Personalized noninvasive imaging of volumetric cardiac electrophysiology

Three-dimensionally distributed electrical functioning is the trigger of mechanical contraction of the heart. Disturbance of this electrical flow is known to predispose to mechanical catastrophe but, due to its amenability to certain intervention techniques, a detailed understanding of subject-specific cardiac electrophysiological conditions is of great medical interest. In current clinical practice, body surface potential recording is the standard tool for diagnosing cardiac electrical dysfunctions. However, successful treatments normally require invasive catheter mapping for a more detailed observation of these dysfunctions. In this dissertation, we take a system approach to pursue personalized noninvasive imaging of volumetric cardiac electrophysiology. Under the guidance of existing scientific knowledge of the cardiac electrophysiological system, we extract the subject specific cardiac electrical information from noninvasive body surface potential mapping and tomographic imaging data of individual subjects. In this way, a priori knowledge of system physiology leads the physiologically meaningful interpretation of personal data; at the same time, subject-specific information contained in the data identifies parameters in individual systems that differ from prior knowledge. Based on this perspective, we develop a physiological model-constrained statistical framework for the quantitative reconstruction of the electrical dynamics and inherent electrophysiological property of each individual cardiac system. To accomplish this, we first develop a coupled meshfree-BE (boundary element) modeling approach to represent existing physiological knowledge of the cardiac electrophysiological system on personalized heart-torso structures. Through a state space system approach and sequential data assimilation techniques, we then develop statistical model-data coupling algorithms for quantitative reconstruction of volumetric transmembrane potential dynamics and tissue property of 3D myocardium from body surface potential recoding of individual subjects. We also introduce a data integration component to build personalized cardiac electrophysiology by fusing tomographic image and BSP sequence of the same subject. In addition, we develop a computational reduction strategy that improves the efficiency and stability of the framework. Phantom experiments and real-data human studies are performed for validating each of the framework’s major components. These experiments demonstrate the potential of our framework in providing quantitative understanding of volumetric cardiac electrophysiology for individual subjects and in identifying latent threats in individual’s heart. This may aid in personalized diagnose, treatment planning, and fundamentally, prevention of fatal cardiac arrhythmia

Guest Editorial Special Issue on Medical Imaging and Image Computing in Computational Physiology

International audienceThe January 2013 Special Issue of IEEE transactions on medical imaging discusses papers on medical imaging and image computing in computational physiology. Aslanid and co-researchers present an experimental technique based on stained micro computed tomography (CT) images to construct very detailed atrial models of the canine heart. The paper by Sebastian proposes a model of the cardiac conduction system (CCS) based on structural information derived from stained calf tissue. Ho, Mithraratne and Hunter present a numerical simulation of detailed cerebral venous flow. The third category of papers deals with computational methods for simulating medical imagery and incorporate knowledge of imaging physics and physiology/biophysics. The work by Morales showed how the combination of device modeling and virtual deployment, in addition to patient-specific image-based anatomical modeling, can help to carry out patient-specific treatment plans and assess alternative therapeutic strategies

Deep learning-based reduced order models in cardiac electrophysiology

Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies on the formulation and numerical approximation of coupled nonlinear dynamical systems. These systems describe the cardiac action potential, that is the polarization/depolarization cycle occurring at every heart beat that models the time evolution of the electrical potential across the cell membrane, as well as a set of ionic variables. Multiple solutions of these systems, corresponding to different model inputs, are required to evaluate outputs of clinical interest, such as activation maps and action potential duration. More importantly, these models feature coherent structures that propagate over time, such as wavefronts. These systems can hardly be reduced to lower dimensional problems by conventional reduced order models (ROMs) such as, e.g., the reduced basis (RB) method. This is primarily due to the low regularity of the solution manifold (with respect to the problem parameters) as well as to the nonlinear nature of the input-output maps that we intend to reconstruct numerically. To overcome this difficulty, in this paper we propose a new, nonlinear approach which exploits deep learning (DL) algorithms to obtain accurate and efficient ROMs, whose dimensionality matches the number of system parameters. Our DL approach combines deep feedforward neural networks (NNs) and convolutional autoencoders (AEs). We show that the proposed DL-ROM framework can efficiently provide solutions to parametrized electrophysiology problems, thus enabling multi-scenario analysis in pathological cases. We investigate three challenging test cases in cardiac electrophysiology and prove that DL-ROM outperforms classical projection-based ROMs.Comment: 28 page

PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps

Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art cardiac models is however a challenging task. In this work, we introduce a novel inverse problem for inferring the anisotropic structure of the conductivity tensor, that is fiber orientation and conduction velocity along and across fibers, of an eikonal model for cardiac activation. The proposed method, named PIEMAP, performed robustly with synthetic data and showed promising results with clinical data. These results suggest that PIEMAP could be a useful supplement in future clinical workflows of personalized therapies.Comment: 12 pages, 4 figures, 1 tabl

