmia},
	journal = {Progress in Biophysics and Molecular Biology},
	volume = {121},
	year = {2016},
	pages = {185-194},
	publisher = {Elsevier},
	author = {Arevalo, Hermenegild and Patrick M {Boyle} and Natalia A. {Trayanova}}
}
@article {26119,
	title = {A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction},
	journal = {EP Europace},
	volume = {18122835229411747171359456711565640366286299284923(Suppl 6)21052047111011842101213110},
	year = {2016},
	month = {Nov-12-2017},
	pages = {iv60 - iv66},
	publisher = {EP Europace},
	issn = {1099-5129},
	doi = {10.1093/europace/euw351},
	url = {https://academic.oup.com/europace/article/18/suppl_4/iv60/2737179http://academic.oup.com/europace/article-pdf/18/suppl_4/iv60/24119767/euw351.pdf},
	author = {Deng, Dongdong and Arevalo, Hermenegild and Prakosa, Adityo and David J. {Callans} and Natalia A. {Trayanova}}
}
@inproceedings {marciniak2016cmr,
	title = {From CMR image to patient-specific simulation and population-based analysis: tutorial for an openly available image-processing pipeline},
	journal = {International Workshop on Statistical Atlases and Computational Models of the Heart},
	year = {2016},
	pages = {106{\textendash}117},
	publisher = {{Springer, Cham},
	author = {Marciniak, Maciej and Arevalo, Hermenegild and Tfelt-Hansen, Jacob and Jespersen, Thomas and Jabbari, Reza and Glinge, Charlotte and Kiril A {Ahtarovski} and Vejlstrup, Niels and Engstrom, Thomas and Maleckar, Molly},
	editor = {, }
}
@inproceedings {24886,
	title = {From MR image to patient-specific simulation and population-based analysis:Tutorial for an openly available image-processing pipeline},
	journal = {MICCAI Workshop on Statistical Atlases and Cardiac Models of the Heart},
	year = {2016},
	publisher = {Springer},
	address = {Berlin},
	author = {Marciniak, Maciej and Arevalo, Hermenegild and Tfelt-Hansen, Jacob and Jespersen, Thomas and Jabbari, Reza and Glinge, Charlotte and Kiril A. {Ahtarovski} and Vejlstrup, Niels and Maleckar, Molly and Kristin Sarah {Mcleod}},
	editor = {Engstrom, Thomas}
}
@article {24790,
	title = {Optogenetic defibrillation terminates ventricular arrhythmia in mouse hearts and human simulations},
	journal = {The Journal of Clnical Investigation},
	volume = {126},
	year = {2016},
	publisher = {American Society for Clinical Investigation},
	doi = {10.1172/JCI88950},
	author = {Bruegmann, Tobias and Patrick M {Boyle} and Vogt, Christoph and Karathanos, T and Arevalo, Hermenegild and Fleishmann, Bernd and Natalia A. {Trayanova} and Sasse, Philipp}
}
@article {deng2015accuracy,
	title = {Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI},
	journal = {Frontiers in Physiology},
	volume = {6},
	year = {2015},
	publisher = {Frontiers Media SA},
	author = {Deng, Dongdong and Arevalo, Hermenegild and Pashakhanloo, Farhad and Prakosa, Adityo and Ashikaga, Hiroshi and McVeigh, Elliot and Halperin, Henry and Natalia A. {Trayanova}}
}
@article {ukwatta2015image,
	title = {Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology},
	journal = {Medical Physics},
	volume = {42},
	year = {2015},
	pages = {4579-90},
	publisher = {American Association of Physicists in Medicine},
	author = {Ukwatta, Eranga and Arevalo, Hermenegild and Rajchl, Martin and White, James and Pashakhanloo, Farhad and Prakosa, Adityo and Daniel A {Herzka} and McVeigh, Elliot and Albert C {Lardo} and Natalia A. {Trayanova}}
}
@article {ukwatta2015myocardial,
	title = {Myocardial Infarct Segmentation from Magnetic Resonance Images for Personalized Modeling of Cardiac Electrophysiology},
	journal = {IEEE Transactions on Medical Imaaging},
	year = {2015},
	publisher = {IEEE},
	issn = {0278-0062},
	doi = {10.1109/TMI.2015.2512711},
	author = {Ukwatta, Eranga and Arevalo, Hermenegild and Li, Kristina and Yuan, Jing and Qiu, Wu and Malamas, Peter and Katherine C {Wu} and Natalia A. {Trayanova} and Vadakkumpadan, Fijoy}
}
@article {prakosa2014methodology,
	title = {Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology},
	journal = {Progress in Biophysics and Molecular Biology},
	volume = {115},
	year = {2014},
