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WO2000046689A1 - Systeme et procede computationnels pour la modelisation du coeur - Google Patents

Systeme et procede computationnels pour la modelisation du coeur Download PDF

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Publication number
WO2000046689A1
WO2000046689A1 PCT/US1999/002755 US9902755W WO0046689A1 WO 2000046689 A1 WO2000046689 A1 WO 2000046689A1 US 9902755 W US9902755 W US 9902755W WO 0046689 A1 WO0046689 A1 WO 0046689A1
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WIPO (PCT)
Prior art keywords
node
nodes
lattice
equations
state
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Ceased
Application number
PCT/US1999/002755
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English (en)
Inventor
Raimond Winslow
Donna Rounds
David Scollan
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PHYSIOME SCIENCES Inc
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PHYSIOME SCIENCES Inc
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Priority to AU26652/99A priority Critical patent/AU2665299A/en
Priority to JP2000597702A priority patent/JP2002537008A/ja
Priority to DE19983999T priority patent/DE19983999T1/de
Priority to CA002361435A priority patent/CA2361435A1/fr
Priority to PCT/US1999/002755 priority patent/WO2000046689A1/fr
Priority to IL14464999A priority patent/IL144649A0/xx
Priority to EP99906830A priority patent/EP1149347A1/fr
Publication of WO2000046689A1 publication Critical patent/WO2000046689A1/fr
Priority to NO20013752A priority patent/NO20013752D0/no
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Definitions