Electromechanical large scale computational models of the ventricular myocardium

Els models computacionals del cor són una eina important que pot donar als investigadors biomèdics una font addicional d’informació per entendre el funcionament del miocardi. Els models numèrics poden ajudar a interpretar dades experimentals i proporcionar informació complementària sobre mecanismes cardíacs que no poden ser determinats amb precisió mitjançant dispositius clínics clàssics. En aquesta tesi, s’apliquen tècniques de computació a gran escala per construir una eina computacional capaç d’executar-se en paral•lel en milers de processadors, permetent simulacions d’alta fidelitat en malles fines. Per simular el bombeig del cor, s’utilitza un esquema d’acoblament explícit entre les equacions electrofisiològiques en tres dimensions i la formulació en mecànica de sòlids. Per trobar la solució numèrica, s’utilitza el mètode d’elements finits. A més, s’implementen tècniques en assimilació de dades per a l’estimació efectiva dels paràmetres electrofisiològics i mecànics rellevants que apareixen a les equacions, la qual cosa ´es un pas crucial cap a un model cardíac sensible a cada pacient. El codi computacional s’aplica per simular problemes físics reals. S’estudia la propagació electromecànica en una geometria de conill, on es prova la sensibilitat del model a les variacions d’entrada. En particular, l’eina de càlcul s’utilitza per avaluar la influència del camp de fibres cardíaques en la contracció del teixit. Per desenvolupar una simulació cardíaca útil per a fins clínics, el model requereix la integració i combinació de la mecànica computacional i les tècniques de processament d’imatge més recents. El model resultant pot ser la base d’estudis teòrics sobre mecanismes de patologies, oferint als investigadors i cardiòlegs pistes addicionals per comprendre el funcionament del cor. Pot ajudar a la planificació de cirurgia i modelització, com és la predicció dels efectes de compostos farmacològics en el ritme cardíac o l’estudi de l’efecte de medicaments. Aquest projecte només és possible en un equip multidisciplinar, on grups especialitzats uneixen les seves forces en les respectives disciplines: cardiòlegs, investigadors imatge, bioenginyers i científics de la computació. El present model computacional del cor és un pas més cap a la creació d’un laboratori cardíac virtual.A cardiac computational model is a relevant tool that can give biomedical researchers an additional source of information to understand how the heart works. Numerical models can help to interpret experimental data and provide information about cardiac mechanisms that can not be determined accurately by classical clinical devices. In this thesis, High Performance Computing (HPC) techniques are used to build a cardiac computational tool, which is capable of running in parallel in thousands of processors, bioengineers and computational scientists. The present cardiac computational model is one further step towards the creation of a virtual lab, allowing high fidelity simulations on fine meshes. To simulate the pumping heart, an explicit coupling scheme between the three-dimensional electrophysiological equations and the solid mechanics formulation is used, solving the governing equations with finite element methods. Also, data assimilation techniques are implemented for the effective estimation of some relevant electrophysiological parameters, which is a crucial step towards the patient-sensitive cardiac model. The data assimilation techniques are assessed on synthetic data generated by the model. Finally, the computational code is applied to simulate real physical problems. The electromechanical propagation in a rabbit geometry is studied to test the sensitivity of the framework to input variations. Particularly, the computational tool is used to evaluate the influence of the fiber field in the contraction of the tissue. To develop a cardiac simulation useful for clinical purposes, the integrative model requires combining computational mechanics and image processing techniques via data assimilation methods. Coupled with the most advanced image processing analysis, the framework can be the base of theoretical studies into the mechanisms of cardiac pathologies. It can help surgery planning and cardiac modeling, such as the prediction of the impact of pharmacological compounds on the heart’s rhythm or to improve the knowledge of drug study, giving medical researchers additional hints to understand the heart. This realization is only possible in a multidisciplinary team, where specialized groups join forces in their respective disciplines: cardiologists, image researchers, bioengineers and computational scientists. The present cardiac computational model is one further step towards the creation of a virtual la

Patient-specific computational modeling of Cortical Spreading Depression via Diffusion Tensor Imaging

Cortical Spreading Depression (CSD), a depolarization wave originat- ing in the visual cortex and traveling towards the frontal lobe, is com- monly accepted as a correlate of migraine visual aura. As of today, little is known about the mechanisms that can trigger or stop such phenomenon. However, the complex and highly individual characteristics of the brain cortex suggest that the geometry might have a significant impact in sup- porting or contrasting the propagation of CSD. Accurate patient-specific computational models are fundamental to cope with the high variability in cortical geometries among individuals, but also with the conduction anisotropy induced in a given cortex by the complex neuronal organisa- tion in the grey matter. In this paper we integrate a distributed model for extracellular potassium concentration with patient-specific diffusivity tensors derived locally from Diffusion Tensor Imaging data.This work was supported by the Bizkaia Talent and European Commission through COFUND under the grant BRAhMS - Brain Aura Mathematical Sim- ulation (AYD-000-285), by the Basque Government through the BERC 2014- 2017 program, and by the Spanish Ministry of Economics and Competitiveness MINECO through the BCAM Severo Ochoa excellence accreditation SEV-2013- 0323 and the Spanish "Plan Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad" under Grant BELEMET - Brain ELEctro- METabolic modeling and numerical approximation (MTM2015-69992-R). JMC acknowledges financial support from Ikerbasque: The Basque Foundation for Science and Euskampus at UPV/EHU

Three-dimensional cardiac computational modelling: methods, features and applications

[EN] The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.This work was partially supported by the "VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica" from the Ministerio de Economia y Competitividad of Spain (TIN2012-37546-C03-01 and TIN2011-28067) and the European Commission (European Regional Development Funds - ERDF - FEDER) and by "eTorso project" (GVA/2013-001404) from the Generalitat Valenciana (Spain). ALP is financially supported by the program "Ayudas para contratos predoctorales para la formacion de doctores" from the Ministerio de Economia y Competitividad of Spain (BES-2013-064089).López Pérez, AD.; Sebastián Aguilar, R.; Ferrero De Loma-Osorio, JM. (2015). 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