	pages = {226-34},
	publisher = {Pergamon},
	author = {Prakosa, A and Malamas, Peter and Zhang, S and Pashakhanloo, F and Arevalo, Hermenegild and Herzka, DA and Lardo, A and Halperin, Henry and McVeigh, E and Natalia A. {Trayanova}}
}
@article {boyle2014sodium,
	title = {Sodium current reduction unmasks a structure-dependent substrate for arrhythmogenesis in the normal ventricles},
	journal = {PLOS ONE},
	year = {2014},
	publisher = {Public Library of Science},
	author = {Patrick M {Boyle} and Carolyn J {Park} and Arevalo, Hermenegild and Edward J {Vigmond} and Natalia A. {Trayanova}}
}
@article {ashikaga2013feasibility,
	title = {Feasibility of image-based simulation to estimate ablation target in human ventricular arrhythmia},
	journal = {Heart Rhythm},
	year = {2013},
	publisher = {Elsevier},
	author = {Ashikaga, Hiroshi and Arevalo, Hermenegild and Vadakkumpadan, Fijoy and Jason D {Bayer} and Nazarian, Saman and Zviman, Muz and Tandri, Harikrishna and Ronald D {Berger} and Calkins, Hugh}
}
@article {arevalo2013tachycardia,
	title = {Tachycardia in post-infarction hearts: insights from 3D image-based ventricular models},
	journal = {PLOS ONE},
	year = {2013},
	publisher = {PLOS ONE},
	author = {Arevalo, Hermenegild and Plank, Gernot and Helm, Patrick and Halperin, Henry and Natalia A. {Trayanova}}
}
@article {vadakkumpadan2012image,
	title = {Image-based estimation of ventricular fiber orientations for personalized modeling of cardiac electrophysiology},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {31},
	year = {2012},
	pages = {1051-1060},
	publisher = {IEEE},
	author = {Vadakkumpadan, Fijoy and Arevalo, Hermenegild and Ceritoglu, Can and Miller, Michael and Natalia A. {Trayanova}}
}
@article {rantner2012three,
	title = {Three-dimensional mechanisms of increased vulnerability to electric shocks in myocardial infarction: Altered virtual electrode polarizations and conduction delay in the peri-infarct zone},
	journal = {Journal of Physiology},
	volume = {590},
	year = {2012},
	pages = {4537-51},
	publisher = {Blackwell Publishing Ltd},
	author = {Lukas J {Rantner} and Arevalo, Hermenegild and Jason L {Constantino} and Igor R {Efimov} and Plank, Gernot and Natalia A. {Trayanova}}
}
@article {Simula.simula.540,
	title = {Susceptibility to Reentry in the Infarcted Heart Depends on the Active Fibroblast Density},
	journal = {Biophysical Journal},
	volume = {110},
	number = {6},
	year = {2011},
	month = {September},
	pages = {1307-15},
	author = {McDowell, Kathleen and Arevalo, Hermenegild and Maleckar, Molly and Natalia A. {Trayanova}}
}
@article {Simula.camo.4,
	title = {Fibroblast-Myocote Coupling Induces Alterations in Potassium Currents That Trigger Regional Action Potensial During (APD) Prolongation in Infarcted Myocardium},
	journal = {Heart Rhythm},
	volume = {7},
	number = {5},
	year = {2010},
	pages = {163-164},
	author = {McDowell, Kathleen and Arevalo, Hermenegild and Maleckar, Molly and Natalia A. {Trayanova}}
}
@article {vadakkumpadan2010image,
	title = {Image-based models of cardiac structure in health and disease},
	journal = {Wiley Interdisciplinary Reviews: Systems Biology and Medicine},
	year = {2010},
	publisher = {John Wiley \& Sons, Inc.},
	author = {Vadakkumpadan, Fijoy and Arevalo, Hermenegild and Anton J {Prassl} and Chen, Junjie and Kickinger, Ferdinand and Kohl, Peter and Plank, Gernot and Natalia A. {Trayanova}}
}
@misc {Simula.SC.546,
	title = {Effects of Fibroblast-Myocyte Coupling in the Infarcted Rabbit Heart},
	howpublished = {Heart Rhythm},
	volume = {6},
	year = {2009},
	month = {May/2009},
	pages = {S295-296},
	author = {McDowell, Kathleen and Arevalo, Hermenegild and Maleckar, Molly and RC, Blake and G, Plank and Natalia A. {Trayanova}}
}
@article {vigmond2009towards,
	title = {Towards predictive modelling of the electrophysiology of the heart},
	journal = {Experimental Physiology},
	year = {2009},
	publisher = {Blackwell Publishing Ltd},
	author = {Vigmond, Edward and Vadakkumpadan, Fijoy and Arevalo, Hermenegild and Deo, Makarand and Plank, Gernot and Natalia A. {Trayanova}}
}