  • the present invention constitutes a computing system and software model of a physical organ, and more particularly to processes and procedures for generating a biophysically detailed, predictive model of the mammalian heart which accepts anatomic and biophysical data and generates a representation of the electrophysiologic state of the heart.
  • Physiologic organs consist of various types of cells organized into tissues. These tissues form an organ, which in turn interacts with the whole body.
  • the ability to model organ function with a high level of biophysical, biochemical, and structural detail is of enormous value to biology and medicine, because such models provide deep insight into the cause of disease.
  • the heart for example includes a sino-atrial node and atrio- ventricular node, as well as the bundle of His and the Purkinje fiber system.
  • These structures have a profound impact on the electrical activation sequence of the heart muscle fibers within the atria and ventricles, and thus have an enormous impact on the heart's mechanical function. It is well known that organic and anatomic defects in these structures can result in life-threatening cardiac arrhythmias.
  • a model that allows a user to interact with an accurate and predictive model of the heart's cells and tissues would be of great value. This objective has spurred the development of computational models of cardiac cells and tissues.
  • These computational models have sought to integrate experimental observations and theoretical knowledge into a formal model expressed in mathematical terms.
  • Various algorithms, processes and procedures are used to describe the behavior of the cells and tissues that comprise the organ system.
  • a useful computer-implemented model should effectively emulate interesting behaviors.
  • the "OXSOFT" models require the solution of thirty or more simultaneous non-linear differential equations. Even on the fastest personal computers it can take several minutes to compute only a few seconds of activity. Nonetheless zero-dimensional models have proven to be successful not only in reproducing normal single cell cardiac electrical activity, but also in reconstructing some of the cellular mechanisms of arrhythmia, including ectopic beating and the effects of therapeutic drug administration (e.g., cardiac glycosides). These models can exhibit the action potential shortening during ATP depletion, and the early after-depolarizations characteristic of potassium blocking compounds and calcium agonists observed in actual hearts. Initial success has prompted researchers to try to extend the dimensionally of these models but just how best to do this has remained elusive.
  • the present model renders detailed three-dimensional information about the heart based on cell function.
  • the present invention is a composite of procedures that, through interaction, permits three dimensional electrophysiological simulation of the heart, commonly referred to as the "3-D heart" model.
  • the model computationally represents cardiac anatomy and a system of mathematical equations describing the spatio-temporal behavior of biophysical quantities such as cardiac voltages at various locations.
  • the computer processes combines these two parts of the model in a simulation presenting the temporal evolution of the state defining quantities in the anatomical model.
  • the preferred finite difference expression of the computerized anatomical model consists of a set of N nodes that are arrayed in a three- dimensional network. Each node corresponds to a region of tissue within the heart. And each node has at least five neighbors. This region of tissue may be defined as follows: a) a segment of an individual cardiac cell; b) an entire cardiac cell; c) a small region of cardiac tissue consisting of more than one cell.
  • each node communicates with its neighbors. This communication reflects electrical coupling between adjacent parts of cells, cells, or groups of cells within the heart. In an actual heart the coupling strength depends on local anatomy. Thus the network level description the coupling strength between nodes allows one to encode or model the heart's anisotropic anatomic detail. Thus the anatomical and biophysical portions of the model are constructed and then they are passed to a solution procedure, along with a file that specifies the initial state of the heart.
  • the model's organization and structure facilitates computation on multiprocessor computers because the nodes engage only in near-neighbor interactions. Therefore the updating process for a large number of locally coupled nodes can be easily segmented and assigned to a single processor. This specific approach enables reasonable processing times for large numbers of nodes, and so resolves the fundamental problem of prior art large-scale biophysically detailed models which are computationally intractable.
  • FIG. 1 is representation of the model depicting the organization of the software processes
  • FIG. 2 is a representation of the ventricles of the heart represented in the form of a finite difference network
  • FIG. 3 is a representation of the model depicting the organization of nodes and coupling relationships
  • FIG. 4 is a representation of the action potentials which result from the set of equations associated with each node
  • FIG. 5 is a representation of the model depicting the relationship between the organization of an illustrative computer system with the model
  • FIG. 6a is a table setting forth preferred set of node equations
  • FIG. 6b is a table setting forth preferred set of node equations
  • FIG. 6c is a table setting forth preferred set of node equations
  • FIG. 7 is a representation of a computed action potential along with corresponding membrane currents.
  • the composite model includes both "nodes" and a complimentary "network".
  • the network reflects and represents the anatomical structure of the heart; the nodes reflect the spatio-temporal evolution of the nodes' biophysical quantities. Therefore at each node of the model, various biophysical quantities and their related equations are defined.
  • This biophysical model along with the anatomical network is used by the solution program to compute the evolution of the biophysical quantities defined at the nodes. In this manner the electrophysiology of the whole heart can be modeled.
  • FIG. 1 represents the composite model and illustrates procedures for modeling the heart.
  • Geometry generator 12 creates a computerized anatomical model of the heart represented by object 14. The exact form that anatomic model object 14 assumes depends upon the type of generation process.
  • the finite difference modeling technique is shown in the specification and drawings but finite element and multigrid models are contemplated within the scope of this disclosure.
  • process 12 is used to construct a finite difference anatomic representation of the heart which is best illustrated in FIG. 2, which shows a finite difference heart model 40 comprised of a lattice 41 of nodes typified by node 46.
  • the node locations and interconnections are specified by object 16.
  • the conductivity between nodes is modeled by object 18.
  • These conductivity relationships between cells of the mammalian heart are specified by coupling relationships between the nodes of the model.
  • nodes representing specific tissues types and how they are coupled to the rest of the heart are specified.
  • a set of nodes and their coupling relationships could be modified to represent Purkinje fiber cells, thus representing the physical extent and direction of Purkinje fibers within the myocardium.
  • Object 20 is used in this fashion to capture tissues like these in the model.
  • Object 14 corresponds to the finite difference heart model 40 shown in FIG. 2. This portion of the model is combined with the biophysical model in the simulation program 28.
