Inter-model consistency and complementarity: Learning from ex-vivo imaging and electrophysiological data towards an integrated understanding of cardiac physiology

Cámara, Óscar; Sermesant, Maxime; Lamata, Pablo; Wang, L.; Pop, Mihaela; Relan, Jatin; Craene, Mathieu De; Delingette, Hervé; Liu, Huijun; Niederer, Steven A.; Pashaei, Ali; Plank, Gernot; Romero, Daniel; Sebastián, Ramón Alonso; Wong, Ken C. L.; Zhang, H.; Ayache, Nicholas; Frangi, Alejandro F.; Shi, Peng; Smith, Nicolas P.; Wright, Graham A.

doi:10.1016/j.pbiomolbio.2011.07.007

Cited by 37 publications

(29 citation statements)

References 44 publications

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“…We evaluated LBM-EP performance in a dataset distributed during CESC'10 MICCAI Grand Challenge with respect to FEM-EP and to a recently published benchmark [1]. Our purpose being evaluation and not personalization, we did not adjust the parameters locally.…”

Section: Comparison With Published Results On Cesc'10 Datamentioning

confidence: 99%

“…Our purpose being evaluation and not personalization, we did not adjust the parameters locally. We thus compared our results with the generic benchmark of the ionic ten Tusscher-Panfilov model only [1]. CESC'10 dataset consisted in an explanted porcine heart, and comprised optical fluorescence images of transmembrane potential and high-resolution diffusion-weighted (DW) MRI images ( [7]).…”

Section: Comparison With Published Results On Cesc'10 Datamentioning

confidence: 99%

“…Phenomenological models provide a good compromise between model details, being able to capture most of the pathological conditions, and complexity. Recent works demonstrated that those models can be personalised from clinical data [1]. However, because they solve stiff partial differential equations (PDE), they are still too computationally demanding for day-to-day clinical setups and intervention guidance.…”

Section: Introductionmentioning

confidence: 99%

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LBM-EP: Lattice-Boltzmann Method for Fast Cardiac Electrophysiology Simulation from 3D Images

Rapaka

Mansi

Georgescu

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Current treatments of heart rhythm troubles require careful planning and guidance for optimal outcomes. Computational models of cardiac electrophysiology are being proposed for therapy planning but current approaches are either too simplified or too computationally intensive for patient-specific simulations in clinical practice. This paper presents a novel approach, LBM-EP, to solve any type of mono-domain cardiac electrophysiology models at near real-time that is especially tailored for patient-specific simulations. The domain is discretized on a Cartesian grid with a level-set representation of patient's heart geometry, previously estimated from images automatically. The cell model is calculated node-wise, while the transmembrane potential is diffused using Lattice-Boltzmann method within the domain defined by the levelset. Experiments on synthetic cases, on a data set from CESC'10 and on one patient with myocardium scar showed that LBM-EP provides results comparable to an FEM implementation, while being 10 − 45 times faster. Fast, accurate, scalable and requiring no specific meshing, LBM-EP paves the way to efficient and detailed models of cardiac electrophysiology for therapy planning.

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Section: Comparison With Published Results On Cesc'10 Datamentioning

confidence: 99%

Section: Comparison With Published Results On Cesc'10 Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LBM-EP: Lattice-Boltzmann Method for Fast Cardiac Electrophysiology Simulation from 3D Images

Rapaka

Mansi

Georgescu

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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“…Please see also related communications in this issue by Aguado-Sierra et al (2011) andCamara et al (2011).…”

Section: Editor's Notementioning

confidence: 99%

Efficient probabilistic model personalization integrating uncertainty on data and parameters: Application to Eikonal-Diffusion models in cardiac electrophysiology

Konukoğlu

Relan

Çilingir

et al. 2011

Progress in Biophysics and Molecular Biology

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Biophysical models are increasingly used for medical applications at the organ scale. However, model predictions are rarely associated with a confidence measure although there are important sources of uncertainty in computational physiology methods. For instance, the sparsity and noise of the clinical data used to adjust the model parameters (personalization), and the difficulty in modeling accurately soft tissue physiology. The recent theoretical progresses in stochastic models make their use computationally tractable, but there is still a challenge in estimating patient-specific parameters with such models. In this work we propose an efficient Bayesian inference method for model personalization using polynomial chaos and compressed sensing. This method makes Bayesian inference feasible in real 3D modeling problems. We demonstrate our method on cardiac electrophysiology. We first present validation results on synthetic data, then we apply the proposed method to clinical data. We demonstrate how this can help in quantifying the impact of the data characteristics on the personalization (and thus prediction) results. Described method can be beneficial for the clinical use of personalized models as it explicitly takes into account the uncertainties on the data and the model parameters while still enabling simulations that can be used to optimize treatment. Such uncertainty handling can be pivotal for the proper use of modeling as a clinical tool, because there is a crucial requirement to know the confidence one can have in personalized models.

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“…Nevertheless, clinical measurements from humans are often incomplete and sparse, then not quite appropriate for exhaustive validation and verification of the electromechanical solvers. More controlled gold-standard electrophysiological data, derived either synthetically [4] or with experimental models [5], has been used in some simulation benchmarks to verify, customize, validate and integrate different cardiac electrophysiological solvers. A challenge organized in STACOM'11 aiming at validating myocardial tracking and deformation algorithms applied to image sequences [6], but, to our knowledge, there has not been yet a challenge for assessing simulated deformation fields provided by electromechanical models of the heart.…”

Section: Introductionmentioning

confidence: 99%

Fully-Coupled Electromechanical Simulations of the LV Dog Anatomy Using HPC: Model Testing and Verification

Aguado-Sierra

Santiago

Rivero

et al. 2015

Lecture Notes in Computer Science

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Abstract. Verification of electro-mechanic models of the heart require a good amount of reliable, high resolution, thorough in-vivo measurements. The detail of the mathematical models used to create simulations of the heart beat vary greatly. Generally, the objective of the simulation determines the modeling approach. However, it is important to exactly quantify the amount of error between the various approaches that can be used to simulate a heart beat by comparing them to ground truth data. The more detailed the model is, the more computing power it requires, we therefore employ a high-performance computing solver throughout this study. We aim to compare models to data measured experimentally to identify the effect of using a mathematical model of fibre orientation versus the measured fibre orientations using DT-MRI. We also use simultaneous endocardial stimuli vs an instantaneous myocardial stimulation to trigger the mechanic contraction. Our results show that synchronisation of the electrical and mechanical events in the heart beat are necessary to create a physiological timing of hemodynamic events. Synchronous activation of all of the myocardium provides an unrealistic timing of hemodynamic events in the cardiac cycle. Results also show the need of establishing a protocol to quantify the zero-pressure configuration of the left ventricular geometry to initiate the simulation protocol; however, the predicted zero-pressure configuration of the same geometry was different, depending on the origin of the fibre field employed.

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Inter-model consistency and complementarity: Learning from ex-vivo imaging and electrophysiological data towards an integrated understanding of cardiac physiology

Cited by 37 publications

References 44 publications

LBM-EP: Lattice-Boltzmann Method for Fast Cardiac Electrophysiology Simulation from 3D Images

LBM-EP: Lattice-Boltzmann Method for Fast Cardiac Electrophysiology Simulation from 3D Images

Efficient probabilistic model personalization integrating uncertainty on data and parameters: Application to Eikonal-Diffusion models in cardiac electrophysiology

Fully-Coupled Electromechanical Simulations of the LV Dog Anatomy Using HPC: Model Testing and Verification

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