  • FIG. 2 depicts a finite difference network of the ventricles of a heart 40 with a portion 42 of the heart cleaved away to show the endocardial surfaces and the chamber geometry. It should be recognized that the set of nodes forms a lattice 41 . The figure also shows that the nodes typified by node 44 lie on or in the myocardium and that no nodes are present in the chamber proper.
  • This figure illustrates a direct relationship between each node and its corresponding spatial location in the heart depicted in the completed lattice 41.
  • the nodes form a cubic lattice structure with each node having at least five (and more commonly six) near neighbors.
  • Fig. 3 shows a subject node 46 extracted from the finite difference model of the myocardium and presented with its near neighbors.
  • the lattice 41 reveals three global orthogonal axes.
  • the X-axis 48 and Y-axis 50 and Z-axis 52 pass through an origin which is occupied by the subject reference node 46.
  • the coupling relationship between node 46 and its companions is shown as a block typified by block 60 connecting node 46 with node 58.
  • the anisotropy of the heart as taken from the anatomic data set 10 is used to defined each of the many relationships illustrated by coupling relationship depicted by block 60.
  • the tensor of the anatomic data will be resolved into the three orthogonal axes shown on the figure.
  • This figure is intended to show the computational coupling relationship between nodes such as node 58 and node 46.
  • the coupling relationship between nodes depends on both cell type and cell orientation.
  • I G * (VA - VB).
  • G may be a constant linear conductance, or it could be given by a biophysically accurate mode of properties of cardiac gap junction channels.
  • the transmembrane voltages are discussed in the following section.
  • Fig. 4 is similar to Fig. 3 in that it shows a representative node 46 of the heart 40.
  • the node 46 is associated with chart 68 which depicts a computed action potential at the node.
  • the action potential is the biologic term used to denominate the time course or evolution of cellular voltage. All cells have a potential difference between the interior of the cell and the exterior of the cell. This so called transmembrane potential results from the accumulation of negatively charged ions within the cell.
  • ion channels in the membrane open and close sequentially which allows the ions to migrate across the membrane results in the depolarization portion of the wave form shown as intrinsic deflection 70.
  • the rapid sodium (Na) channels open. This sharp change in voltage is communicated to the near neighbor cells, triggering a depolarization in the adjacent cells. Metabolic processes then commence to repolarize the cell. The return to the resting potential is shown in the action potential table 68 by curve 72.
  • the biophysical model 22 must be specified. First, a choice is made of what biophysical quantities are of interest. Next, a mathematical model that describes their spatio-temporal behavior is developed in process 26. Usually these processes can be described in the form of partial differential equations. These equations describe the cellular and subcelluar processes that determine the values of the biophysical quantities from one moment in time to the next. Then this mathematical model must be translated into a computational form by process 26 so that the solution program of process 28 can use the process 26 as a subroutine.
  • the structure of this simulation program 28 will depend upon what type of anatomical mode was constructed — i.e., a finite difference anatomical model demands a finite difference representation of the biophysical model.
  • the biophysical model 22 (Fig. 1 ) includes in a set of N x Neq coupled (through voltage) nonlinear ordinary differential equations (ODEs), with coupling as defined by the anatomical model. Given initial values 34 for the state variables defined by each of these equations (referred to as an initial condition), and given boundary conditions on electrical current flow at the bounding surfaces of the model, these ODEs may be evolved in time to predict electrical activity within the heart. The ability to relate this predicted electrical activity to cellular electrophysiology is the single most useful characteristic of this model.
  • Representative equations for defining the state of the node are set forth in tables in Fig. 6a; Fig. 6b and in Fig. 6c. They include: voltage dependent transmembrane currents for (Na), (K), and Ca) ionic species, transmembrane ion pump currents for (Na), (K), and (Ca) ionic species; total transmembrane flux of (Na), (K), and (Ca) ionic species; total transmembrane ion flux in cellular organelles; each node having a total transmembrane flux due to lipid bilayer membrane capacitance.
  • the partial differential equations reduce to a set of ordinary differential equations defined at each node. These equations define the biophysical processes giving rise to the unique properties of cardiac tissue.
  • this system includes: a) equations defining properties of nonlinear, voltage- gated transmembrane currents; b) equations describing properties of ion pumps and exchangers in the cell membrane; c) equations describing the buffering, uptake, storage, transfer, and release of calcium ions by intracellular organelles; and d) equations describing time-varying changes of intracellular ion concentration.
  • Fig. 6a sets forth exemplary equations while the tables of Fig.
  • Fig. 6b and Fig. 6c identify a grouping of biophysical processes that are suitable for use in the preferred model. These tables may be related to the action potential of Fig. 7.
  • the corresponding ionic current flows are associated with the time course of Fig. 7.
  • the simulation program 28 interactively computes the action potential defining equations from the biophysical model 22 for all the nodes and additionally computes the contribution that near neighbors have on the voltage at the nodes. This process is represented in the figure by integration process 30.
  • the initial conditions are presented to the simulation process 28 by the object 34 which is typically a data file.
  • the run parameters are presented by a data file shown as object 32.
  • the output 36 of the simulation program 28 can be expressed in any one of a number of ways.
  • One very useful output format is a 3D animation of the time course of voltage over several heartbeats. Normally the animation is created by a graphics terminal dedicated to animating large data files. Single node results are also available and may be presented in the form of computed action potentials.
  • Fig. 5 among other things illustrates a parallel processor computer system 86.
  • the operating system software can select a set of nodes 81 and have the state equations run on a single process 90.
  • a separate set of nodes 82 can be concurrently computed on processor 92.
  • the state defining equations at the nodes can be calculated simultaneously.
  • the fact that the state of each node is essentially local renders this approach practical.
  • the action potentials of all nodes is completed the appropriate state data can be accumulated in the shared memory 94. With this data available the individual processors can next compute the coupling relationships giving rise to the output 36 (Fig. 1 ).
  • Various graphical display techniques can be used to present this data to the user including 3D animation 38, single node action potential results 39 or simulated surface presentations 37.
  • Figure 7 shows a computed-action potential for a single node.
  • the action potential is broken into several phases: the resting potential corresponds to Phase 4; the rapid depolarization of the cell across the membrane is represented by Phase 0; Phases 1 and 2 correspond to a depolarization plateau; and Phase 3 corresponds to the return of the node to the quiescent state.

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  • Medical Informatics (AREA)
  • Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Biomedical Technology (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
  • Complex Calculations (AREA)

Abstract

L'invention concerne un modèle computationnel pour la simulation et la prédiction des dynamiques électriques et chimiques du coeur. Ledit modèle consiste en une représentation informatisée de l'anatomie du coeur et en un système d'équations mathématiques qui décrivent le comportement spatio-temporel des variables biophysiques, telles que la tension en différents endroits du coeur. Le processus informatique peut présenter l'évolution temporelle des variables biophysiques dans le modèle anatomique informatisé.
PCT/US1999/002755 1999-02-03 1999-02-03 Systeme et procede computationnels pour la modelisation du coeur Ceased WO2000046689A1 (fr)

Priority Applications (8)

Application Number Priority Date Filing Date Title
AU26652/99A AU2665299A (en) 1999-02-03 1999-02-03 Computational system and method for modeling the heart
JP2000597702A JP2002537008A (ja) 1999-02-03 1999-02-03 心臓をコンピュータによってモデリングする装置および方法
DE19983999T DE19983999T1 (de) 1999-02-03 1999-02-03 Berechnungssystem- und -Verfahren zur Herz-Modellbildung
CA002361435A CA2361435A1 (fr) 1999-02-03 1999-02-03 Systeme et procede computationnels pour la modelisation du coeur
PCT/US1999/002755 WO2000046689A1 (fr) 1999-02-03 1999-02-03 Systeme et procede computationnels pour la modelisation du coeur
IL14464999A IL144649A0 (en) 1999-02-03 1999-02-03 Computational system and method for modeling the heart
EP99906830A EP1149347A1 (fr) 1999-02-03 1999-02-03 Systeme et procede computationnels pour la modelisation du coeur
NO20013752A NO20013752D0 (no) 1999-02-03 2001-07-31 Beregningssystem og fremgangsmåte for å modellere hjertet

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JP (1) JP2002537008A (fr)
AU (1) AU2665299A (fr)
CA (1) CA2361435A1 (fr)
DE (1) DE19983999T1 (fr)
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WO (1) WO2000046689A1 (fr)

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US7165017B2 (en) 1999-04-16 2007-01-16 Entelos, Inc. Method and apparatus for conducting linked simulation operations utilizing a computer-based system model
WO2008047766A1 (fr) * 2006-10-17 2008-04-24 Shoji Yamamoto Système, procédé, programme de simulation des performances cardiaques et feuille de matériau composite
US8095321B2 (en) 2005-01-26 2012-01-10 Japan Science And Technology Agency Modeling device, program, computer-readable recording medium, and method of establishing correspondence
US10055518B2 (en) 2013-12-05 2018-08-21 Fujitsu Limited Apparatus and method for generating shape data
WO2019212622A1 (fr) * 2018-05-04 2019-11-07 Cardioinsight Technologies, Inc. Détection de régions présentant une activité électrophysiologique irrégulière
CN111768865A (zh) * 2019-07-11 2020-10-13 美国思力科技公司 用于模拟椎间盘的病理生理学的方法和系统

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US7844431B2 (en) 2004-02-20 2010-11-30 The Mathworks, Inc. Method and apparatus for integrated modeling, simulation and analysis of chemical and biochemical reactions
EP1783490A4 (fr) * 2004-08-26 2012-01-04 Univ Kyoto Dispositif et programme de sortie de bioparametre
JP2007222313A (ja) * 2006-02-22 2007-09-06 Kyoto Univ 生体パラメータ決定装置、およびプログラム
KR101741580B1 (ko) * 2015-02-25 2017-05-30 금오공과대학교 산학협력단 유전자 돌연변이에 기인한 심장 시뮬레이션 방법 및 장치

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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7165017B2 (en) 1999-04-16 2007-01-16 Entelos, Inc. Method and apparatus for conducting linked simulation operations utilizing a computer-based system model
US8095321B2 (en) 2005-01-26 2012-01-10 Japan Science And Technology Agency Modeling device, program, computer-readable recording medium, and method of establishing correspondence
US8554491B2 (en) 2005-01-26 2013-10-08 Japan Science And Technology Agency Modeling device, program, computer-readable recording medium, and method of establishing correspondence
WO2008047766A1 (fr) * 2006-10-17 2008-04-24 Shoji Yamamoto Système, procédé, programme de simulation des performances cardiaques et feuille de matériau composite
US8412503B2 (en) 2006-10-17 2013-04-02 Shoji Yamamoto Simulation system of cardiac function, simulation method of cardiac function, simulation program of cardiac function, and composite material sheet
US10055518B2 (en) 2013-12-05 2018-08-21 Fujitsu Limited Apparatus and method for generating shape data
WO2019212622A1 (fr) * 2018-05-04 2019-11-07 Cardioinsight Technologies, Inc. Détection de régions présentant une activité électrophysiologique irrégulière
US10729345B2 (en) 2018-05-04 2020-08-04 Cardioinsight Technologies, Inc. Detection of regions exhibiting irregular electrophysiological activity
CN111768865A (zh) * 2019-07-11 2020-10-13 美国思力科技公司 用于模拟椎间盘的病理生理学的方法和系统

Also Published As

Publication number Publication date
IL144649A0 (en) 2002-05-23
EP1149347A1 (fr) 2001-10-31
AU2665299A (en) 2000-08-25
DE19983999T1 (de) 2003-06-18
CA2361435A1 (fr) 2000-08-10
JP2002537008A (ja) 2002-11-05
NO20013752D0 (no) 2001-07-31

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